Artificial Immune Systems (AIS) are intelligent algorithms derived on the principles inspired by human immune system. In this research work, electroencephalography (EEG) signals for four distinct motor movement of human limbs are detected and classified using Negative Selection Classification Algorithm (NSCA). For this study, a widely studied open source EEG signal database (BCI IV - Graz dataset 2a, comprising 9 subjects) has been used. Mel Frequency Cepstral Coefficients (MFCCs) are extracted as selected feature from recorded EEG signals. Dimensionality reduction of (...) data is carried out by applying two hidden layered stacked auto-encoder. Genetic Algorithm (GA) optimized detectors (Artificial Lymphocytes) are trained using Negative Selection Algorithm (NSA) for detection and classification of four motor movements. The trained detectors consist of four sets of detectors, each set is trained for detection and classification of one of the four movements from the other three movements. The optimized radius of detector is small enough not to mis-detect the sample. Euclidean distance of each detector with every training dataset sample is taken and compared with optimized radius of detector as a non-self detector. Our proposed approach achieved a mean classification accuracy of 86.39% for limb movements over 9 subjects with a maximum individual subject classification accuracy of 97.5 % for subject number eight. (shrink)

The value of resting electroencephalogram (EEG) in revealing neural constitutes of consciousness (NCC) was examined. We quantified the dynamic repertoire, duration and oscillatory type of EEG microstates in eyes-closed rest in relation to the degree of expression of clinical self-consciousness. For NCC a model was suggested that contrasted normal, severely disturbed state of consciousness and state without consciousness. Patients with disorders of consciousness were used. Results suggested that the repertoire, duration and oscillatory type of EEG microstates in resting condition (...) quantitatively related to the level of consciousness expression in brain-damaged patients and healthy-conscious subjects. Specifically, results demonstrated that (a) decreased number of EEG microstate types was associated with altered states of consciousness, (b) unawareness was associated with the lack of diversity in EEG alpha-rhythmic microstates, and (c) the probability for the occurrence and duration of delta-, theta- and slow-alpha-rhythmic microstates were associated with unawareness, whereas the probability for the occurrence and duration of fast-alpha-rhythmic microstates were associated with consciousness. In conclusion, resting EEG has a potential value in revealing NCC. This work may have implications for clinical care and medical–legal decisions in patients with disorders of consciousness. (shrink)

People are ingesting various information from different sense organs all the time to complete different cognitive tasks. The brain integrates and regulates this information. The two significant sensory channels for receiving external information are sight and hearing that have received extensive attention. This paper mainly studies the effect of music and visual-auditory stimulation on electroencephalogram of happy emotion recognition based on a complex system. In the experiment, the presentation was used to prepare the experimental stimulation program, and the cognitive (...) neuroscience experimental paradigm of EEG evoked by happy emotion pictures was established. Using 93 videos as natural stimuli, fMRI data were collected. Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t-test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two-classification algorithm can reach 98.8%, and the average accuracy can reach 83.45%. The results show that the brain source under the combined visual and auditory stimulus is not a simple superposition of the brain source of the single visual and auditory stimulation, but a new interactive source is generated. (shrink)

Research on electroencephalogram (EEG) characteristics associated with major depressive disorder (MDD) has accumulated diverse neurophysiologic findings related to the content, topography, neurochemistry, and functions of EEG oscillations. Significant progress has been made since the first landmark EEG study on affective disorders by Davidson 35 years ago. A systematic account of these data is important and necessary for building a consistent neuropsychophysiologic model of MDD and other affective disorders. Given the extensive data on frequency-dependent functional significance of EEG oscillations, a (...) frequency domain approach may reveal the types of brain functions involved and disturbed in MDD. In this review, we systematize and integrate diverse and often unconnected observations on the content, topography, neurochemistry, and functions of EEG oscillations involved in MDD within the general concept of an EEG oscillatory pattern. (shrink)

Virtual reality is a rapidly developing technology that simulates the real world. However, for some cybersickness-susceptible people, VR still has an unanswered problem—cybersickness—which becomes the main obstacle for users and content makers. Sensory conflict theory is a widely accepted theory for cybersickness. It proposes that conflict between afferent signals and internal models can cause cybersickness. This study analyzes the brain states that determine cybersickness occurrence and related uncomfortable feelings. Furthermore, we use the electroencephalogram microstates and functional connectivity approach based (...) on the sensory conflict theory. The microstate approach is a time–space analysis method that allows signals to be divided into several temporarily stable states, simultaneously allowing for the exploration of short- and long-range signals. These temporal dynamics can show the disturbances in mental processes associated with neurological and psychiatric conditions of cybersickness. Furthermore, the functional connectivity approach gives us in-depth insight and relationships between the sources related to cybersickness. We recruited 40 males, and they watched a VR video on a curved computer monitor for 10 min to experience cybersickness. We recorded the 5-min resting state EEG and 10-min EEG while watching the VR video. Then, we performed a microstate analysis, focusing on two temporal parameters: mean duration and global explained variance. Finally, we obtained the functional connectivity data using eLoreta and lagged phase synchronization. We discovered five sets of microstates, including four widely reported canonical microstates, during baseline and task conditions. The average duration increased in microstates A and B, which is related to the visual and auditory networks. The GEV and duration decreased in microstate C, whereas those in microstate D increased. Microstate C is related to the default mode network and D to the attention network. The temporal dynamics of the microstate parameters are from cybersickness disturbing the sensory, DMN, and attention networks. In the functional connectivity part, the LPS between the left and right parietal operculum significantly decreased compared with the baseline condition. Furthermore, the connectivity between the right OP and V5 significantly decreased. These results also support the disturbance of the sensory network because a conflict between the visual and vestibular system causes cybersickness. Changes in the microstates and functional connectivity support the sensory conflict theory. These results may provide additional information in understanding brain dynamics during cybersickness. (shrink)

e understanding of the etiology and pathogenesis of these psyc hiatric disorders such as schizophrenia and depression is still n ot completely clear. At present, there is a lack of objective ne urobiological markers that can be used in clinical routine work such as clinical diagnosis, curative effect evaluation and progn osis evaluation of psychiatric disorders. Therefore, it is of great clinical significance to find biomarkers to improve the diagnos is level and evaluate the curative effect. Electroencephalogram (EEG) is (...) a non-invasive technique to record the potential activi ty of biological brain, through which researchers can analyze th e mechanisms underlying psychiatric disorders. In addition, mac hine learning can be used to further verify the role of these el ectrophysiological indicators in clinical diagnosis and curative e ffect evaluation of psychiatric disorders. The goal of this resear ch topic is to advance research on neurobiomarkers and EEG/ MEG-based diagnostic methods for mental disorders. We hope to conduct in-depth research on the pathogenesis and diagnostic measures of mental diseases such as schizophrenia and depressi ve disorder through EEG/MEG, in order to improve the perfor mance of artificial intelligence for mental diseases diagnosing.Under this research theme, the following is a brief overview of nine published articles, which respectively studied the diagnosis, The work in this special issue may fall into three categories of research: 1) method... (shrink)

ObjectiveWhen regaining consciousness, patients who emerge from a minimally conscious state (EMCS) present with different levels of functional disability, which pose great challenges for treatment. This study investigated the frontoparietal activity in EMCS patients and its effects on functional disability.Materials and methodsIn this preliminary study, 12 EMCS patients and 12 healthy controls were recruited. We recorded a resting-state scalp electroencephalogram (EEG) for at least 5 min for each participant. Each patient was assessed using the disability rating scale (DRS) to (...) determine the level of functional disability. We analyzed the EEG power spectral density and sensor-level functional connectivity in relation to the patient’s functional disability.ResultsIn the frontoparietal region, EMCS patients demonstrated lower relative beta power (P < 0.01) and higher weighted phase lag index (wPLI) values in the theta (P < 0.01) and gamma (P < 0.01) bands than healthy controls. The frontoparietal theta wPLI values of EMCS patients were positively correlated with the DRS scores (rs = 0.629, P = 0.029). At the whole-brain level, EMCS patients only had higher wPLI values in the theta band (P < 0.01) than healthy controls. The whole-brain theta wPLI values of EMCS patients were also positively correlated with the DRS scores (rs = 0.650, P = 0.022). No significant difference in the power and connectivity between the frontoparietal region and the whole brain in EMCS patients was observed.ConclusionEMCS patients still experience neural dysfunction, especially in the frontoparietal region. However, the theta connectivity in the frontoparietal region did not increase specifically. At the level of the whole brain, the same shift could also be seen. Theta functional connectivity in the whole brain may underlie different levels of functional disability. (shrink)

Objective: The value of spontaneous EEG oscillations in distinguishing patients in vegetative and minimally conscious states was studied. Methods: We quantified dynamic repertoire of EEG oscillations in resting condition with closed eyes in patients in vegetative and minimally conscious states (VS and MCS). The exact composition of EEG oscillations was assessed by the probability-classification analysis of short-term EEG spectral patterns. Results: The probability of delta, theta and slow-alpha oscillations occurrence was smaller for patients in MCS than for VS. Additionally, only (...) patients in MCS demonstrated fast-alpha oscillation occurrence. Depending on the type and composition of EEG oscillations, the probability of their occurrence was either aetiology dependent or independent. The probability of EEG oscillations occurrence differentiated brain injuries with different aetiologies. Conclusions: Spontaneous EEG oscillations have a potential value in distinguishing patients in VS and MCS. Significance: This work may have implications for clinical care, rehabilitative programs and medical–legal decisions in patients with impaired consciousness states following coma due to acute brain injuries. (shrink)

In the human-computer interaction, electroencephalogram access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy features of each frequency band were extracted. Secondly, the constructed 3D MSE feature matrices (...) were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network and hidden Markov models. Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method. (shrink)

Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have urged the needs of mobile brain-computer interfaces (BCIs) for applications in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the (...) key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systemati-cally explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while con-currently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87±13.55%) to walking (1 MPH: 83.03±13.24%, 2 MPH: 79.47±13.53%, and 3 MPH: 75.26±17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications. (shrink)

In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact (...) removal requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems. (shrink)

Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine classifier to control a robot arm in order to help a person to find an object in the environment. (...) The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform and the empirical modal decomposition, combined with the principal component analysis that were fed through a linear and radial basis function kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction. (shrink)

Electroencephalogram (EEG) recordings are increasingly used to evaluate patients with disorders of consciousness (DOC) or assess their prognosis outcome in the short-term perspective. However, there is a lack of information concerning the effectiveness of EEG in classifying long-term (many years) outcome in chronic DOC patients. Here we tested whether EEG operational architectonics parameters (geared towards consciousness phenomenon detection rather than neurophysiological processes) could be useful for distinguishing a very long-term (6 years) clinical outcome of DOC patients whose EEGs were (...) registered within 3 months post-injury. The obtained results suggest that EEG recorded at third month after sustaining brain damage, may contain useful information on the long-term outcome of patients in vegetative state: it could discriminate patients who remain in a persistent vegetative state from patients who reach a minimally conscious state or even recover a full consciousness in a long-term perspective (6 years) post-injury. These findings, if confirmed in further studies, may be pivotal for long-term planning of clinical care, rehabilitative programs, medical-legal decisions concerning the patients, and policy makers. (shrink)

There is some complementarity of models for the origin of the electroencephalogram (EEG) and neural network models for information storage in brainlike systems. From the EEG models of Freeman, of Nunez, and of the authors' group we argue that the wavelike processes revealed in the EEG exhibit linear and near-equilibrium dynamics at macroscopic scale, despite extremely nonlinear – probably chaotic – dynamics at microscopic scale. Simulations of cortical neuronal interactions at global and microscopic scales are then presented. The simulations (...) depend on anatomical and physiological estimates of synaptic densities, coupling symmetries, synaptic gain, dendritic time constants, and axonal delays. It is shown that the frequency content, wave velocities, frequency/wavenumber spectra and response to cortical activation of the electrocorticogram (ECoG) can be reproduced by a “lumped” simulation treating small cortical areas as single-function units. The corresponding cellular neural network simulation has properties that include those of attractor neural networks proposed by Amit and by Parisi. Within the simulations at both scales, sharp transitions occur between low and high cell firing rates. These transitions may form a basis for neural interactions across scale. To maintain overall cortical dynamics in the normal low firing-rate range, interactions between the cortex and the subcortical systems are required to prevent runaway global excitation. Thus, the interaction of cortex and subcortex via corticostriatal and related pathways may partly regulate global dynamics by a principle analogous to adiabatic control of artificial neural networks. (shrink)

Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 subjects, was constructed. The electroencephalogram signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the (...) Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed thatK-Nearest Neighbor had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis. (shrink)

Extant research on design thinking is subjective and limited. This manuscript combines protocol analysis and electroencephalogram to read design thoughts in the core design activities of concept generation phase. The results suggest that alpha band power had event related synchronization in the scenario task and divergent thinking occupies a dominant position. However, it had event related desynchronization in analogy and inference activities, etc., and it is stronger for mental pressure and exercised cognitive processing. In addition, the parietooccipital area differs (...) significantly from other brain areas in most design activities. This study explores the relationship of different design thinking and EEG data, which is innovative and professional in the field of design, providing a more objective data basis and evaluation method for future applied research and diverse educational practices. (shrink)

This study examines the neural substrate of the understanding of human relationships in verbal communication with Japanese honorific sentences as experimental materials. We manipulated two types of Japanese verbs specifically used to represent respect for others, i.e., exalted and humble verbs, which represent respect for the person in the subject and the person in the object, respectively. We visually presented appropriate and anomalous sentences containing the two types of verbs and analyzed the electroencephalogram elicited by the verbs. We observed (...) significant parietal negativity at a latency of approximately 400 ms for anomalous verbs compared with appropriate verbs. This parietal negativity could be a manifestation of the pragmatic process used to integrate the linguistic forms with the human relationships represented in the sentences. The topographies of these event-related potentials corresponded well with those of ERPs for two second-person pronouns in Chinese. This correspondence suggests that the pragmatic integration process in honorific expressions is cross-linguistically common in part. Furthermore, we assessed the source localization by means of independent component analysis and dipole fitting and observed a significant difference in ERP between the honorific and control sentences in the IC cluster centered in the precentral gyrus and in the cluster centered in the medial part of the occipital lobe, which corresponded well with the functional magnetic resonance imaging findings for Japanese honorification. We also found several significant differences in the time-frequency analyses for the medial occipital cluster. These significant differences in the medial occipital cluster suggested that the circuit of the theory of mind was involved in the processing of Japanese honorification. Our results suggest that pragmatic and syntactic processing are performed in parallel because the person to be respected must fulfill the grammatical function appropriate for the honorific verb. (shrink)

In this exploratory, correlational study, several psychophysiological measures were assessed and the relation between these measures and an experimental self-report questionnaire to measure the seven implicit beliefs of sexual offenders ) was established in a sample of Dutch participants recruited from the healthy population using correlational analyses. After analyzing task performance, electroencephalogram data and electrocardiogram data, the psychophysiological variables were correlated with the experimental QITSO subscales. The subscale “children as sexual beings” correlated positively with the P300 amplitude at electrode (...) Pz. The subscale “women are unknowable” correlated positively with resting-state beta activity during eyes closed and eyes open, and with alpha activity during eyes open. Additionally, the subscale “entitlement’ correlated positively with low frequency heart rate variability power during eyes closed and eyes open, and with high frequency power during eyes closed. This study is a first exploratory step towards establishing a psychophysiological profile underlying the self-report questionnaire QITSO. (shrink)

The therapeutic potential of meditation for physical and mental well-being is well documented, however the possibility of adverse effects warrants further discussion of the suitability of any particular meditation practice for every given participant. This concern highlights the need for a personalized approach in the meditation practice adjusted for a concrete individual. This can be done by using an objective screening procedure that detects the weak and strong cognitive skills in brain function, thus helping design a tailored meditation training protocol. (...) Quantitative electroencephalogram (qEEG) is a suitable tool that allows identification of individual neurophysiological types. Using qEEG screening can aid developing a meditation training program that maximizes results and minimizes risk of potential negative effects. This brief theoretical–conceptual review provides a discussion of the problem and presents some illustrative results on the usage of qEEG screening for the guidance of mediation personalization. (shrink)

EEGs were analyzed to investigate the effect of experiences in listening to preferred music in dancers and non-dancers. Participants passively listened to instrumental music of their preferred genre for 2 min, alternate genres, and silence. Both groups showed increased activity for their preferred music compared to non-preferred music in the gamma, beta, and alpha frequency bands. The results suggest all participants' conscious recognition of and affective responses to their familiar music, appreciation of the tempo embedded in their preferred music and (...) emotional arousal, and enhanced attention mechanism for cognitive operations such as memory retrieval. The observed alpha activity is considered in the framework of the alpha functional inhibition hypothesis, in that years of experience listening to their favorite type of music may have honed the cerebral responses to achieve efficient cortical processes. Analyses of the electroencephalogram activity over 100s-long music pieces revealed a difference between dancers and non-dancers in the magnitude of an initial alpha event-related desynchronization and the later development of an alpha event-related synchronization for their preferred music. Dancers exhibited augmented alpha ERD, as well as augmented and uninterrupted alpha ERS over the remaining 80s. This augmentation in dancers is hypothesized to be derived from creative cognition or motor imagery operations developed through their dance experiences. (shrink)

Loving-Kindness Meditation (LKM) is an efficient mental practice with a long history that has recently attracted interest in the fields of neuroscience, medicine and education. However, the neural characters and underlying mechanisms have not yet been fully illustrated, which has hindered its practical usefulness. This study aimed to investigate LKM from varied aspects and interactions between the brain, the heart, and psychological measurements. A Buddhist monk practitioner was recruited to complete one 10-min LKM practice, in between two 10-min resting tasks (...) (pre- and post-resting) per experimental run. Two sets of single-channel wearable EEG devices were used to collect EEG data (placed at Fz and Pz) and heart rate simultaneously. A self-report evaluation was conducted to repeatedly record the comprehensive performance of mind and body in each session. EEG data were preprossessed and analyzed by EEGlab. Further statistics were made by SPSS. Spectrum analysis showed a significant increase of theta power (Fz: t = −3.356; p = 0.002; Pz: t = −5.199; p < 0.001) and decrease of heart rate between pre- and post-resting tasks (t = 4.092, p < 0.001). The analysis showed a negative correlation between theta power and heart rate (Fz: r = −0.681, p < 0.001; Pz: r = −0.384, p = 0.008), and a positive correlation between theta power and the self-designed report score (Fz: r = 0.601, p < 0.001). These findings suggest that LKM is accompanied by significant neurophysiological changes, mainly an increase in slower frequencies, such as theta, and a decrease in heart rate. More importantly, subjective psychological assessments were also correlated with objective neurophysiological measurements in a long-term meditator participant. During LKM meditation, this connection was stronger. The results of this case report have promising implications for LKM practice in daily life. (shrink)

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life (...) situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding. (shrink)

Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project “Feel Your Reach”. In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram. In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping (...) correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the “Feel Your Reach” framework to people with cervical spinal cord injury and evaluate the decoders’ performance while participants attempt to perform upper-limb movements. While on the one hand, we made major progress towards this ambitious goal, we also critically discuss current limitations. (shrink)

Background: Patients in a vegetative state pose problems in diagnosis, prognosis and treatment. Currently, no prognostic markers predict the chance of recovery, which has serious consequences, especially in end-of-life decision-making. Objective: We aimed to assess an objective measurement of prognosis using advanced electroencephalography (EEG). Methods: EEG data (19 channels) were collected in 14 patients who were diagnosed to be persistently vegetative based on repeated clinical evaluations at 3 months following brain damage. EEG structure parameters (amplitude, duration and variability within quasi-stationary (...) segments, as well as the spatial synchrony between such segments and the strength of this synchrony) were used to predict recovery of consciousness 3 months later. Results: The number and strength of cortical functional connections between EEG segments were higher in patients who recovered consciousness (P < .05 – P < .001) compared with those who did not recover. Linear regression analysis confirms that EEG structure parameters are capable of predicting (P = .0025) recovery of consciousness 6 months post-injury, whereas the same analysis failed to significantly predict patient outcome based on aspects of their clinical history alone (P = .629) or conventional EEG spectrum power (P = .473). Conclusions: The result of this preliminary study demonstrates that structural strategy of EEG analysis is better suited for providing prognosis of consciousness recovery than existing methods of clinical assessment and of conventional EEG. Our results may be a starting point for developing reliable prognosticators in patients who are in vegetative state, with the potential to improve their day-to-day management, quality of life, and access to early interventions. (shrink)

For years now, phase-amplitude cross frequency coupling (CFC) has been observed across multiple brain regions under different physiological and pathological conditions. It has been suggested that CFC serves as a mechanism that facilitates communication and information transfer between local and spatially separated neuronal populations. In non-invasive brain computer interfaces (BCI), CFC has not been thoroughly explored. In this work, we propose a CFC estimation method based on Linear Parameter Varying Autoregressive (LPV-AR) models and we assess its performance using both synthetic (...) data and electroencephalographic (EEG) data recorded during attempted arm/hand movements of spinal cord injured (SCI) participants. Our results corroborate the potentiality of CFC as a feature for movement attempt decoding and provide evidence of the superiority of our proposed CFC estimation approach compared to other commonly used techniques. (shrink)

Emotion recognition plays an important part in human-computer interaction. Currently, the main challenge in electroencephalogram -based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are then passed (...) through TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high: sadness, anger, and fear on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high: sadness, anger, and fear on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition. (shrink)

It was meaningful to predict the customers' decision-making behavior in the field of market. However, due to individual differences and complex, non-linear natures of the electroencephalogram signals, it was hard to classify the EEG signals and to predict customers' decisions by using traditional classification methods. To solve the aforementioned problems, a recurrent t-distributed stochastic neighbor embedding neural network was proposed in current study to classify the EEG signals in the designed brand extension paradigm and to predict the participants' decisions. (...) The recurrent t-SNE neural network contained two steps. In the first step, t-SNE algorithm was performed to extract features from EEG signals. Second, a recurrent neural network with long short-term memory layer, fully connected layer, and SoftMax layer was established to train the features, classify the EEG signals, as well as predict the cognitive performance. The proposed network could give a good prediction with accuracy around 87%. Its superior in prediction accuracy as compared to a recurrent principal component analysis network, a recurrent independent component correlation algorithm [independent component analysis ] network, a t-SNE support vector machine network, a t-SNE back propagation neural network, a deep LSTM neural network, and a convolutional neural network were also demonstrated. Moreover, the performance of the proposed network with different activated channels were also investigated and compared. The results showed that the proposed network could make a relatively good prediction with only 16 channels. The proposed network would become a potentially useful tool to help a company in making marketing decisions and to help uncover the neural mechanisms behind individuals' decision-making behavior with low cost and high efficiency. (shrink)

The goal of this study was to implement a Riemannian geometry -based algorithm to detect high mental workload and mental fatigue using task-induced electroencephalogram signals. In order to elicit high MWL and MF, the participants performed a cognitively demanding task in the form of the letter n-back task. We analyzed the time-varying characteristics of the EEG band power features in the theta and alpha frequency band at different task conditions and cortical areas by employing a RG-based framework. MWL and (...) MF were considered as too high, when the Riemannian distances of the task-run EEG reached or surpassed the threshold of the baseline EEG. The results of this study showed a BP increase in the theta and alpha frequency bands with increasing experiment duration, indicating elevated MWL and MF that impedes/hinders the task performance of the participants. High MWL and MF was detected in 8 out of 20 participants. The Riemannian distances also showed a steady increase toward the threshold with increasing experiment duration, with the most detections occurring toward the end of the experiment. To support our findings, subjective ratings and behavioral measures were also considered. (shrink)

Numerous studies had found that creativity is not only associated with low effort and flexible processes, but also associated with high effort and persistent processes especially when defensive behavior being induced negative emotions. The important role of self-esteem is to buffer the negative emotions and low self-esteem are prone to instigate various forms of defensive behaviors. Thus, we thought that the relationships between trait creativity and executive control brain networks might be modulated by self-esteem. The resting-state electroencephalogram (RS-EEG) microstates (...) can divided into four classical types (MS1, MS2, MS3 and MS4) which can reflect the brain networks as well as the dynamic characteristic of them. Thus, Williams creative tendency scale (WCTS) and Rosenberg Self-Esteem Scale (RSES) were used to investigate the modulating role of self-esteem on the relationships between trait creativity and the RS-EEG microstates. As our results showed, self-esteem consistently modulated the relationships between creativity and the duration and contribution of MS2 related to visual or imagery processing, the occurrence of MS3 related to cingulo-opercular networks, as well as transitions between MS2 and MS4 which was related to frontoparietal control networks. Based on these results, we thought that Moreover, individuals with low self-esteem might be more prone to influenced by negative emotions and develop low trait creativity; bottom-up information from vision or visual imagery might be beneficial to develop trait creativity for individuals with high self-esteem to improve trait creativity, while inhibition of irrelevant information could facilitate trait creativity for individuals with low self-esteem. (shrink)

Electroencephalogram plays an essential role in analysing and recognizing brain-related diseases. EEG has been increasingly used as a new type of biometrics in person identification and verification systems. These EEG-based systems are important components in applications for both police and civilian works, and both areas process a huge amount of EEG data. Storing and transmitting these huge amounts of data are significant challenges for data compression techniques. Lossy compression is used for EEG data as it provides a higher compression (...) ratio than lossless compression techniques. However, lossy compression can negatively influence the performance of EEG-based person identification and verification systems via the loss of information in the reconstructed data. To address this, we propose introducing performance measures as additional features in evaluating lossy compression techniques for EEG data. Our research explores if a common value of CR exists for different systems using datasets with lossy compression that could provide almost the same system performance with those using datasets without lossy compression. We performed experiments on EEG-based person identification and verification systems using two large EEG datasets, CHB MIT Scalp and Alcoholism, to investigate the relationship between standard lossy compression measures and our proposed system performance measures with the two lossy compression techniques, discrete wavelet transform—adaptive arithmetic coding and discrete wavelet transform—set partitioning in hierarchical trees. Our experimental results showed a common value of CR exists for different systems, specifically, 70 for person identification systems and 50 for person verification systems. (shrink)

Evoked potentials in the amplitude-time spectrum of the electroencephalogram are commonly used to assess the extent of brain responses to stimulation with noxious contact heat. The magnitude of the N- and P-waves are used as a semi-objective measure of the response to the painful stimulus: the higher the magnitude, the more painful the stimulus has been perceived. The strength of the N-P-wave response is also largely dependent on the chosen reference electrode site. The goal of this study was to (...) examine which reference technique excels both in practical and theoretical terms when analyzing noxious contact heat evoked potentials in the amplitude-time spectrum. We recruited 21 subjects. We applied seven noxious contact heat stimuli using two temperatures, 51°C, and 54°C, to each subject. During EEG analysis, we aimed to identify the referencing technique which produces the highest N-wave and P-wave amplitudes with as little artifactual influence as possible. For this purpose, we applied the following six referencing techniques: mathematically linked A1/A2, average reference, REST, AFz, Pz, and mathematically linked PO7/PO8. We evaluated how these techniques impact the N-P amplitudes of CHEPS based on our data from healthy subjects. Considering all factors, we found that mathematically linked earlobes to be the ideal referencing site to use when displaying and evaluating CHEPS in the amplitude-time spectrum. (shrink)

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram and functional near-infrared spectroscopy are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to (...) enhance brain–computer interface performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go. (shrink)

Transcranial alternating current stimulation and neurofeedback are two different types of non-invasive neuromodulation techniques, which can modulate brain activity and improve brain functioning. In this review, we compared the current state of knowledge related to the mechanisms of tACS and NFB and their effects on electroencephalogram activity and on aftereffects, including the duration of their persistence and potential behavioral benefits. Since alpha bandwidth has been broadly studied in NFB and in tACS research, the studies of NFB and tACS in (...) modulating alpha bandwidth were selected for comparing the online and offline effects of these two neuromodulation techniques. The factors responsible for variability in the responsiveness of the modulated EEG activity by tACS and NFB were analyzed and compared too. Based on the current literature related to tACS and NFB, it can be concluded that tACS and NFB differ a lot in the mechanisms responsible for their effects on an online EEG activity but they possibly share the common universal mechanisms responsible for the induction of aftereffects in the targeted stimulated EEG band, namely Hebbian and homeostatic plasticity. Many studies of both neuromodulation techniques report the aftereffects connected to the behavioral benefits. The duration of persistence of aftereffects for NFB and tACS is comparable. In relation to the factors influencing responsiveness to tACS and NFB, significantly more types of factors were analyzed in the NFB studies compared to the tACS studies. Several common factors for both tACS and NFB have been already investigated. Based on these outcomes, we propose several new research directions regarding tACS and NFB. (shrink)

Transcranial alternating-current stimulation in the frequency range of 1–100 Hz has come to be used routinely in electroencephalogram studies of brain function through entrainment of neuronal oscillations. It turned out, however, to be highly non-trivial to remove the strong stimulation signal, including its harmonic and non-harmonic distortions, as well as various induced higher-order artifacts from the EEG data recorded during the stimulation. In this paper, we discuss some of the problems encountered and present methodological approaches aimed at overcoming them. (...) To illustrate the mechanisms of artifact induction and the proposed removal strategies, we use data obtained with the help of a schematic demonstrator setup as well as human-subject data. (shrink)

The recognition in electroencephalogram of action idea is to identify what action people want to do by EEG. The significance of this project is to help people who have trouble in movement. Their action ideas are identified by EEG, and then robot hands can assist them to complete the action. This paper, with comparative experiments, used OpenBCI to collect EEG action ideas during static action and dynamic action and used the EEG recognition model Conv1D-GRU to training and recognition action, (...) respectively. The experimental result shows that the brain wave action idea is easier to recognize in static state. The accuracy of brain wave action idea recognition in dynamic state is only 72.27%, and the accuracy of brain wave action idea recognition in static state is 99.98%. The experimental result confirms that the action idea will be of great help to people with mobility difficulties. (shrink)

Transcranial Photobiomodulation has demonstrated its ability to alter electrophysiological activity in the human brain. However, it is unclear how tPBM modulates brain electroencephalogram networks and is related to human cognition. In this study, we recorded 64-channel EEG from 44 healthy humans before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation with an irradiance of 257 mW/cm2. In data processing, a novel methodology by combining group singular value decomposition with the exact low-resolution brain electromagnetic tomography was implemented and (...) performed on the 64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components projected in the 2D sensor and 3D source domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and were justified as 11 EEG brain networks. Finally, baseline-normalized power changes of each EEG brain network in each EEG frequency band were quantified during the first 4-min, second 4-min, and post tPBM/sham periods, followed by comparisons of frequency-specific power changes between tPBM and sham conditions. Our results showed that tPBM-induced increases in alpha powers occurred at default mode network, executive control network, frontal parietal network and lateral visual network. Moreover, the ability to decompose EEG signals into individual, independent brain networks facilitated to better visualize significant decreases in gamma power by tPBM. Many similarities were found between the cortical locations of SVD-revealed EEG networks and fMRI-identified resting-state networks. This consistency may shed light on mechanistic associations between tPBM-modulated brain networks and improved cognition outcomes. (shrink)

Radiation therapy therapists face challenging daily tasks that leave them prone to high attrition and burnout and subsequent deficits in performance. Here, we employed an accelerated alpha-theta neurofeedback protocol that is implementable in a busy medical workplace to test if 12 RTTs could learn the protocol and exhibit behavior and brain performance-related benefits. Following the 3-week protocol, participants showed a decrease in subjective cognitive workload and a decrease in response time during a performance task, as well as a decrease in (...) desynchrony of the alpha electroencephalogram band. Additionally, novel microstate analysis for neurofeedback showed a significant decrease in global field power following neurofeedback. These results suggest that the RTTs successfully learned the protocol and improved in perceived cognitive workload following 3 weeks of neurofeedback. In sum, this study presents promising behavioral improvements as well as brain performance-related evidence of neurophysiological changes following neurofeedback, supporting the feasibility of implementing neurofeedback in a busy workplace and encouraging the further study of neurofeedback as a tool to mitigate burnout. (shrink)

Recent advances in neuroscience indicate that analysis of bio-signals such as rest state electroencephalogram and eye-tracking data can provide more reliable evaluation of children autism spectrum disorder than traditional methods of behavior measurement relying on scales do. However, the effectiveness of the new approaches still lags behind the increasing requirement in clinical or educational practices as the “bio-marker” information carried by the bio-signal of a single-modality is likely insufficient or distorted. This study proposes an approach to joint analysis of (...) EEG and eye-tracking for children ASD evaluation. The approach focuses on deep fusion of the features in two modalities as no explicit correlations between the original bio-signals are available, which also limits the performance of existing methods along this direction. First, the synchronization measures, information entropy, and time-frequency features of the multi-channel EEG are derived. Then a random forest applies to the eye-tracking recordings of the same subjects to single out the most significant features. A graph convolutional network model then naturally fuses the two group of features to differentiate the children with ASD from the typically developed subjects. Experiments have been carried out on the two types of the bio-signals collected from 42 children. The results indicate that the proposed approach can achieve an accuracy of 95% in ASD detection, and strong correlations exist between the two bio-signals collected even asynchronously, in particular the EEG synchronization against the face related/joint attentions in terms of covariance. (shrink)

The prevalence of mobile phone addiction has increased rapidly in recent years, and it has had a certain negative impact on emotions and cognitive capacities. At the level of neural circuits, the continued increase in activity in the brain regions associated with addiction leads to neural adaptations and structural changes. At present, the spontaneous brain microstates that could be negatively influenced by MPA are unclear. In this study, the temporal characteristics of four resting-state electroencephalogram microstates related to mobile phone (...) addiction tendency were investigated using the Mobile Phone Addiction Tendency Scale. We attempted to analyze the correlation between MPAT and corresponding microstates and provide evidence to explain the brain and behavioral changes caused by MPA. The results showed that the total score of the MPATS was positively correlated with the duration of MS1, related to phonological processing and negatively correlated with the duration of MS2, related to visual or imagery processing, and MS4, related to the attentional network; the score of the withdrawal symptoms subscale was additionally associated with duration of MS3, related to the cingulo-opercular emotional network. Based on these results, we believe that MPAT may have some negative effects on attentional networks and sensory brain networks; moreover, withdrawal symptoms may induce some negative emotions. (shrink)

Using theoretical analysis of self-consciousness concept and experimental evidence on the brain default mode network (DMN) that constitutes the neural signature of self-referential processes, we hypothesized that the anterior and posterior subnets comprising the DMN should show differences in their integrity as a function of meditation training. Functional connectivity within DMN and its subnets (measured by operational synchrony) has been measured in ten novice meditators using an electroencephalogram (EEG) recording in a pre-/post-meditation intervention design. We have found that while (...) the whole DMN was clearly suppressed, different subnets of DMN responded differently after 4 months of meditation training: The strength of EEG operational synchrony in the right and left posterior modules of the DMN decreased in resting post-meditation condition compared to a pre-meditation condition, whereas the frontal DMN module on the contrary exhibited an increase in the strength of EEG operational synchrony. These findings combined with published data on functional–anatomic heterogeneity within the DMN and on trait subjective experiences commonly found following meditation allow us to propose that the first-person perspective and the sense of agency (the witnessing observer) are presented by the frontal DMN module, while the posterior modules of the DMN are generally responsible for the experience of the continuity of ‘I’ as embodied and localized within bodily space. Significance of these findings is discussed. (shrink)

If the cortex is an associative memory, strongly connected cell assemblies will form when neurons in different cortical areas are frequently active at the same time. The cortical distributions of these assemblies must be a consequence of where in the cortex correlated neuronal activity occurred during learning. An assembly can be considered a functional unit exhibiting activity states such as full activation (“ignition”) after appropriate sensory stimulation (possibly related to perception) and continuous reverberation of excitation within the assembly (a putative (...) memory process). This has implications for cortical topographies and activity dynamics of cell assemblies forming during language acquisition, in particular for those representing words. Cortical topographies of assemblies should be related to aspects of the meaning of the words they represent, and physiological signs of cell assembly ignition should be followed by possible indicators of reverberation. The following postulates are discussed in detail: (1) assemblies representing phonological word forms are strongly lateralized and distributed over perisylvian cortices; (2) assemblies representing highly abstract words such as grammatical function words are also strongly lateralized and restricted to these perisylvian regions; (3) assemblies representing concrete content words include additional neurons in both hemispheres; (4) assemblies representing words referring to visual stimuli include neurons in visual cortices; and (5) assemblies representing words referring to actions include neurons in motor cortices. Two main sources of evidence are used to evaluate these proposals: (a) imaging studies focusing on localizing word processing in the brain, based on stimulus-triggered event-related potentials (ERPs), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI), and (b) studies of the temporal dynamics of fast activity changes in the brain, as revealed by high-frequency responses recorded in the electroencephalogram (EEG) and magnetoencephalogram (MEG). These data provide evidence for processing differences between words and matched meaningless pseudowords, and between word classes, such as concrete content and abstract function words, and words evoking visual or motor associations. There is evidence for early word class-specific spreading of neuronal activity and for equally specific high-frequency responses occurring later. These results support a neurobiological model of language in the Hebbian tradition. Competing large-scale neuronal theories of language are discussed in light of the data summarized. Neurobiological perspectives on the problem of serial order of words in syntactic strings are considered in closing. Key Words: associative learning; cell assembly; cognition; cortex; ERP; EEG; fMRI; language; lexicon; MEG; PET; word category. (shrink)

Neuroarchitecture uses neuroscientific tools to better understand architectural design and its impact on human perception and subjective experience. The form or shape of the built environment is fundamental to architectural design, but not many studies have shown the impact of different forms on the inhabitants’ emotions. This study investigated the neurophysiological correlates of different interior forms on the perceivers’ affective state and the accompanying brain activity. To understand the impact of naturalistic three-dimensional (3D) architectural forms, it is essential to perceive (...) forms from different perspectives. We computed clusters of form features extracted from pictures of residential interiors and constructed exemplary 3D room models based on and representing different formal clusters. To investigate human brain activity during three-dimensional perception of architectural spaces, we used a mobile brain/body imaging (MoBI) approach recording the electroencephalogram (EEG) of participants while they naturally walk through different interior forms in virtual reality (VR). The results revealed a strong impact of curvature geometries on activity in the anterior cingulate cortex (ACC). Theta band activity in ACC correlated with specific feature types (rs (14) = 0.525, p = 0.037) and geometry (rs (14) = - 0.579, p = 0.019), providing evidence for a role of this structure in processing architectural features beyond their emotional impact. The posterior cingulate cortex and the occipital lobe were involved in the perception of different room perspectives during the stroll through the rooms. This study sheds new light on the use of mobile EEG and VR in architectural studies and provides the opportunity to study human brain dynamics in participants that actively explore and realistically experience architectural spaces. (shrink)

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an (...) electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: 1. Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. 2. ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are nondisclosing. 3. The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including nonlinear dynamical analysis, complexity measures, analysis of causal interactions, graph theory and information dynamics. 4. and 5. are too long to include here. (shrink)

This paper briefly review a current trend in neuroscience aiming to combine neurophysiological and physical concepts in order to understand the emergence of spatio-temporal patterns within brain activity by which brain constructs knowledge from multiple streams of information. The authors further suggest that the meanings, which subjectively are experienced as thoughts or perceptions can best be described objectively as created and carried by large fields of neural activity within the operational architectonics of brain functioning.

Recently, a three-dimensional construct model for complex experiential Selfhood has been proposed (Fingelkurts et al., 2016b,c). According to this model, three specific subnets (or modules) of the brain self-referential network (SRN) are responsible for the manifestation of three aspects/features of the subjective sense of Selfhood. Follow up multiple studies established a tight relation between alterations in the functional integrity of the triad of SRN modules and related to them three aspects/features of the sense of self; however, the causality of this (...) relation is yet to be shown. In this article we approached the question of causality by exploring functional integrity within the three SRN modules that are thought to underlie the three phenomenal components of Selfhood while these components were manipulated mentally by experienced meditators in a controlled and independent manner. Participants were requested, in a block-randomised manner, to mentally induce states representing either increased (up-regulation) or decreased (down-regulation) sense of (a) witnessing agency (“Self”), or (b) body representational-emotional agency (“Me”), or (c) reflective/narrative agency (“I”), while their brain activity was recorded by an electroencephalogram (EEG). This EEG-data was complemented by first-person phenomenological reports and standardised questionnaires which focused on subjective contents of three aspects of Selfhood. The results of the study strengthen the case for a direct causative relationship between three phenomenological aspects of Selfhood and related to them three modules of the brain SRN. Furthermore, the putative integrative model of the dynamic interrelations among three modules of the SRN has been proposed. (shrink)

Background and Objective: Understanding how trauma impacts the self-structure of individuals suffering from the Post-Traumatic Stress Disorder (PTSD) symptoms is a complex matter and despite several attempts to explain the relationship between trauma and the “Self”, this issue still lacks clarity. Therefore, adopting a new theoretical perspective may help understand PTSD deeper and to shed light on the underlying psychophysiological mechanisms. Methods: In this study, we employed the “three-dimensional construct model of the experiential selfhood” where three major components of selfhood (...) (phenomenal first-person agency, embodiment, and reflection/narration) are related to three Operational Modules (OMs) of the self-referential brain network. These modules can be reliably estimated through operational synchrony analysis of the Electroencephalogram (EEG). Six individuals with PTSD symptoms and twenty-nine sex-, age- and demographic- (race, education, marital status) matched healthy controls underwent resting state EEG signal acquisition with the following estimation of the synchrony strength within every OM. Results: Our results indicate that subjects with PTSD symptoms had significantly stronger EEG operational synchrony within anterior and right posterior OMs as well as significantly weaker EEG operational synchrony within left posterior OM compared to healthy controls. Moreover, increased the functional integrity of the anterior OM was positively associated with hyperactivity symptoms, reduced synchrony of the left posterior OM was associated with greater avoidance, and increased right posterior OM integrity was positively correlated with intrusion and mood symptoms. Conclusion: The results are interpreted in light of the triad model of selfhood and its theoretical and clinical implications (including a new treatment approach) are discussed. (shrink)

Based on previous studies implicating increased functional connectivity within the self-referential brain network in major depressive disorder (MDD), and considering the functional roles of three distinct modules of such brain net (responsible for three-dimensional components of Selfhood) together with the documented abnormalities of self-related processing in MDD, we tested the hypothesis that patients with depression would exhibit increased connectivity within each module of the self-referential brain network and that the strength of these connections would correlate positively with depression severity. Applying (...) the electroencephalogram (EEG) operational synchrony analysis to extract three modules of the self-referential brain network in 12 medication-free depressive outpatients and 10 control subjects we have found an increase in the strength of EEG synchrony within all three modules in depressive patients (though non-significant for the right module). Furthermore, multiple regression analysis that used 3 factors (values of synchrony strength for all three modules) as input indicated that combined increase in the strength of synchrony in all three modules was positively associated with severity of depression. Taken together the findings of this study suggest that depression is primarily associated with hypersynchrony in all three modules of the brain self-referential network (the anterior module been responsible for “witnessing observation and first–person perspective”, the left posterior module been responsible for “reflective agency and narration” and the right posterior module been responsible for “bodily representational-emotional agency”), thus contributing to excessive self-focus, rumination, and body tension. (shrink)

To build a true conscious robot requires that a robot’s “brain” be capable of supporting the phenomenal consciousness as human’s brain enjoys. Operational Architectonics framework through exploration of the temporal structure of information flow and inter-area interactions within the network of functional neuronal populations [by examining topographic sharp transition processes in the scalp electroencephalogram (EEG) on the millisecond scale] reveals and describes the EEG architecture which is analogous to the architecture of the phenomenal world. This suggests that the task (...) of creating the “machine” consciousness would require a machine implementation that can support the kind of hierarchical architecture found in EEG. (shrink)

Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is “cognitive-motor dissociation” (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram (EEG) recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient’s family, because this information may confuse, alarm and mislead. Instead, we need a (...) procedure for generating case-specific probabilistic assessments that can be communicated clearly. This article constructs a possible procedure with three key elements: (1) a shift from categorical “responding or not” assessments to degrees of evidence; (2) the use of patient-centred priors to convert degrees of evidence to probabilistic assessments; and (3) the use of standardized probability yardsticks to convey those assessments as clearly as possible. (shrink)

Previous research has demonstrated electroencephalogram (EEG) changes in response to low-odor concentrations, resulting in near-chance detection. Such findings have been taken as evidence for olfaction without awareness. We replicated and extended previous work by examining EEG responses to water-water control, 0.0001, 0.001, 0.01, and 1 ppm isoamyl acetate (IAA) in water paired with water only. Detection was above chance (>50%) for .001 and above, and alpha decreased only to those concentrations, suggesting that EEG changes corresponded to IAA awareness. However, (...) when correct trial EEGs were compared to incorrect trial EEGs during .001 ppm, right posterior/central alpha decreased during incorrect trials and alpha decreased more globally (including frontal sites) during correct trials. These data may not reflect awareness or unawareness per se. Instead, results are discussed regarding activation of perceptual systems in the posterior region during incorrect trials and the activation of frontal action systems during a subset of correct trials. (shrink)