Scientists, engineers, and healthcare professionals are currently developing a variety of new devices under the category of brain-computer interfaces (BCIs). Current and future applications are both medical/assistive (e.g., for communication) and non-medical (e.g., for gaming). This array of possibilities comes with ethical challenges for all stakeholders. As a result, BCIs have been an object of both hope and concern in various media. We argue that these conflicting sentiments can be productively understood in terms of personhood, specifically the impact of BCIs (...) on what it means to be a person and to be recognized as such by others. To understand the dynamics of personhood in the context of BCI use and investigate whether ethical guidance is required, a meeting entitled "BCIs and Personhood: A Deliberative Workshop" was held in May 2018. In this article, we describe how BCIs raise important questions about personhood and propose recommendations for BCI development and governance. (shrink)

Neurofeedback has been suggested as a potential complementary therapy to different psychiatric disorders. Of interest for this approach is the prediction of individual performance and outcomes. In this study, we applied functional connectivity-based modeling using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) modalities to (i) investigate whether resting-state connectivity predicts performance during an affective neurofeedback task and (ii) evaluate the extent to which predictive connectivity profiles are correlated across EEG and fNIRS techniques. The fNIRS oxyhemoglobin and deoxyhemoglobin concentrations and the (...) EEG beta and gamma bands modulated by the alpha frequency band (beta-m-alpha and gamma-m-alpha, respectively) recorded over the frontal cortex of healthy subjects were used to estimate functional connectivity from each neuroimaging modality. For each connectivity matrix, relevant edges were selected in a leave-one-subject-out procedure, summed into “connectivity summary scores” (CSS), and submitted as inputs to a support vector regressor (SVR). Then, the performance of the left-out-subject was predicted using the trained SVR model. Linear relationships between the CSS across both modalities were evaluated using Pearson’s correlation. The predictive model showed a mean absolute error smaller than 20%, and the fNIRS oxyhemoglobin CSS was significantly correlated with the EEG gamma-m-alpha CSS (r = −0.456, p = 0.030). These results support that pre-task electrophysiological and hemodynamic resting-state connectivity are potential predictors of neurofeedback performance and are meaningfully coupled. This investigation motivates the use of joint EEG-fNIRS connectivity as outcome predictors, as well as a tool for functional connectivity coupling investigation. (shrink)