Electroencephalography-based endogenous brain–computer interface for online communication with a completely locked-in patient

Abstract

BackgroundBrain–computer interfaces (BCIs) have demonstrated the potential to provide paralyzed individuals with new means of communication, but an electroencephalography (EEG)-based endogenous BCI has never been successfully used for communication with a patient in a completely locked-in state (CLIS).MethodsIn this study, we investigated the possibility of using an EEG-based endogenous BCI paradigm for online binary communication by a patient in CLIS. A female patient in CLIS participated in this study. She had not communicated even with her family for more than one year with complete loss of motor function. Offline and online experiments were conducted to validate the feasibility of the proposed BCI system. In the offline experiment, we determined the best combination of mental tasks and the optimal classification strategy leading to the best performance. In the online experiment, we investigated whether our BCI system could be potentially used for real-time communication with the patient.ResultsAn online classification accuracy of 87.5% was achieved when Riemannian geometry-based classification was applied to real-time EEG data recorded while the patient was performing one of two mental-imagery tasks for 5 s.ConclusionsOur results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.

Brain-Based Binary Communication Using Spatiotemporal Features of fNIRS Responses

“…Encouraging communication results have been reported using EEG-based BCIs (Farwell and Donchin, 1988;Birbaumer et al, 1999;Leuthardt et al, 2004;Nijboer et al, 2008). Recent binary communication paradigms established accuracies consistently above 70% Käthner et al, 2015), even reaching an accuracy of 87.5% in one patient (Han et al, 2019). EEG-based BCIs have been mainly tested with visual paradigms using event-related potentials that, at least partly, dependent on patients ability to fixate (Brunner et al, 2010;Treder and Blankertz, 2010).…”

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

“…The advantage of a BCI can be focused on the implementation of a communication system [34], real-time control of peripherals as robots [35][36][37] or emotion recognition, among other. Many of the current BCI based on the detection of EEG imaginary movements (for a review [38]) analyze the alpha/beta EEG band, usually using several channels.…”

Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research

“…They could not, however, establish goaldirected communication [37]. Goal-directed communication with an ALS patient was recently reported by Han et al [38], but this result could not be reproduced in a follow-up study four months later. As there is no reason to assume that ALS ceases to progress once patients have entered the CLIS, and recent evidence pointing to a collapse of the frequency of the α-rhythm in long-term CLIS-ALS patients [39], welldocumented negative results are essential to foster a deeper understanding regarding which skills CLIS-ALS patients retain -and for how long.…”