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.
Highlights
Brain–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)
Neurological assessment based on event-related potentials After IRB approval, neurological and associated signs found during the examined period between December 2015 to January 2016 confirmed that our participant had already progressed to CLIS based on the following evidence: there were no voluntary or involuntary eye movements, including blinking and corneal reflexes, with verbal commands or external stimuli such as light, tactile, and auditory stimuli
The only disadvantage of endogenous BCI paradigms might be the necessity of pre-training sessions to build classifiers (Note that the previous study [26] adopted an exogenous BCI paradigm, but it required training sessions); this study showed that there were no significant differences in the classification accuracies even when a training dataset that was recorded several months prior to an online experiment was used for the online experiment
Summary
Brain–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). Steady-state visual evoked potential (SSVEP) [7, 8], event-related potential (ERP) [9, 10], and motor imagery (MI) [11, 12] are three major EEG-based BCI paradigms. These can be roughly classified into exogenous and endogenous BCI paradigms. No study has yet reported on the successful online communication with CLIS patients using EEG-based endogenous BCI systems, which do not require external stimuli, but instead use neural signals volitionally modulated by BCI users. To the best of our knowledge, there were only two EEG-based endogenous BCI studies with CLIS patients; both failed to establish online communication [21, 27]
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