Journal of neuroengineering and rehabilitation · 2019
Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in patient.
Клинический случайСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Han Chang-Hee, Kim Yong-Wook, Kim Do Yeon, Kim Seung Hyun, Nenadic Zoran, Im Chang-Hwan
Аннотация
BACKGROUND: 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).
METHODS: In 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.
RESULTS: An 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.
CONCLUSIONS: Our results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.
METHODS: In 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.
RESULTS: An 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.
CONCLUSIONS: Our results suggest that an EEG-based endogenous BCI has the potential to be used for online communication with a patient in CLIS.