You better hope that your hospital has this if you present there as locked-in.
Electroencephalography-based endogenous brain–computer interface for online communication with a completely locked-in patient
- Chang-Hee Han,
- Yong-Wook Kim,
- Do Yeon Kim,
- Seung Hyun Kim,
- Zoran Nenadic and
- Chang-Hwan ImEmail authorView ORCID ID profile
Journal of NeuroEngineering and Rehabilitation201916:18
© The Author(s). 2019
- Received: 21 September 2018
- Accepted: 23 January 2019
- Published: 30 January 2019
Abstract
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.
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