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Electroencephalography-based endogenous brain-computer interface for online communication with a completely locked-in
Chang-Hee Han1, Yong-Wook Kim1, Do Yeon Kim1
1Department of Biomedical Engineering, Hanyang University, Seoul, 04763, South Korea.
Journal of Neuroengineering and Rehabilitation
|February 1, 2019
Summary
This study demonstrates that an electroencephalography (EEG)-based brain-computer interface (BCI) can enable online communication for individuals in a completely locked-in state (CLIS). An accuracy of 87.5% was achieved, offering new hope for communication restoration.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Rehabilitation Technology
Background:
- Brain-computer interfaces (BCIs) offer communication potential for paralyzed individuals.
- Electroencephalography (EEG)-based endogenous BCIs have not been successfully used for communication in a completely locked-in state (CLIS).
Observation:
- A female patient in CLIS, unable to communicate for over a year, participated in the study.
- Both offline and online experiments were conducted to assess the feasibility of the BCI system.
- The patient performed one of two mental-imagery tasks for 5-second intervals.
Findings:
- An EEG-based endogenous BCI paradigm was investigated for online binary communication.
- Riemannian geometry-based classification achieved 87.5% online classification accuracy on real-time EEG data.
- The optimal combination of mental tasks and classification strategy was determined offline.
Implications:
- This research suggests that EEG-based endogenous BCIs hold potential for enabling online communication in patients with CLIS.
- The findings open new avenues for restoring communication for individuals with severe motor impairments.
- Successful BCI application in CLIS could significantly improve quality of life and reduce social isolation.
Keywords:
Brain-computer interface (BCI)Completely locked-in state (CLIS)Electroencephalography (EEG)Pattern classificationRiemannian geometryMore Related Videos
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