A design and evaluation of wearable p300 brain-computer interface system based on Hololens2 / 生物医学工程学杂志
J. biomed. eng
; Sheng wu yi xue gong cheng xue za zhi;(6): 709-717, 2023.
Article
in Zh
| WPRIM
| ID: wpr-1008891
Responsible library:
WPRO
ABSTRACT
Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.
Key words
Full text:
1
Database:
WPRIM
Main subject:
Quality of Life
/
Event-Related Potentials, P300
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Brain-Computer Interfaces
/
Wearable Electronic Devices
/
Amyotrophic Lateral Sclerosis
Limits:
Humans
Language:
Zh
Journal:
J. biomed. eng
/
Sheng wu yi xue gong cheng xue za zhi
Year:
2023
Document type:
Article