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- Title
Electroencephalography-based brain-computer interface using neural networks.
- Authors
Pham Van Huu Thien; Nguyen Ngoc Son
- Abstract
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
- Subjects
BRAIN-computer interfaces; ELECTRIC wheelchairs; FAST Fourier transforms; WHEELCHAIRS; TEST systems; GESTURE
- Publication
Telkomnika, 2023, Vol 21, Issue 5, p1068
- ISSN
1693-6930
- Publication type
Article
- DOI
10.12928/TELKOMNIKA.v21i5.24839