TELKOMNIKA Telecommunication Computing Electronics and Control
Electroencephalography-based brain-computer interface using neural networks

TELKOMNIKA Telecommunication Computing Electronics and Control
Electroencephalography-based brain-computer interface using neural networks

Brain-controller wheelchair
Electroencephalography
Fast fourier transform
Neural networks

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.

Pham Van Huu Thien, Nguyen Ngoc Son

http://telkomnika.uad.ac.id

Mar 07, 2023

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