SSVEP-Based BCI Wheelchair Control System
원문 초록
A brain-computer interface (BCI) is a system that allows a person to communicate or control the surroundings without depending on the brain's normal output pathways of peripheral nerves and muscles. A lot of successful applications have arisen utilizing the advantages of BCI to assist disabled people with so-called assistive technology. Considering using BCI has fewer limitations and huge potential, this project has been proposed to control the movement of an electronic wheelchair via brain signals. The goal of this project is to help disabled people, especially paralyzed people suffering from motor disabilities, improve their life qualities. In order to realize the project stated above, Steady-State Visual Evoked Potential (SSVEP) is involved. It can be easily elicited in the visual cortical with the same frequency as the one is being focused by the subject. There are two important parts in this project. One is to process the EEG signals and another one is to make a visual stimulator using hardware. The EEG signals are processed in Matlab using the algorithm of Butterworth Infinite Impulse Response (IIR) bandpass filter (for preprocessing) and Fast Fourier Transform (FFT) (for feature extraction). Besides, a harmonics-based classification method is proposed and applied in the classification part. Moreover, the design of the visual stimulator combines LEDs as flickers and LCDs as information displayers on one panel. Microcontrollers are employed to control the SSVEP visual stimuli panel. This project is evaluated by subjects with different races and ages. Experimental results show the system is easy to be operated and it can achieve approximately a minimum 1-second time delay. So it demonstrates that this SSVEP-based BCI-controlled wheelchair has a huge potential to be applied to disabled people in the future.
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Improving SSVEP BCI Spellers With Data Augmentation and Language Models
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP Tasks
토론
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