Browsing by Author "Chen, Tsan-Yu"

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Development of a Reliable SSVEP-Based BCI Mobile Dialing System
(2016)
Chen, Tsan-Yu
;
Feng, Chih-Wei
;
Fang, Wai-Chi
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
This paper demonstrates a high performance brain-computer interface (BCI) that allows users to dial phone numbers. The system is based on Canonical Correlation Analysis (CCA) and the automatic artifacts removal system based on BSS-CCA method and Steady-State Visual Evoked Potential (SSVEP). Through six frequency bands (9Hz, 10Hz, 11Hz, 12Hz, 13 Hz, 14Hz) displayed on the screen, subjects can choose a phone number by gazing at the display interface. Implementing a prototype of the SSVEP-based BCI system verifies the effectiveness of the proposed system. Experimental results show that the subjects\' SSVEP can successfully control the dial device through the proposed BCI system with high identification accuracy.
FPGA Implementation of EEG System-on-Chip with Automatic Artifacts Removal based on BSS-CCA Method
(2016-01-01)
Chou, Chia-Ching
;
Chen, Tsan-Yu
;
Fang, Wai-Chi
;
電機工程學系
;
Department of Electrical and Computer Engineering
This paper presents an automatic muscle artifacts removal system for multi-channel electroencephalogram (EEG) applications. Since EEG signals are very weak and highly sensitive to the environment, they are easily contaminated by noises and artifacts. To get clean and usable EEG signals for brain-computer interface (BCI) applications, we should acquire these signals from the human brain without artifacts. Recently, Blind Source Separation (BSS) technique based on Canonical Correlation Analysis (CCA) was proposed to reconstruct clean EEG signals from recordings by removing muscle artifacts components. To enhance the feasibility and reliability of BCIs, EEG processing systems used for BCIs should be more portable and signals should be acquired in real-time without artifacts. To match with these requirements, a hardware design of the artifacts removal system is adopted for artifacts extraction. The performance of eye-blink and muscle artifacts elimination is evaluated through the correlation coefficients between processed and pure EEG signals. The experimental results show that the average correlation coefficients for eye-blink and muscle elimination are 0.9341 and 0.8927 respectively.