TY - GEN
T1 - FPGA implementation of EEG system-on-chip with automatic artifacts removal based on BSS-CCA method
AU - Chou, Chia Ching
AU - Chen, Tsan Yu
AU - Fang, Wai-Chi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Artifact
KW - BSS-CCA
KW - EEG
KW - Muscle artifact removal
UR - http://www.scopus.com/inward/record.url?scp=85014168502&partnerID=8YFLogxK
U2 - 10.1109/BioCAS.2016.7833772
DO - 10.1109/BioCAS.2016.7833772
M3 - Conference contribution
AN - SCOPUS:85014168502
T3 - Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
SP - 224
EP - 227
BT - Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
Y2 - 17 October 2016 through 19 October 2016
ER -