TY - GEN
T1 - Analysis of electroencephalography alteration during sustained cycling exercise using power spectrum and fuzzy entropy
AU - Lin, Szu Yu
AU - Jao, Chii Wen
AU - Wang, Po Shan
AU - Wu, Yu Te
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/8/8
Y1 - 2017/8/8
N2 - Electroencephalogram (EEG) is a common tool to study the changes in brain activity during exercise. In this study, we used recorded Electrocardiography (ECG) to define an average-to-maximal heart rate ratio (AMHRR) as a gauge of relative exercise workload, and explored how the increase of AMHRR affected the brain activity. The EEG signals were recorded from forty-four healthy subjects on four scalp sites, including C1, C2, P1, and P2 during nine minutes non-stop cycling exercise. The relationships between AMHRR and EEG power spectral or EEG fuzzy entropy (FuzzyEn) were established. Results of EEG power spectral and FuzzyEn displayed similar increasing patterns with AMHRR at all electrodes. Compared with EEG power spectral, the FuzzyEn has better specificity in selecting effective frequency bands, namely theta, alpha and beta bands. The FuzzyEn method can be applied in a wearable device for human machine interface (HMI) in monitoring the EEG during exercise in the future.
AB - Electroencephalogram (EEG) is a common tool to study the changes in brain activity during exercise. In this study, we used recorded Electrocardiography (ECG) to define an average-to-maximal heart rate ratio (AMHRR) as a gauge of relative exercise workload, and explored how the increase of AMHRR affected the brain activity. The EEG signals were recorded from forty-four healthy subjects on four scalp sites, including C1, C2, P1, and P2 during nine minutes non-stop cycling exercise. The relationships between AMHRR and EEG power spectral or EEG fuzzy entropy (FuzzyEn) were established. Results of EEG power spectral and FuzzyEn displayed similar increasing patterns with AMHRR at all electrodes. Compared with EEG power spectral, the FuzzyEn has better specificity in selecting effective frequency bands, namely theta, alpha and beta bands. The FuzzyEn method can be applied in a wearable device for human machine interface (HMI) in monitoring the EEG during exercise in the future.
UR - http://www.scopus.com/inward/record.url?scp=85030172222&partnerID=8YFLogxK
U2 - 10.1109/iFUZZY.2016.8004946
DO - 10.1109/iFUZZY.2016.8004946
M3 - Conference contribution
AN - SCOPUS:85030172222
T3 - 2016 International Conference on Fuzzy Theory and Its Applications, iFuzzy 2016
BT - 2016 International Conference on Fuzzy Theory and Its Applications, iFuzzy 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Conference on Fuzzy Theory and Its Applications, iFuzzy 2016
Y2 - 9 November 2016 through 11 November 2016
ER -