TY - JOUR
T1 - A Hybrid EEG-fNIRS Brain-Computer Interface Based on Dynamic Functional Connectivity and Long Short-Term Memory
AU - Wang, Peng
AU - He, Jing
AU - Lan, Wenli
AU - Yang, Hui
AU - Leng, Yue
AU - Wang, Ruimin
AU - Iramina, Keiji
AU - Ge, Sheng
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this study, we combined dynamic functional connectivity (dFC) and long short-term memory (LSTM) to obtain robust high classification accuracy on bimodal motor imagery signals. To characterize the information flow in brain cortical activity, we measured the variation of the functional interaction between different signal channels with dFC in the feature extraction stage. The variation measured by dFC was then decoded by LSTM for classification. In addition, we used the electroencephalograph (EEG) and functional near-infrared spectroscopy (fNIRS) bimodal signals acquired simultaneously to overcome the artifact susceptibility of EEG. The average classification accuracy of 20 subjects was 90.03%, significantly higher than the traditional common spatial pattern (CSP) method. The result demonstrates the effectiveness of our data processing model.
AB - In this study, we combined dynamic functional connectivity (dFC) and long short-term memory (LSTM) to obtain robust high classification accuracy on bimodal motor imagery signals. To characterize the information flow in brain cortical activity, we measured the variation of the functional interaction between different signal channels with dFC in the feature extraction stage. The variation measured by dFC was then decoded by LSTM for classification. In addition, we used the electroencephalograph (EEG) and functional near-infrared spectroscopy (fNIRS) bimodal signals acquired simultaneously to overcome the artifact susceptibility of EEG. The average classification accuracy of 20 subjects was 90.03%, significantly higher than the traditional common spatial pattern (CSP) method. The result demonstrates the effectiveness of our data processing model.
UR - http://www.scopus.com/inward/record.url?scp=85104612979&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85104612979&partnerID=8YFLogxK
U2 - 10.1109/IAEAC50856.2021.9390839
DO - 10.1109/IAEAC50856.2021.9390839
M3 - Conference article
AN - SCOPUS:85104612979
SP - 2214
EP - 2219
JO - IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
JF - IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
SN - 2689-6621
M1 - 9390839
T2 - 5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021
Y2 - 12 March 2021 through 14 March 2021
ER -