TY - GEN
T1 - Identification of gait-related brain activity CP using electroencephalographic signals
AU - Chai, Jingwen
AU - Chen, Gong
AU - Thangavel, Pavithra
AU - Dimitrakopoulos, Georgios N.
AU - Kakkos, Ioannis
AU - Sun, Yu
AU - Dai, Zhongxiang
AU - Yu, Haoyong
AU - Thakor, Nitish
AU - Bezerianos, Anastasios
AU - Li, Junhua
N1 - Funding Information:
*This work was supported by the Ministry of Education of Singapore under the grant MOE2014-T2-1-115. The authors also thank the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology (SINAPSE) under grant R-719-001-102-232.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/10
Y1 - 2017/8/10
N2 - Restoring normal walking abilities following the loss of them is a challenge. Importantly, there is a growing need for a better understanding of brain plasticity CP and the neural involvements for the initiation and control of these abilities so as to develop better rehabilitation programmes and external support devices. In this paper, we attempt to identify gait-related neural activities by decoding neural signals obtained from electroencephalography (EEG) measurements while subjects performed three types of walking: without exoskeleton (free walking), and with exoskeleton support (zero force and assisting force). An average classification accuracy of 92.0% for training and 73.8% for testing sets was achieved using features extracted from mu and beta frequency bands. Furthermore, we found that mu band features contributed significantly to the classification accuracy and were localized mainly in sensorimotor regions that are associated with the control of the exoskeleton. These findings contribute meaningful insight on the neural dynamics associated with lower limb movements and provide useful information for future developments of orthotic devices and rehabilitation programs.
AB - Restoring normal walking abilities following the loss of them is a challenge. Importantly, there is a growing need for a better understanding of brain plasticity CP and the neural involvements for the initiation and control of these abilities so as to develop better rehabilitation programmes and external support devices. In this paper, we attempt to identify gait-related neural activities by decoding neural signals obtained from electroencephalography (EEG) measurements while subjects performed three types of walking: without exoskeleton (free walking), and with exoskeleton support (zero force and assisting force). An average classification accuracy of 92.0% for training and 73.8% for testing sets was achieved using features extracted from mu and beta frequency bands. Furthermore, we found that mu band features contributed significantly to the classification accuracy and were localized mainly in sensorimotor regions that are associated with the control of the exoskeleton. These findings contribute meaningful insight on the neural dynamics associated with lower limb movements and provide useful information for future developments of orthotic devices and rehabilitation programs.
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U2 - 10.1109/NER.2017.8008410
DO - 10.1109/NER.2017.8008410
M3 - Conference contribution
AN - SCOPUS:85028594630
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 548
EP - 551
BT - 8th International IEEE EMBS Conference on Neural Engineering, NER 2017
PB - IEEE Computer Society
T2 - 8th International IEEE EMBS Conference on Neural Engineering, NER 2017
Y2 - 25 May 2017 through 28 May 2017
ER -