TY - GEN
T1 - Classification and Transfer Learning of EEG during a Kinesthetic Motor Imagery Task using Deep Convolutional Neural Networks
AU - Craik, Alexander
AU - Kilicarslan, Atilla
AU - Contreras-Vidal, Jose L.
N1 - Funding Information:
This study acknowledges the support of Mission Connect, a TIRR Foundation.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The reliable classification of Electroencephalography (EEG) signals is a crucial step towards making EEG-controlled non-invasive neuro-exoskeleton rehabilitation a practical reality. EEG signals collected during motor imagery tasks have been proposed to act as a control signal for exoskeleton applications. Here, a Deep Convolutional Neural Network (DCNN) was optimized to classify a two-class kinesthetic motor imagery EEG dataset, leading to an optimized architecture consisting of four convolutional layers and three fully connected layers. Transfer learning, or the leveraging of data from past subjects to classify the intentions of a new subject, is important for rehabilitation as it helps to minimize the number of training sessions required from subjects who lack full motor functionality. The transfer learning training paradigm investigated through this study utilized region criticality trends to reduce the number of new subject training sessions and increase the classification performance when compared against a single-subject non-transfer-learning classifier.
AB - The reliable classification of Electroencephalography (EEG) signals is a crucial step towards making EEG-controlled non-invasive neuro-exoskeleton rehabilitation a practical reality. EEG signals collected during motor imagery tasks have been proposed to act as a control signal for exoskeleton applications. Here, a Deep Convolutional Neural Network (DCNN) was optimized to classify a two-class kinesthetic motor imagery EEG dataset, leading to an optimized architecture consisting of four convolutional layers and three fully connected layers. Transfer learning, or the leveraging of data from past subjects to classify the intentions of a new subject, is important for rehabilitation as it helps to minimize the number of training sessions required from subjects who lack full motor functionality. The transfer learning training paradigm investigated through this study utilized region criticality trends to reduce the number of new subject training sessions and increase the classification performance when compared against a single-subject non-transfer-learning classifier.
UR - http://www.scopus.com/inward/record.url?scp=85077905105&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2019.8857575
DO - 10.1109/EMBC.2019.8857575
M3 - Conference contribution
C2 - 31946530
AN - SCOPUS:85077905105
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3046
EP - 3049
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -