TY - GEN
T1 - Deep James-Stein Neural Networks for Brain-Computer Interfaces
AU - Angjelichinoski, Marko
AU - Soltani, Mohammadreza
AU - Choi, John
AU - Pesaran, Bijan
AU - Tarokh, Vahid
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Nonparametric regression has proven to be successful in extracting features from limited data in neurological applications. However, due to data scarcity, most brain-computer interfaces still rely on linear classifiers. This work leverages the robustness of the James-Stein theorem in nonparametric regression to harness the potentials of deep learning and foster its successful application in neural engineering with small data sets. We propose a novel method that combines James-Stein regression for feature extraction, and deep neural network for decoding; we refer to the architecture as deep James-Stein neural network (DJSNN). We apply the DJSNN to decode eye movement goals in a memory-guided visual saccades to one of eight target locations. The results demonstrate that the DJSNN outperforms existing methods by a substantial margin, especially at deep cortical sites.
AB - Nonparametric regression has proven to be successful in extracting features from limited data in neurological applications. However, due to data scarcity, most brain-computer interfaces still rely on linear classifiers. This work leverages the robustness of the James-Stein theorem in nonparametric regression to harness the potentials of deep learning and foster its successful application in neural engineering with small data sets. We propose a novel method that combines James-Stein regression for feature extraction, and deep neural network for decoding; we refer to the architecture as deep James-Stein neural network (DJSNN). We apply the DJSNN to decode eye movement goals in a memory-guided visual saccades to one of eight target locations. The results demonstrate that the DJSNN outperforms existing methods by a substantial margin, especially at deep cortical sites.
KW - Brain-computer interfaces
KW - James-Stein estimation
KW - deep neural networks
KW - nonparametric regression
UR - http://www.scopus.com/inward/record.url?scp=85089241158&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089241158&partnerID=8YFLogxK
U2 - 10.1109/ICASSP40776.2020.9053694
DO - 10.1109/ICASSP40776.2020.9053694
M3 - Conference contribution
AN - SCOPUS:85089241158
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1339
EP - 1343
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -