TY - GEN
T1 - Wavelet Shrinkage and Thresholding Based Robust Classification for Brain-Computer Interface
AU - Banerjee, Taposh
AU - Choi, John
AU - Pesaran, Bijan
AU - Ba, Demba
AU - Tarokh, Vahid
N1 - Funding Information:
Designed, supervised the experiments, finalized the manuscript and provided financial support: Changshun Shao.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - A macaque monkey is trained to perform two different kinds of tasks, memory aided and visually aided. In each task, the monkey saccades to eight possible target locations. A classifier is proposed for direction decoding and task decoding based on local field potentials (LFP) collected from the prefrontal cortex. The LFP time-series data is modeled in a nonparametric regression framework, as a function corrupted by Gaussian noise. It is shown that if the function belongs to Besov bodies, then the proposed wavelet shrinkage and thresholding based classifier is robust and consistent. The classifier is then applied to the LFP data to achieve high decoding performance. The proposed classifier is also quite general and can be applied for the classification of other types of time-series data as well, not necessarily brain data.
AB - A macaque monkey is trained to perform two different kinds of tasks, memory aided and visually aided. In each task, the monkey saccades to eight possible target locations. A classifier is proposed for direction decoding and task decoding based on local field potentials (LFP) collected from the prefrontal cortex. The LFP time-series data is modeled in a nonparametric regression framework, as a function corrupted by Gaussian noise. It is shown that if the function belongs to Besov bodies, then the proposed wavelet shrinkage and thresholding based classifier is robust and consistent. The classifier is then applied to the LFP data to achieve high decoding performance. The proposed classifier is also quite general and can be applied for the classification of other types of time-series data as well, not necessarily brain data.
KW - Adaptive minimaxity and sparsity
KW - Besov bodies
KW - Gaussian sequence model
KW - Local field potentials
KW - Minimax function estimators
UR - http://www.scopus.com/inward/record.url?scp=85054283137&partnerID=8YFLogxK
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U2 - 10.1109/ICASSP.2018.8462321
DO - 10.1109/ICASSP.2018.8462321
M3 - Conference contribution
AN - SCOPUS:85054283137
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 836
EP - 840
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -