We present RPCA, a method well suited for extracting data matrices' low-rank component and this method allows (1) removal of task-irrelevant signal from data, ( ...
PDF | In this study, Robust Principal Component Analysis (RPCA) is applied to neural spike datasets to extract neural signatures that signify the onset.
Low-rank representation of neural activity and detection ... - IEEE Xplore
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In the context of neural activity, the low-rank matrix corresponds to the common features of neural activity across similar motor primitives, which are ...
Low-rank representation of neural activity and detection of submovements. record by Young Hwan Chang • Low-rank representation of neural activity and ...
Low-rank representation of neural activity and detection of submovements. YH Chang, M Chen, S Gowda, SA Overduin, JM Carmena, C Tomlin. 52nd IEEE Conference on ...
Apr 25, 2024 · Accelerating Submovement Decomposition With Search-Space ... Low-rank representation of neural activity and detection of submovements.
Low-rank representation of neural activity and detection of submovements Proceedings of the Ieee Conference On Decision and Control. 2544-2549. DOI: 10.1109 ...
The low-rank component putatively represents (submovement relevant) neural signatures and the sparse component represents neural activity unrelated to ...
... low-rank matrices", the Seventh Annual RECOMB/ISCB RSG Conference, Nov 9-14, San Diego 2014 (Accepted for oral presentation and invited to submit partner ...
The low-rank component putatively represents (submovement relevant) neural signatures and the sparse component represents neural activity unrelated to ...