ADA: A decoding algorithm for temporally-variable brain responses.

ABSTRACT: Decoding mental contents from brain activity is a long-standing goal in theoretical neuroscience and neural engineering. While current methods perform well in tasks with externally timed events, such as perception or motor execution, decoding covert cognitive processes like imagery or memory recall remains challenging due to uncertainty in the timing of underlying neural dynamics. In these settings, neurophysiological responses are not reliably linked to observable behaviour and likely vary in latency across trials. This complicates the use of time-locked analysis techniques, which perform decoding time point by time point across trials, thus assuming consistent signal timing. This problem corresponds to an understudied class of supervised learning where input features may be effectively m

SUBMITTER: Oyarzo P 

PROVIDER: S-EPMC12657816 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature