#6038. Decoding Premovement Patterns with Task-Related Component Analysis

July 2026publication date
Proposal available till 28-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Science Applications;
Cognitive Neuroscience;
Computer Vision and Pattern Recognition;
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Abstract:
Noninvasive brain–computer interface (BCI)-based electroencephalograms (EEGs) have made great progress in cognitive activities detection. However, the decoding of premovements from EEG signals remains a challenge for noninvasive BCI. This work aims to decode human intention (movement or rest) before movement onset from EEG signals. We propose to decode premovement patterns from movement-related cortical potential activities with task-related component analysis and canonical correlation patterns (TRCA+CCPs). Specifically, we first optimize the MRCP data with the spatial filter TRCA. CCPs are then extracted from the optimized signals. The extracted CCPs are classified with the linear discriminated analysis classifier. We applied the classification in a sliding window, which changes from readiness potential (RP section) to movement-monitoring potential (MMP section).
Keywords:
Canonical correlation patterns; Detection of movement onset; Electroencephalogram; Movement-related cortical potential; Task-related component analysis

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