Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning
Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-length sliding windows that may be misaligned with neural events. Existing window optimization methods often evaluate windows independently, overlooking temporal dependencies and potentially producing fragmented or physiologically implausible selections. We propose Temporal Structure Embedded Reinforcement Learning (TSE-RL), a framework that incorporates prior knowledge of MI event structure into a constrained Markov decision process for adaptive window optimization. First, TSE-RL assigns each window an explicit semantic role through Discard, Onset, Sustain, or Offset actions, extend...
| Authors | Huan Luo, Na Lu, Xiaopeng Wang et al. |
|---|---|
| Primary category | cs.NE |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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