Adaptive time window selection improves motor imagery brain interface accuracy
Time Window Optimization for Motor Imagery Classification via Temporal Structure Embedded Reinforcement Learning
Neural and Evolutionary ComputingMachine Learning
Summary
Motor imagery brain-computer interfaces read brain signals to control devices, but they usually use fixed time windows that don’t always match the brain’s natural activity. The authors propose a new method that picks time windows more intelligently by recognizing different phases of brain activity and keeping the sequence realistic. Their approach uses reinforcement learning with a model that understands the order of brain events and remembers past windows better. This method works well across different people and sessions, making brain signal decoding more accurate.
What this means in practice
- •For bci system developers: Improve brain signal decoding accuracy by integrating adaptive window selection based on temporal event structure into motor imagery classifiers.
- •For wearable device engineers: Enhance real-time neural monitoring by using temporally aware window selection to better align processing with physiological signal phases.
Authors
Huan Luo, Na Lu, Xiaopeng Wang, Ziyang Shang
Abstract
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, extending binary retain/discard decisions to event-level alignment. Second, a finite-state machine masks invalid actions to enforce the temporal order of MI phases and promote physiologically plausible window sequences. Third, a Transformer-based history encoder captures dependencies across windows, improving training stability and computational efficiency compared with recurrent architectures. TSE-RL is plug-and-play and can be integrated with diverse MI classifiers. Experiments on multiple benchmark datasets demonstrate consistent improvements in decoding performance in both cross-session and cross-subject settings. Visualization analyses further suggest that the learned window selection policy aligns with neurophysiological principles.