Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm

This study introduces a new spatial filter-solving paradigm named adaptive spatial pattern (ASP), which aims to minimize the energy intra-class matrix and maximize the inter-class matrix of MI-EEG after spatial filtering. The filter bank adaptive and common spatial pattern (FBACSP), our proposed method for MI-EEG decoding, amalgamates ASP spatial filters with CSP features across multiple frequency bands. Through a dual-stage feature selection strategy, it employs the Particle Swarm Optimization algorithm for spatial filter optimization, surpassing traditional CSP approaches in MI classification.

• 1
  The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively.
• 2
  Compared with the baseline algorithm, filter bank common spatial pattern (FBCSP), the proposed algorithm improves by 11.44 and 7.11% on two datasets, respectively (p  <  0.05).
• 3
  ASP features have excellent decoding ability for MI-EEG signals and explains the improvement of classification performance by the introduction of ASP features.