Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification

Abstract

Selection of the time-window mainly affects the effectiveness of piecewise feature extraction procedures. We present an enhanced bag-of-patterns representation that allows capturing the higher-level structures of brain dynamics within a wide window range. So, we introduce augmented instance representations with extended window lengths for the short-time Common Spatial Pattern algorithm. Based on multiple-instance learning, the relevant bag-of-patterns are selected by a sparse regression to feed a bag classifier. The proposed higher-level structure representation promotes two contributions: (i) accuracy improvement of bi-conditional tasks, (ii) A better understanding of dynamic brain behavior through the learned sparse regression fits. Using a support vector machine classifier, the achieved performance on a public motor imagery dataset (left-hand and right-hand tasks) shows that the proposed framework performs very competitive results, providing robustness to the time variation of electroencephalography recordings and favoring the class separability.