AUTHOR=Collazos-Huertas Diego , Caicedo-Acosta Julian , Castaño-Duque German A. , Acosta-Medina Carlos D. TITLE=Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification JOURNAL=Frontiers in Neuroscience VOLUME=14 YEAR=2020 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00155 DOI=10.3389/fnins.2020.00155 ISSN=1662-453X 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.