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.
journal_name
Front Neuroscijournal_title
Frontiers in neuroscienceauthors
Collazos-Huertas D,Caicedo-Acosta J,Castaño-Duque GA,Acosta-Medina CDdoi
10.3389/fnins.2020.00155subject
Has Abstractpub_date
2020-02-25 00:00:00pages
155eissn
1662-4548issn
1662-453Xjournal_volume
14pub_type
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