Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification
| Autor: | Carlos Daniel Acosta-Medina, Diego Fabian Collazos-Huertas, Germán Albeiro Castaño-Duque, Julian Caicedo-Acosta |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Feature extraction 02 engineering and technology Accuracy improvement lcsh:RC321-571 multiple-instance learning 03 medical and health sciences 0302 clinical medicine Motor imagery motor imagery CSP Robustness (computer science) 0202 electrical engineering electronic engineering information engineering dynamic brain behavior lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry LASSO regularization Original Research business.industry General Neuroscience Pattern recognition Class separability Time–frequency analysis Piecewise 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Frontiers in Neuroscience Frontiers in Neuroscience, Vol 14 (2020) |
| ISSN: | 1662-453X 1662-4548 |
| Popis: | 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: \textit{i}) accuracy improvement of bi-conditional tasks, \textit{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. |
| Databáze: | OpenAIRE |
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