ArticleFrontiers in neuroscience2020
Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments.Sensors (Basel, Switzerland) · 2023Article
- KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Deep and Wide Transfer Learning with Kernel Matching for Pooling Data from Electroencephalography and Psychological Questionnaires.Sensors (Basel, Switzerland) · 2021Article
- Suppressing of Power Line Artifact From Electroencephalogram Measurements Using Sparsity in Frequency Domain.Frontiers in neuroscience · 2021Article
- Optimization of Task Allocation for Collaborative Brain-Computer Interface Based on Motor Imagery.Frontiers in neuroscience · 2021Article
- CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification.Brain informatics · 2020Article
- Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns.Entropy (Basel, Switzerland) · 2020Article
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Authors and funding
4 authors.
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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.
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