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Classification of EEG signals using the spatio-temporal feature selection via the elastic net

Noei, S ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/ICBME.2016.7890962
  3. Abstract:
  4. Effective classification of motor imagery electroencephalograph (EEG) data is an important challenge. Spatial filtering such as Common Spatial Pattern (CSP) and its variants are commonly used for this task. However, CSP effectiveness depends on the subject-specific frequency band. Even by optimally selecting a subject-specific frequency band, this method still fails for some subjects. On the other hand, some studies suggest that temporal features may discriminate classes more efficiently. This work proposes a hybrid method based on elastic net and Least Absolute Shrinkage and Selector Operator (LASSO) to optimally select between spatial and temporal features. This algorithm uses joint spatial and temporal features followed by an optimal combined feature selection scheme for each subject. Results show significant improvement for subjects whose spatial features failed to produce acceptable results and overall improvement over the combined data. © 2016 IEEE
  5. Keywords:
  6. Common spatial pattern ; Electroencephalogram ; Feature selection ; Biomedical engineering ; Biomedical signal processing ; Biophysics ; Brain computer interface ; Electroencephalography ; Frequency bands ; Interfaces (computer) ; Regression analysis ; Common spatial patterns ; Hybrid method ; Motor imagery ; Spatial features ; Spatial filterings ; Spatio temporal features ; Subject-specific ; Temporal features ; Feature extraction
  7. Source: 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering, ICBME 2016, 23 November 2016 through 25 November 2016 ; 2017 , Pages 232-236 ; 9781509034529 (ISBN)
  8. URL: https://ieeexplore.ieee.org/document/7890962