Abstract
Problem statement: Brain-Computer Interface (BCI) is a emerging research area which translates the brain signals for any motor related actions into computer understandable signals by capturing the signal, processing the signal and classifying the motor imagery. This area of work finds various applications in neuroprosthetics. Mental activity leads to changes of electrophysiological signals like the Electroencephalogram (EEG) or Electrocorticogram (ECoG). Approach: The BCI system detects such changes and transforms it into a control signal which can, for example, be used as to control a electric wheel. In this study the BCI paradigm is tested by our proposed Gaussian smoothened Fast Hartley Transform (GS-FHT) which is used to compute the energies of different motor imageries the subject thinks after selecting the required frequencies using band pass filter. Results: We apply this procedure to BCI Competition dataset IVA, a publicly available EEG repository. Conclusion: The evaluations of preprocessed signals showed that the extracted features were interpretable and can lead to high classification accuracy by various mining algorithms.
Highlights
An emerging technology is Brain-Computer Interface (BCI) which enables paralyzed people to communicate with the external world
One of the most effective algorithms for Motor Imagery (MI)-BCI is based on Common Spatial Pattern (CSP) technique (Ramoser et al, 2000; Guger et al, 2000)
The Filter bank CSP (FBCSP) (Ang et al, 2008) uses CSP features from a set of fixed band pass filters and feature selection algorithm based on mutual information to effectively choose the subject-specific features
Summary
An emerging technology is Brain-Computer Interface (BCI) which enables paralyzed people to communicate with the external world. The FBCSP (Ang et al, 2008) uses CSP features from a set of fixed band pass filters and feature selection algorithm based on mutual information to effectively choose the subject-specific features. This selection process selects features from the relevant frequency components. In this study we propose to measure energy of specific motor imageries in the brain signal using our proposed Gaussian Smoothened Fast Hartley Transform (GS-FHT) along with the Chebyshev filter and data resembling. The metric combines a per-feature value distance metric with global feature weights that account for relative differences in discriminative power of the features
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