A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction

A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction. Carrera-Leon, O., Ramirez, J., M., Alarcon-Aquino, V., Baker, M., D'Croz-Baron, D., & Gomez-Gil, P. In 2012 Workshop on Engineering Applications, pages 1-6, 5, 2012. IEEE.

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A brain computer interface (BCI) is a system that aims to control devices by analyzing brain signals patterns. In this work, a convenient time-frequency representation (TFR) for visualizing ERD/ERS phenomenon (Event related synchronization and desynchronization) based on Hilbert transform and spatial patterns is addressed, and a wavelet based feature extraction method for motor imagery tasks is presented. The feature vectors are constructed with four statistical and energy parameters obtained from wavelet decomposition, based on the sub-band coding algorithm. Experimentation with three classification methods for comparison purposes was carried out using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). In each case, ten-fold validation is used to obtain average misclassification rates.

@inproceedings{
 title = {A motor imagery BCI experiment using wavelet analysis and spatial patterns feature extraction},
 type = {inproceedings},
 year = {2012},
 pages = {1-6},
 websites = {http://ieeexplore.ieee.org/document/6220084/},
 month = {5},
 publisher = {IEEE},
 id = {925c7822-9dd6-36d0-894d-bd6533e5de05},
 created = {2022-08-29T17:42:58.080Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:58.080Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
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 abstract = {A brain computer interface (BCI) is a system that aims to control devices by analyzing brain signals patterns. In this work, a convenient time-frequency representation (TFR) for visualizing ERD/ERS phenomenon (Event related synchronization and desynchronization) based on Hilbert transform and spatial patterns is addressed, and a wavelet based feature extraction method for motor imagery tasks is presented. The feature vectors are constructed with four statistical and energy parameters obtained from wavelet decomposition, based on the sub-band coding algorithm. Experimentation with three classification methods for comparison purposes was carried out using Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). In each case, ten-fold validation is used to obtain average misclassification rates.},
 bibtype = {inproceedings},
 author = {Carrera-Leon, Obed and Ramirez, Juan Manuel and Alarcon-Aquino, Vicente and Baker, Mary and D'Croz-Baron, David and Gomez-Gil, Pilar},
 doi = {10.1109/WEA.2012.6220084},
 booktitle = {2012 Workshop on Engineering Applications}
}

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