On Signal P-300 Detection for BCI Applications Based on Wavelet Analysis and ICA Preprocessing. Rosas-Cholula, G., Ramirez-Cortes, J., M., Alarcon-Aquino, V., Martinez-Carballido, J., & Gomez-Gil, P. In 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference, pages 360-365, 9, 2010. IEEE.
On Signal P-300 Detection for BCI Applications Based on Wavelet Analysis and ICA Preprocessing [link]Website  doi  abstract   bibtex   
This paper describes an experiment on the detection of a P-300 rhythm from electroencephalographic signals for brain computer interfaces applications. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected through a time-scale analysis based on the discrete wavelet transform (DWT). Comparison using the Short Time Fourier Transform (STFT), and Wigner-Ville Distribution (WVD) indicates that the DWT outperforms the others as an analyzing tool for P300 rhythm detection. © 2010 IEEE.
@inproceedings{
 title = {On Signal P-300 Detection for BCI Applications Based on Wavelet Analysis and ICA Preprocessing},
 type = {inproceedings},
 year = {2010},
 keywords = {BCI,DWT,ICA,P300},
 pages = {360-365},
 websites = {http://ieeexplore.ieee.org/document/5692363/},
 month = {9},
 publisher = {IEEE},
 id = {e8c818d8-71a3-35e7-aeb5-068ddf69f88a},
 created = {2022-08-29T17:43:10.744Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:10.744Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper describes an experiment on the detection of a P-300 rhythm from electroencephalographic signals for brain computer interfaces applications. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected through a time-scale analysis based on the discrete wavelet transform (DWT). Comparison using the Short Time Fourier Transform (STFT), and Wigner-Ville Distribution (WVD) indicates that the DWT outperforms the others as an analyzing tool for P300 rhythm detection. © 2010 IEEE.},
 bibtype = {inproceedings},
 author = {Rosas-Cholula, Gerardo and Ramirez-Cortes, Juan Manuel and Alarcon-Aquino, Vicente and Martinez-Carballido, Jorge and Gomez-Gil, Pilar},
 doi = {10.1109/CERMA.2010.48},
 booktitle = {2010 IEEE Electronics, Robotics and Automotive Mechanics Conference}
}

Downloads: 0