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  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},
 identifiers = {[object Object]},
 keywords = {BCI,DWT,ICA,P300},
 pages = {360-365},
 websites = {http://ieeexplore.ieee.org/document/5692363/},
 month = {9},
 publisher = {IEEE},
 id = {320885c9-eb45-3a86-baa7-9d0edc6ce902},
 created = {2020-07-06T21:18:25.733Z},
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 last_modified = {2021-10-23T16:53:33.953Z},
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 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},
 booktitle = {2010 IEEE Electronics, Robotics and Automotive Mechanics Conference}
}

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