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]](https://bibbase.org/img/filetypes/link.svg "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},
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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},
doi = {10.1109/CERMA.2010.48},
booktitle = {2010 IEEE Electronics, Robotics and Automotive Mechanics Conference}
}
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