Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated Eeg signals

Juan Manuel Ramirez-Cortes; Vicente Alarcon-Aquino; Gerardo Rosas-Cholula; Pilar Gomez-Gil; Jorge Escamilla-Ambrosio

doi:10.1007/978-1-4614-0373-9_27

Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated Eeg signals

Juan Manuel Ramirez-Cortes, Vicente Alarcon-Aquino, Gerardo Rosas-Cholula, Pilar Gomez-Gil, Jorge Escamilla-Ambrosio

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

An experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. 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 using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered to the ANFIS system. Experimental results are presented.

Original language	English
Title of host publication	Intelligent Automation and Systems Engineering
Pages	353-365
Number of pages	13
DOIs	https://doi.org/10.1007/978-1-4614-0373-9_27
State	Published - 2011
Event	International Conference on Advances in Intelligent Automation and Systems Engineering - Berkeley, CA, United States Duration: 20 Oct 2010 → 22 Oct 2010

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	103 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Advances in Intelligent Automation and Systems Engineering
Country/Territory	United States
City	Berkeley, CA
Period	20/10/10 → 22/10/10

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10.1007/978-1-4614-0373-9_27

Cite this

Ramirez-Cortes, J. M., Alarcon-Aquino, V., Rosas-Cholula, G., Gomez-Gil, P., & Escamilla-Ambrosio, J. (2011). Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated Eeg signals. In Intelligent Automation and Systems Engineering (pp. 353-365). (Lecture Notes in Electrical Engineering; Vol. 103 LNEE). https://doi.org/10.1007/978-1-4614-0373-9_27

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abstract = "An experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. 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 using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered to the ANFIS system. Experimental results are presented.",

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Ramirez-Cortes, JM, Alarcon-Aquino, V, Rosas-Cholula, G, Gomez-Gil, P & Escamilla-Ambrosio, J 2011, Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated Eeg signals. in Intelligent Automation and Systems Engineering. Lecture Notes in Electrical Engineering, vol. 103 LNEE, pp. 353-365, International Conference on Advances in Intelligent Automation and Systems Engineering, Berkeley, CA, United States, 20/10/10. https://doi.org/10.1007/978-1-4614-0373-9_27

Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated Eeg signals. / Ramirez-Cortes, Juan Manuel; Alarcon-Aquino, Vicente; Rosas-Cholula, Gerardo et al.
Intelligent Automation and Systems Engineering. 2011. p. 353-365 (Lecture Notes in Electrical Engineering; Vol. 103 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated Eeg signals

AU - Ramirez-Cortes, Juan Manuel

AU - Alarcon-Aquino, Vicente

AU - Rosas-Cholula, Gerardo

AU - Gomez-Gil, Pilar

AU - Escamilla-Ambrosio, Jorge

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N2 - An experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. 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 using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered to the ANFIS system. Experimental results are presented.

AB - An experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. 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 using the discrete wavelet transform (DWT) applied on the preprocessed signal as a feature extractor, and further entered to the ANFIS system. Experimental results are presented.

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Anfis-based P300 rhythm detection using wavelet feature extraction on blind source separated Eeg signals

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