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Sensors (Basel). 2018 May 9;18(5). pii: E1483. doi: 10.3390/s18051483.

Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors.

Author information

1
Tecnológico Nacional de México-CENIDET, Interior Internado Palmira S/N, Col. Palmira, Cuernavaca, Morelos, C.P. 62490, México. alanperezvidal@cenidet.edu.mx.
2
Tecnológico Nacional de México-CENIDET, Interior Internado Palmira S/N, Col. Palmira, Cuernavaca, Morelos, C.P. 62490, México. cgarcia@cenidet.edu.mx.
3
Tecnológico Nacional de México-Instituto Tecnológico de Orizaba, Av. Oriente 9 N° 852, Col. Emiliano Zapata, Orizaba, C.P. 94320, México. amartinez@ito-depi.edu.mx.
4
Tecnológico Nacional de México-Instituto Tecnológico de Orizaba, Av. Oriente 9 N° 852, Col. Emiliano Zapata, Orizaba, C.P. 94320, México. rposada@itorizaba.edu.mx.

Abstract

The evoked potential is a neuronal activity that originates when a stimulus is presented. To achieve its detection, various techniques of brain signal processing can be used. One of the most studied evoked potentials is the P300 brain wave, which usually appears between 300 and 500 ms after the stimulus. Currently, the detection of P300 evoked potentials is of great importance due to its unique properties that allow the development of applications such as spellers, lie detectors, and diagnosis of psychiatric disorders. The present study was developed to demonstrate the usefulness of the Stockwell transform in the process of identifying P300 evoked potentials using a low-cost electroencephalography (EEG) device with only two brain sensors. The acquisition of signals was carried out using the Emotiv EPOC® device—a wireless EEG headset. In the feature extraction, the Stockwell transform was used to obtain time-frequency information. The algorithms of linear discriminant analysis and a support vector machine were used in the classification process. The experiments were carried out with 10 participants; men with an average age of 25.3 years in good health. In general, a good performance (75⁻92%) was obtained in identifying P300 evoked potentials.

KEYWORDS:

P300 evoked potentials; Stockwell transform; brain-computer interface; electroencephalograph; non-invasive brain sensors; signals processing; wireless device

PMID:
29747374
PMCID:
PMC5982572
DOI:
10.3390/s18051483
[Indexed for MEDLINE]
Free PMC Article

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