Appling of Neural Networks to Classification of Brain-Computer Interface Data

Plechawska–Wójcik, Małgorzata; Wolszczak, Piotr

doi:10.1007/978-3-319-34099-9_37

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The results obtained prove that the mobile EEG better meets the needs of BCI applications, as the signal recorded by this type of EEG contains a considerably lower number of biological artefacts and a significantly decreased number of technological artefacts related to the power network with the frequency of 50 Hz [5]. These features might facilitate achieving the following results: 1. the performance requirements for the system based on the mobile EEG would be lower, since it enables to process the obtained signal consuming less performance resources (simpler artefact correction method, absence of notch filter); 2. better possibilities to process data in the frequency domain in the range of higher frequencies (40+ Hz), which would be achieved thanks to the absence of the notch filter.…”

Section: Discussionmentioning

confidence: 72%

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Identification, characterisation, and correction of artefacts in electroencephalographic data in study of stationary and mobile electroencephalograph

Kaczorowska

2017

ITM Web Conf.

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Abstract. The present paper covers the review of artefacts of EEG recording, as well as methods to eliminate them, i.e. PCA and ICA. The reviewed artefacts were of various origins: biological (muscle, eye blinking, eyeball moving), as well as technological artefacts caused mainly by the power network with the frequency of 50 Hz. The research has been conducted on a group consisting of 8 students. The research was carried out with the use of two defined models of electroencephalographs: stationary and mobile, then results have been compared. The comparison of two amplifiers combined with various artefact elimination methods has been presented. Presented research has shown that the record performed by the mobile amplifier contains the artefacts of significantly lower amplitude.

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Section: Discussionmentioning

confidence: 72%

“…The mobile electroencephalograph is a comparatively new type of electroencephalograph convenient for implementing BCIs [4,5]. Mobile EEG does not hamper the examined person due to the absence of cables connecting it with a computer.…”

Section: Introductionmentioning

confidence: 99%