Adaptive classification to reduce non-stationarity in visual evoked potential brain-computer interfaces

• Autorzy: Kapgate Deepak , Kalbande Dhananjay , Shrawankar Urmila

Identyfikatory

DOI

10.1515/bams-2019-0020

• Języki publikacji: EN

Abstrakty

EN

Non-stationarity of electroencephalogram (EEG) signals greatly affect classifier performance in brain-computer interface (BCI). To overcome this problem we propose an adaptive classifier model known as extended multiclass pooled mean linear discriminant analysis (EMPMLDA). Here, we update the average class pair co-variance matrix along with pooled mean values. Evaluation of classifiers are done on visual evoked cortical potential data-sets. We demonstrate that EMPMLDA can significantly outperform other static classifiers such as MLDA and adaptive classifiers (MPMLDA). Furthermore an optimal update coefficient can be achieved using different datasets.

Słowa kluczowe

EN

adaptive classification; brain-computer interface; nonstationarity; visual evoked potentials

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2019
• Tom: Vol. 15, no. 2
• Strony: art. no. 20190020
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Kapgate Deepak

• Department of Information Technology, G. H. Raisoni College of Engineering, Nagpur, India, Phone: +(91)9503703225

autor

Kalbande Dhananjay

• Department of Information Technology, G. H. Raisoni College of Engineering, Nagpur, India

autor

Shrawankar Urmila

• G. H. Raisoni College of Engineering, Nagpur, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).