Abstract
To develop a good BCI, it is necessary to take into account what features can be extracted and what classification algorithm can be used. In this manuscript, a cross-validation method is used to compare different classification algorithms (SVM, KNN, discriminant analyses and decision trees) as applied to EEG records obtained by a non-invasive wireless electroencephalograph (Emotiv EPOC+). The features used in the classification algorithms are the power spectrum of the signal and the hemispheric asymmetry. The used experimental paradigms (e.g. motor imagery) are designed to be used with reduced mobility people, because the aim is to develop a BCI to control an external device such as a wheelchair or a prosthesis.
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Acknowledgments
This work was conducted under the auspices of the Research Project ProID2017010100, supported by Consejería de Economía, Industria, Comercio y Conocimiento from Canary Government (Spain) and FEDER (European regional development fund (ERDF)), the Researches Projects TEC2016-80063-C3-2-R and DPI2017-90002-R, supported by Spanish Ministerio de Economía, Industria y Competitividad. J. Ortega has a fellowship by Agencia Canaria de Investigación, Innovación y Sociedad de la Información (ACIISI) from Canary Government (Spain).
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Martín-Chinea, K., Ortega, J., Gómez-González, J.F., Toledo, J., Pereda, E., Acosta, L. (2020). Accuracy of Classification Algorithms Applied to EEG Records from Emotiv EPOC+ Using Their Spectral and Asymmetry Features. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_37
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DOI: https://doi.org/10.1007/978-3-030-36778-7_37
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