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Descripción
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| Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduction is usually implemented via the mathematical approaches (e.g., principal component analysis, linear discriminant analysis, etc.) regardless of the origin of analyzed data. We hypothesize that since EEG features are determined by certain neurophysiological processes, they should have distinctive characteristics in spatiotemporal domain. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Chaos |
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ISSN
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1054-1500 |
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Factor de impacto JCR
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2,643 |
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Información de impacto
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Volumen
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29 |
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DOI
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10.1063/1.5113844 |
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Número de revista
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9 |
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Desde la página
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93110 |
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Hasta la página
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93130 |
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Mes
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SIN MES |
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Ranking
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