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• W3084407798 abstract "In this paper, an attempt for identification of electroencephalogram (EEG) signal features for detection of stressed mental state has been made. In present lifestyle, almost all peoples suffer from stress. Assessment and management of stress are necessary to avoid serious mental illness. By the mean of EEG signal analysis, stressed mental state is easily detected. Here, 24 EEG features including spectral, amplitude, connectivity, and range properties are extracted in different frequency bands. A well-known feature selection algorithm sequential floating backward selection (SFBS) is used to select subset of best features. Features of theta frequency band EEG signal are identified as best features for mental stress identification." @default.
• W3084407798 created "2020-09-14" @default.
• W3084407798 creator A5065486002 @default.
• W3084407798 date "2020-09-09" @default.
• W3084407798 modified "2023-09-23" @default.
• W3084407798 title "Analysis and Identification of EEG Features for Mental Stress" @default.
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• W3084407798 doi "https://doi.org/10.1007/978-981-15-5788-0_19" @default.
• W3084407798 hasPublicationYear "2020" @default.
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