Med Biol Eng Comput - Feature selection on movement imagery discrimination and attention detection.

Tópicos

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Resumo

Noninvasive brain-computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar ratios (PRs) were extracted from the EEG data for movement imagery discrimination. Event-related potentials (ERPs) were employed in the discrimination of cue-evoked responses. While center-out arrows, commonly used in calibration sessions, cued the subjects in the first experiment (for both PR and ERP analyses), less stimulating arrows that were centered in the visual field were employed in the second experiment (for ERP analysis). The proposed algorithms outperformed other three popular feature selection algorithms in movement imagery discrimination. In the first experiment, both algorithms achieved classification errors as low as 12.5% reducing the feature set dimensionality by more than 90%. The classification accuracy of ERPs dropped in the second experiment since centered cues reduced the amplitude of cue-evoked ERPs. The two proposed algorithms effectively reduced feature dimensionality while increasing movement imagery discrimination and detected cue-evoked ERPs that reflect subject attention.

Resumo Limpo

noninvas braincomput interfac bci translat subject electroencephalogram eeg featur devic command larg featur set downselect effici featur translat work propos two differ featur downselect algorithm bci sequenti forward select b acrossgroup varianc power rar ratio prs extract eeg data movement imageri discrimin eventrel potenti erp employ discrimin cueevok respons centerout arrow common use calibr session cu subject first experi pr erp analys less stimul arrow center visual field employ second experi erp analysi propos algorithm outperform three popular featur select algorithm movement imageri discrimin first experi algorithm achiev classif error low reduc featur set dimension classif accuraci erp drop second experi sinc center cue reduc amplitud cueevok erp two propos algorithm effect reduc featur dimension increas movement imageri discrimin detect cueevok erp reflect subject attent

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