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- W2389751593 abstract "The feature extraction of electroencephalographic(EEG) signals is a crucial component in a brain-computer interface(BCI),which plays a decisive role in increasing classification accuracy and consequent information transfer rate.Feature information for classification was extracted by three multichannel linear descriptors that were applied alone and together to three electrode subsets of interest,resulting in twelve feature vectors.Five subjects participated in an on-line feedback BCI experiment during which they were asked to imagine the movement of either left or right hand.The EEG recordings from all subjects were analyzed off-line.The averaged classification accuracies of the five subjects ranged from 89% to 93.5% for the eight feature vectors from the two electrode subsets of seven and eleven leads,and their best classification accuracies ranged between 85% and 99.9%.The performance of the descriptors-based features was compared with that of autoregressive(AR) model-based features.Results indicated that multichannel linear descriptors were effective methods for feature extraction in motor imagery-based BCIs and the ideal electrode number used in the method were between seven and eleven." @default.
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- W2389751593 date "2007-01-01" @default.
- W2389751593 modified "2023-09-23" @default.
- W2389751593 title "Study on Classification Algorithms of a Brain—Computer Interface Based on Multichannel Linear Descriptors" @default.
- W2389751593 hasPublicationYear "2007" @default.
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