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- W2945001056 abstract "In recent several decades, brain-computer interface (BCI) technology continually yield fruitful results. The electrocorticogram (ECoG) has attracted considerable interest because of its advantages of higher signal-to-noise ratio and greater long-term stability than electroencephalography (EEG) signals. We present an optimal scheme of ECoG signals for motor imagery (MI) classification. The time-frequency features are first extracted by the modified S-transform (MST) algorithm, and then a classifier is trained by using the support vector machine (SVM). In addition, channel selection is performed to reduce the computational complexity of MI-based BCI scheme. This method was tested on BCI Competition III dataset I. The MST coupled with the SVM can obtain the satisfactory classification of 95%. Channel selection can greatly reduce the computational burden of classification and enable this scheme to classify MI tasks in real time." @default.
- W2945001056 created "2019-05-29" @default.
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- W2945001056 date "2019-03-01" @default.
- W2945001056 modified "2023-09-25" @default.
- W2945001056 title "Classification of Motor Imagery Electrocorticogram Signals for Brain-Computer Interface" @default.
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- W2945001056 doi "https://doi.org/10.1109/ner.2019.8716963" @default.
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