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• W2785758529 abstract "In this paper, we have classified the initial dips that are detected from the prefrontal and motor cortices using functional near-infrared spectroscopy (fNIRS) for brain-computer interface (BCI). The fNIRS data of mental arithmetic, mental counting, and right-hand finger tapping tasks are acquired from 5 healthy subjects. Vector phase analysis with a threshold circle (as a decision criterion) is used to detect the initial dips. Five different features including signal mean, signal slope, signal minimum value, kurtosis, and skewness in 0∼1, 0∼1.5, 0∼2, and 0∼2.5 sec windows are computed using oxyhemoglobin (HbO) signals. Linear discriminant analysis is used for the classification of the data. The average accuracy of 66.6% is obtained using signal mean and signal minimum value in 0∼2.5 sec window. We used a conventional hemodynamic response to extract the signal mean and signal slope as features in 2∼7 sec window for further validation of our results. LDA-based classification resulted in 73.2% accurate results for conventional hemodynamic response. The results seem significant for BCI using initial dip features." @default.
• W2785758529 created "2018-02-23" @default.
• W2785758529 creator A5021609168 @default.
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• W2785758529 date "2017-11-01" @default.
• W2785758529 modified "2023-09-25" @default.
• W2785758529 title "Classification of prefrontal and motor cortex initial dips for fNIRS-based-BCI" @default.
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• W2785758529 doi "https://doi.org/10.1109/cacs.2017.8284261" @default.
• W2785758529 hasPublicationYear "2017" @default.
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