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Automatic recognition methods for non-stationary electroencephalogram EEG data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. T girls Friedrichshafen Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier.

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Automatic recognition methods for non-stationary electroencephalogram EEG data collected from EEG sensors play an essential role in neurological detection.

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