The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. this paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (ieeG). it is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (Hfos) for identifying epileptic focus commonly referred to as the seizure onset zone (SoZ). in this analysis, the multi-channel interictal ieeG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SoZ and non-SoZ channels in ieeG data, the use of machine learning techniques is always tricky. to deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYn) with radial basis function kernel-based SVM was used to detect the focal segments. finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.

The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.

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