Debarshi Kumar Sanyal

The paper proposes a mix-bagging classifier with sliding window feature extraction and fuzzy discernibility matrix for motor-imagery EEG classification, achieving 92.14% accuracy on BCI Competition II Dataset III.

The paper compares temporal window-based and filter-bank approaches for motor-imagery EEG classification, finding that overlapping segments achieve the highest accuracy of 91.43% on BCI Competition II Dataset III.