Automated and accurate focal EEG signal detection method based on the cube pattern

Tuncer, Turker
Dogan, Sengul
Kaya, Muhammed Çağrı
Subasi, Abdulhamit
Electroencephalography (EEG) signals are named letters of the brain, and their translation is a complex issue. This work recommends a new hand-crafted feature-based EEG signal classification model, including a new local histogram-based feature generation function, the cube pattern. The recommended model comprises preprocessing/signal denoising, feature extraction using the presented cube pattern, neighborhood component analysis-based feature selection, and classification by employing 25 classifiers. Multi-scale principal component analysis (MSPCA) is applied to the raw EEG signals in the denoising phase. Afterward, the denoised EEG signals are forwarded to the feature extraction method. Next, tunable q-factor wavelet transform (TQWT) is employed to denoise signals for decomposition, and levels/sub-bands are generated. The selected features are classified from 25 classifiers by using the MATLAB Classification Learning tool. The presented model is applied to a commonly used EEG signal dataset. Variable performance evaluation metrics are used to test the performance of each classifier. Per the calculated results, the presented model reached over 99% accuracy using 24 of the 25 classifiers, and a comprehensive benchmark is obtained. The calculated results and obtained findings denote the high performance of the presented cube pattern and the neighborhood component analysis-based model.
Multimedia Tools and Applications


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Citation Formats
T. Tuncer, S. Dogan, M. Ç. Kaya, and A. Subasi, “Automated and accurate focal EEG signal detection method based on the cube pattern,” Multimedia Tools and Applications, pp. 0–0, 2023, Accessed: 00, 2023. [Online]. Available: