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

Date

2023-01-01

Author

Tuncer, Turker
Dogan, Sengul
Kaya, Muhammed Çağrı
Subasi, Abdulhamit

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Cube pattern, EEG classification, Multi-scale principal component analysis, NCA, TQWT

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147129072&origin=inward
https://hdl.handle.net/11511/102323

Journal

Multimedia Tools and Applications

DOI

https://doi.org/10.1007/s11042-023-14430-0

Collections

Department of Computer Engineering, Article

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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: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85147129072&origin=inward.