A new stable nonlinear textural feature extraction method based EEG signal classification method using substitution Box of the Hamsi hash function: Hamsi pattern. (15th January 2021)

• Record Type: Journal Article
• Authors: Tuncer, Turker
• Is Part Of: Applied acoustics. Volume 172(2021)
• Publication Date: 2021-01-15
• Subjects: Hamsi-Pat -- S-Box based feature generation -- INCA -- EEG signal classification -- Machine learning -- Artificial intelligence; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2020.107607
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: Background: The EEG signal classification is crucial for epileptic seizure prediction. Therefore, many machine learning model has been presented to classify EEG signals accurately. Material and Method: This work presents a novel automated EEG classification method by using a novel nonlinear feature extractor, and it is called as Hamsi-Pat. It uses the substitution box (S-Box) of the Hamsi hash function. As stated in the literature, S-Boxes have generally used for diffusion in symmetric encryption (especially block ciphers) methods and cryptologic hash functions. Since it is a nonlinear structure, this work aims to illustrate the merit of an S-Box for feature generation. Therefore, a new generation feature generator, which is Hamsi-Pat, is presented by using S-Box of the Hamsi hash function, and a novel EEG classification method is proposed by using Hamsi-Pat. The presented biomedical signal classification method has three elementary phases, and these phases are Hamsi-Pat based multileveled feature generation, iterative neighborhood component analysis (INCA) selector based feature dimension reduction, and classification using k nearest neighborhood (kNN) classifier. The presented Hamsi-Pat and INCA based methods were tested on Bonn electroencephalography (EEG) datasets. Result: This model yielded 99.20% classification accuracy on the used EEG dataset for five classes case and it yielded 100.0% accuracies for other cases. Conclusion: These results obviously denoted that the S-Boxes can be considered as a feature generator, and a novel S-Box based feature generation research area can be defined as textural feature generation and statistical feature generation.