Automated classification of eight different Electroencephalogram (EEG) bands using hybrid of Fast Fourier Transform (FFT) with machine learning methods

Analysing and processing the EEG dataset is crucial. Countless actions have been taken to ensure that the researcher in brain studies always achieves informative data and produces notable findings. There are several standard procedures to produce an informative result in analysing the EEG data. However, the techniques used in each standard procedure might be different for the researcher or data analyst because they have their preferences to suit the purpose of their experiments to adapt with the dataset collected. Not only the current manual method is time-consuming, but the main challenges are that researchers need to analyse only a small portion of the brain signals that are the most relevant to be observed through the analysis of several bands such as Very low, Delta, Theta, Alpha-1, Alpha-2, Beta-1, Beta-2, and Gamma. Therefore, one of the best alternatives is to automate the process of classifying the eight bands and extract the most relevant features. Hence, this paper proposed an automated classification method and feature extraction method through hybridising Fast Fourier Transform (FFT) with three different machine learning methods (KNN, SVM, and ANN) that can improve the efficiency of EEG analysis. Based on the result, the FFT + SVM method gives a 100% accuracy and successfully classified the bands into different of eight EEG bands accurately.