This research highlights the importance of the prefrontal theta-EEG rhythm in sustained attention monitoring over the Fp1 electrode. In an experiment conducted with 20 participants, four successive mental tasks are sent briefly by an automated computer program connected to a speakerphone: wait, relax, get ready, and concentrate. Furthermore, each individual participated in this experiment 20 times. The result is determined by how well the individual performed on the task and by examining the collected data. Subjects who start to focus on a target in fewer than 100 s are considered high-focused, and those who take more than 100 s are referred to as low-focused. The gamma, beta, alpha, and theta EEG rhythms are classified using multi-stage discrete wavelet transform for the high-focused and low-focused subjects. Then, eight statistical features are computed for the theta, alpha, beta, and gamma rhythms for the high-focused and low-focused subjects. Finally, these features train the proposed model with a 55% training and 45% testing ratio. The K-Nearest Neighbour (KNN), a machine learning classifier, is applied to classify these features. The research findings are (a) that the KNN classifier attained the best f1-score of 88.88% for theta-EEG rhythm, (b) additionally, the KNN classifier got 85.71% f1-score with alpha-EEG rhythm, 66.66% f1-score with beta, and gamma EEG rhythms, and 53.33% f1-score with the combination of all the EEG rhythms (theta, alpha, beta, and gamma). This research concludes that the theta-EEG rhythm is highly relevant in identifying the human "attentive state" compared to other EEG rhythms.
Keywords: Alpha-EEG rhythm; Beta-EEG rhythm; Gamma-EEG rhythm; Humans sustained attention; K-nearest neighbour (KNN) classifier; Multi-stage discrete wavelet transform (MDWT); Theta-EEG rhythm.
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