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Prediction of attention and Short-Term Memory loss by EEG Workload Estimation

Journal of Biosciences and Medicines 2023年第4期11卷 304-318页

作者： Md. Ariful Islam Ajay Krishno Sarkar Md. Imran Hossain Md. Tofail Ahmed A. H. M. Iftekharul Ferdous Department of Electrical and Electronic Engineering Rajshshi University of Engineering and Technology Rajshshi Bangladesh Department of Electrical Electronic and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Information and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Electrical and Electronic Engineering Pabna University of Science and Technology Pabna Bangladesh

Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.

关键词： attention loss Cognitive Impairment EEG Feature Selection SIMKAP Short Term Memory loss Machine Learning Workload

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