EEG workload estimation for simultaneous task using deep learning algorithm

Mental workload plays a vital role in cognitive impairment refers to a person’s trouble of remembering, receiving new information, learning new things, concentrating, or making decisions that affect seriously in their everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was discussed with 45 subjects for multitasking mental workload estimation using an open access preprocessed EEG dataset. Discrete wavelet transforms (DWT) was used for feature extraction and selection. Scalogram formation was performed for data image conversion form from extracted data. AlexNet classification algorithm was used to classify dataset for low and high workload conditions including some other CNN models to show the comparative study of them. The comparative studies of the used classifier’s accuracy along with other performance parameters with the literature expresses the validation for the study which crossed state-of-the art methodologies in the literature by 77.78 percent.