EEG workload estimation for simultaneous task using deep learning algorithm

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