A Novel Efficient AI-Based EEG Workload Assessment System Using ANN-DL Algorithm

Abstract

Individual’s mental and physical health, as well as their performance, is affected by excessive mental workload. The mental effort of operators doing vital activities must be monitored. EEG can collect electrical signals produced by neural structures in the brain and provide information about an operator’s mental state. It can be deduced that the power distribution of these transmissions in various frequency bands has changed. The mental workload has been assessed using this method. Because of the poor signal-to-noise ratio, these noisy signals necessitate extensive filtering and preprocessing. Several factors are at play. The accuracy of EEG-based workload is influenced by factors like window size, filter cutoff, and so on assessment. On an open-source workload dataset, the performance of a workload assessment pipeline is in relation to these parameters in this negotiation. Furthermore, data gathered from individual lobes are used to analyze workload assessment performance. Instead of utilizing the entire brain, multiple frequency bands were used instead of the whole range of frequencies. Finally, for three-level workload classification, we compare the performance of a number of classifiers. We approved our calculations to ensemble ANN calculation with neighborhood data given by EEG information base gathered during the execution of human errands. The deep learning calculation had the greatest precision contrasted with machine learning classifiers. By executing our calculation, we got the greatest precision of 78–80%. From this, we can say that ANN algorithm will be best suitable for calculating human brain activities.