Why the study?
The potential of using EEG cognitive function alongside machine learning algorithms for activity monitoring and emotional analysis was mainly untested.
A novel Optimized Artificial Neural Network approach demonstrated high accuracy (97%) for emotion recognition using EEG signals.
Before earlier, the primary reason why academics were interested in human observations from books was so they could quickly identify human cognition. Conversely, these signals from linguistic processing can also help with machine learning (ML) algorithms. The potential of using EEG cognitive function for this function is mainly untested at this time. This paper presents the first comprehensive assessment of the capability of EEG brainwave activity data for monitoring and predicting emotional analysis by analysis of recorded Electroencephalography (EEG) signals using a novel Optimized Artificial Neural network (OANN) approach. With ANN and ANO working together, we can pick the best possible values for weights. To further improve activity prediction, the proposed method imports EEG data from the Bern-Barcelona EEG collection. With an accuracy rate of 97% for OANN for emotion recognition challenges, experimental evidence confirms the efficacy of these networks in terms of effectiveness metrics.
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Kalaivani et al. (2023) studied this question.
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