Advanced EEG Signal Based Min to Mean Algorithm Approach For Human Emotion Taxonomy And Mental State Analysis

Main Article Content

Nisha Vishnnupant Kimmatkar
B. Vijaya Babu

Abstract

With electroencephalography (EEG) brain waves alone, it is full-scale phenomena in the field of computer-brain interface DNN, CNN, and SVM have improved detection and prediction accuracy in a number of researches during the last several years. But when it comes to recognizing global reliance, both deep learning and SVM have obvious limits. Pre-processing, extraction capabilities, and network design are the most common techniques used in deep learning models today, yet they are still unable to produce reliable results in noisy and sparse datasets. Any dataset, no matter how little or large, may suffer from poor SVM performance due to overlapping target instructions and boundaries. There are many different sorts of emotions that may be classified using the particular approach employed in this research. In order to get a whole picture of a person's mental state, it is best to use a "Min of mean” proposed technique. After comparison to the referential mean, a feeling is divided into one of four emotional quadrants. The MIN Max range is used to further split the emotion into 12 subcategories based on the amount of arousal. The proposed set of rules performed better than existing methods. Research on multi-class emotion reputation has shown that, compared to more recent studies, the proposed technique may be rather strong. It is possible to analyze a person's mental health by using the emotional spectrum, which has an accuracy rate of above 90%.

Article Details

How to Cite
Kimmatkar, N. V. ., and B. V. . Babu. “Advanced EEG Signal Based Min to Mean Algorithm Approach For Human Emotion Taxonomy And Mental State Analysis”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 10, Oct. 2022, pp. 119-30, doi:10.17762/ijritcc.v10i10.5741.
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