Advances in Artificial Intelligence

Kunal thakur

Abstract


Affective computing research is expanding quickly, and new applications are being created on a regular basis. They adjust their interfaces or provide additional functions based on information regarding users' affective/mental states. Face activity, voice, text physiology, and other user inputs are utilised to influence recognition modules, which are developed as classification algorithms. Due to a lack of a defined theoretical framework, brain EEG data have seldom been employed to develop such classifiers. An evaluation of three different classification techniques and their adaptive variations of a 10-class emotion recognition experiment is presented here. Our findings suggest that affect recognition from EEG signals is possible, and that an adaptive algorithm improves classification task performance.

References


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