Emotion Recognition using Machine Learning Models on Electroencephalogram (EEG) Data

Analyzing Electroencephalogram (EEG) data using cutting-edge machine learning techniques to comprehend emotional states. EEGs are non-invasive and have been shown to illuminate the complex neural mechanisms at play during affective experiences in the human brain. Everything is addressed in detail, from data collection and organization to feature extraction for model training and evaluation. It has the potential to increase the accuracy of diagnosing mental health disorders and has additional applications in psychology, human-computer interaction, and healthcare. The proposed machine learning algorithms and refined data processing strategies are being investigated to improve the accuracy and applicability of EEG-based emotion identification. This is the case despite the challenges posed by factors such as the signal's complexity, individual variation, and the need for robust feature extraction. Due to the interdisciplinary nature of the discipline and the ongoing collaboration of its practitioners, EEG analysis has the potential to shed light on underexplored aspects of the human emotional experience.