Emotion recognition from EEG signals using machine learning model

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The objective of this work is to identify emotion from EEG signals, that represent the brain activity of individuals. With the rapid advancement of machine learning algorithms and numerous real-world applications of brain-computer interface for regular people, emotion categorization from EEG data has recently gained a lot of attention. Researchers previously have little knowledge of the specific interactions between distinct EEG characteristics and various emotional states. Using EEG-based emotion recognition, the computer may be able to understand the emotional state of the user. This work is executed with DEAP dataset with 32 channels for EEG recording, it achieves a better classification accuracy with the random forest machine learning (ML) algorithm. In the subject-specific experiment an average best accuracy of 91.26% and in the subject-dependent experiment, the best accuracy of 78.5% is obtained for the random forest classifier. The proposed method is superior to other studies when compared to those using a four-class problem because those have only achieved the best accuracy of 71.43%.