Citation :

Varsha K. Harpale, Amit K. Bairagi, Sharad T. Jadhav, Archana R. Date, Mrinai Dhanvijay, "Analyzing Brain Waves and Mapping Musically Stimulated EEG Signals for Emotion and Depressive Disorder Classification," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 5, pp. 89-103, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I5P107

Abstract

Neurological disorders have been highly prevalent problems observed in humans in recent eras. Majorly, the youth of India and the world are suffering from anxiety, stress, and depression. A significant mental imbalance observed is due to current lifestyle and economic competition; thus, maintaining good mental health is a challenge for human beings. Besides, youth cannot handle this stress, which invokes suicidal thoughts and has an impact on their behavior. Emotion Artificial Intelligence is a model for analyzing emotion. The proposed work uses emotion artificial intelligence to label an emotion as a detection of depression. In the proposed work, the classification of emotions and depression analysis, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Fuzzy Inference System (FIS) classification models have been discussed and implemented. The results are discussed here by comparing the classification model, performance matrix, and accuracy. The classification accuracy observed using the ANFIS model is better than that of the ANN and FIS models.

Keywords

Feature Extraction and Selection, Depressive Disorder Detection, ANFIS Classification, and EEG Analysis.

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