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@article{181508,
        author = {G.JAYAGOPI and C.B.SUMATHI},
        title = {Automated Depression Detection via EEG Signal Processing and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4617-4622},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181508},
        abstract = {Depression is increasingly recognized as one of the most widespread mental health disorders affecting individuals today. This paper proposes a reasoning-based model for the detection of depression using Electroencephalography (EEG) signals. EEG data were collected using a portable three-electrode device, pre-processed to eliminate artifacts, and subjected to feature extraction. The processed signals were then analysed using a Linear Pattern Recognition Network (LPRN), a type of machine learning algorithm. Wavelet transform was applied to the EEG signals to capture key frequency components relevant to depressive patterns. Frequency-specific features served as statistical indicators for further analysis. The system performs wavelet transformation on all collected EEG data and utilizes the output of the LPRN to generate statistical scores for classification. System performance was evaluated in terms of accuracy, using a confusion matrix that compares classified output with the training data. Furthermore, based on emotion classification derived from EEG patterns, the system also provides personalized music recommendations corresponding to the detected emotional state.},
        keywords = {Depression detection, EEG signal analysis, Emotion recognition, Machine learning, Wavelet transform, Linear pattern recognition.},
        month = {June},
        }