AI/DL/ML for electroencephalogram (EEG) signals


Electroencephalography (EEG) is a well-known modality in the neuroscience and widely used in identifying and classifying neurological disorders. This project investigates on how EEG data can be used for various applications:

Knowledge distilled transfer learning (KDTL) framework [1] for analysing the EEG-based spectrograms 


[1] S. Singh, H. Jadli, R. P. Priya, V. B. S. Prasath. KDTL: Knowledge distilled transfer learning framework for diagnosing mental disorders using EEG spectrograms. Neural Computing and Applications, 2024. doi:TBA

[2] P. Y. Preema, J. Chandra, V. A. Immanuel, B. Iyer, V. B. S. Prasath. Interpretable machine learning models for EEG signals for identification of emotional intelligence. Submitted, 2024.

[3] A. Shukla, G. Chettiar, B. Iyer, V. Vijayarajan, V. B. S. Prasath. Computational techniques for EEG-based Parkinson disease detection and classification - A review. Submitted, 2024.