Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning. - Sriram Kumar P; Praveen Kumar Govarthan; Nagarajan Ganapathy; Jac Fredo Agastinose Ronickom (MIE, 2023)

Emotion classification using electrocardiogram and machine learning: A study on the effect of windowing techniques. - Praveen Kumar Govarthan; Sriram Kumar P; Nagarajan Ganapathy; Jac Fredo Agastinose Ronickom (Expert Syst. Appl., 2024)

Deep Learning-Based Automated Emotion Recognition Using Multimodal Physiological Signals and Time-Frequency Methods. - Sriram Kumar P; Praveen Kumar Govarthan; Abdul Aleem Shaik Gadda; Nagarajan Ganapathy; Jac Fredo Agastinose Ronickom (IEEE Trans. Instrum. Meas., 2024)

Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-Based Feature Extraction and Machine Learning. - Sriram Kumar P; Jac Fredo Agastinose Ronickom (Int. J. Neural Syst., 2024)

Evaluating the effectiveness of machine learning in identifying the optimal facial electromyography location for emotion detection. - Vinay Kumar Barigala; Swarubini PJ; Sriram Kumar P; Nagarajan Ganapathy; Karthik P. A.; Deepesh Kumar; Jac Fredo Agastinose Ronickom (Biomed. Signal Process. Control., 2025)

Emotion Classification Through Optimal Segments of EDA and Texture Analysis of Time-Encoded Images With Artificial Intelligence. - Sriram Kumar P; Jac Fredo Agastinose Ronickom (IEEE Trans. Instrum. Meas., 2025)