Abstract: Time–frequency (TF) analysis (TFA) is a valuable tool for capturing nonstationary features in time-varying signals. However, for strongly amplitude-modulated and frequency-modulated (AM–FM) ...

Emotion recognition from EEG signals has been one of the most promising areas due to its potential in enhancing human–computer interaction, especially in adaptive systems. This paper proposes a novel ...

Abstract: Feature extraction for fault signals is critical and difficult in all kinds of fault detection schemes. A novel simple and effective method of faulty feeder detection in resonant grounding ...

This project presents a comprehensive analysis of Photoplethysmography (PPG) signals and Pulse Oximetry principles for non-invasive monitoring of heart rate and blood oxygen saturation (SpO₂). The ...

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The intersection of machine learning, biosensing technologies, and neurological behavior analysis presents fertile ground for groundbreaking research and innovation. This research topic aims to bridge ...