5 papers
A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
Kaidong Feng, Zhu Sun, Roy Ka-Wei Lee +3
Smartphone sensing offers an unobtrusive and scalable way to track daily behaviors linked to mental health, capturing changes in sleep, mobility, and phone use that often precede s…
From Muscle to Text with MyoText: sEMG to Text via Finger Classification and Transformer-Based Decoding
Meghna Roy Chowdhury, Shreyas Sen, Yi Ding
Surface electromyography (sEMG) provides a direct neural interface for decoding muscle activity and offers a promising foundation for keyboard-free text input in wearable and mixed…
SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks
Meghna Roy Chowdhury, Yi Ding, Shreyas Sen
Electroencephalography (EEG) plays a crucial role in brain-computer interfaces (BCIs) and neurological diagnostics, but its real-world deployment faces challenges due to noise arti…
Predicting and Understanding College Student Mental Health with Interpretable Machine Learning
Meghna Roy Chowdhury, Wei Xuan, Shreyas Sen +2
Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental he…
Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors
Wei Xuan, Meghna Roy Chowdhury, Yi Ding +1
The global mental health crisis is a pressing concern, with college students particularly vulnerable to rising mental health disorders. The widespread use of smartphones among youn…