Post by Sohan Daniel Singh

I'm pleased to announce that I’ve published my first paper! It's been accepted for the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026). My contribution was on the engineering side during my time at AETHER Singapore University of Technology and Design (SUTD). I built out the data processing pipeline, feature extraction, and the model training and evaluation framework that the analysis ran on. The paper explores which regions of the scalp carry the most useful information for predicting cognitive workload from electroencephalography (EEG) signals via machine learning methods. We found that focussing on the frontal and fronto-central regions outperformed using signals from all regions of the brain. This means that fewer electrodes are needed for workload classification, improving feasibility of data collection and processing. Accurate cognitive workload classification can enable more effective human-computer interfaces, adaptive learning systems, and improved safety in high-stress environments, making this research a crucial step towards real-world applications. I'm grateful for Prannaya Gupta's guidance, and for Jacob Wong, Prannaya, and the team for building on my work. Looking forward to researching more on the applications of technology! You can access the paper here: https://lnkd.in/e5bYS-2F