Chirplet Analysis of Brain State During Snow and Cold Exposure
IEEE 39th International Symposium on Computer-Based Medical Systems (CBMS) 2026
Limassol, Cyprus, June 2026 · Published; full paper on IEEE Xplore
Gyro-cleaned AF7 band powers across a snow session: synchronized gyroscope data flags the movement artifacts before the chirplet analysis.
EEG is usually recorded in still, controlled rooms. Portable headbands let us record it while people move through snow and cold, but motion artifacts come with it. This short paper pairs wearable EEG with synchronized gyroscope data to suppress those artifacts, then applies the chirplet transform, Mann’s generalization of the wavelet, to bring out the rapid, non-stationary frequency changes that appear during movement on snow-covered terrain.
Abstract
Electroencephalography (EEG) is traditionally studied under highly controlled laboratory conditions in order to minimize environmental noise and motion artifacts. While this approach enables precise measurements of neural activity, it is poorly representative of real-world physical and environmental stressors. The advent of portable EEG devices has enabled neural data collection during physical activity, creating new opportunities to study brain–body–environment interactions outside the laboratory. In this work, we investigate EEG dynamics during exposure to snow and cold environments using wearable EEG, inertial sensors, and chirplet-based time–frequency analysis. By leveraging synchronized gyroscope measurements to suppress motion artifacts, we apply the chirplet transform to highlight rapid, non-stationary frequency dynamics that arise during movement in snow-covered terrain. This approach extends conventional Fourier and wavelet methods and provides a framework for analyzing mobile EEG under highly dynamic environmental conditions.
Authors
Ali Sher, Alexander Vicol, Xiaoming Chen, Nareen Kouyoumdjian, Steve Mann
Links
- Paper on IEEE Xplore (DOI 10.1109/CBMS69103.2026.00191)
- Related project: Cold Plunge Mini-Golf
- Related project: State-of-Float