The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Stephanie Murray (University of Washington Bothell, University of Hawaii at Manoa), Erika Parsons (University of Washington Bothell)
Supervisor:
Abstract: Electroencephalography (EEG) is widely used in brain–computer interfaces, but movement-related signals are weak, variable, and often buried in noise. Classical pipelines, such as Random Forests trained on PCA+CSP features, work fairly well but can miss cross-channel patterns. Quantum machine learning offers a different approach by embedding features in high-dimensional Hilbert spaces. In this study, we built a 10-qubit variational quantum classifier (VQC) in Qiskit and compared it with a tuned Random Forest baseline using a curated subset of the PhysioNet Motor Movement dataset. Each EEG window was compressed to 10 dimensions using PCA and CSP. Over 40 repeated simulations on GPU backends, the VQC reached stronger best-case performance (macro-F1 up to 0.95 versus 0.70 for Random Forest) and much higher recall on movement detection, albeit with greater variance. These results point to the potential of compact quantum classifiers for EEG and the open challenge of variance.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF