Bridging AI and Health on Time Series Analysis and Explainability Using the Case Study of EEG Channel Selection Problem

Abstract

Time series (TS) analysis is an active application area for Artificial Intelligence (AI) methods, where the objective is to analyze numeric quantities indexed by time for tasks like classification, forecasting, and abnormality detection. In health, TS manifests as biosignals like the electroencephalogram (EEG), where electrical signals from the brain are analyzed. AI and health communities can tremendously benefit each other in TS, with the former offering advanced analytical methods while the latter provides complex data sets and trust-sensitive use cases. But the communities also need to overcome confusing terminologies, hidden assumptions, and a lack of necessary domain contexts for result evaluation and interpretation. In this paper, we attempt to bridge the gap using the problem of channel selection in EEG. We outline challenges in working with EEG data, demonstrate via two experiments how simple explainable AI (XAI) methods can be quite effective for channel selection, irrespective of the EEG tasks/paradigms, and argue that recent TS trends in AI, like LLMs and XAI methods, can benefit health as well. We hope that this work will bring researchers working on TS problems at the intersection of AI and health, closer to work in AI trustworthiness so that they can better leverage results from their respective areas to overcome common challenges. All code and resources are released on GitHub to help others replicate.