Biaxialformer: Leveraging Channel Independence and Inter-Channel Correlations in EEG Signal Decoding for Predicting Neurological Outcomes

--Accurate decoding of EEG signals requires comprehensive modeling of both temporal dynamics within individual channels and spatial dependencies across channels. While Transformer-based models utilizing channel-independence (CI) strategies have demonstrated strong performance in various time series tasks, they often overlook the inter-channel correlations that are critical in multivariate EEG signals. This omission can lead to information degradation and reduced prediction accuracy, particularly in complex tasks such as neurological outcome prediction. T o address these challenges, we propose Biaxialformer, characterized by a meticulously engineered two-stage attention-based framework. By employing joint learning of positional encodings, Biaxialformer preserves both temporal and spatial relationships in EEG data, mitigating the inter-channel correlation forgetting problem common in traditional CI models. T o enhance spatial feature extraction, we leverage bipolar EEG signals, which capture inter-hemispheric brain interactions, a critical but often overlooked aspect in EEG analysis. Our study broadens the use of Transformer-based models by addressing the challenge of predicting neurological outcomes in comatose patients. Impact Statement --Decisions about continued treatment for comatose patients hinge on uncertain predictions of brain recovery, leaving families and clinicians in a difficult position. This work delivers a reliable AI-based forecast of recovery chances by analyzing routine EEGs, consistently across multiple hospitals. This clarity can guide doctors toward personalized treatment plans, reduce the performance of invasive or costly procedures with little benefit, and give families timely, trustworthy information when weighing care options. This work was supported in part by the Health South East Authority in Norway, Helse Sør-Øst RHF (HSØ: New Realtime Decision Support during Blood Loss using Machine Learning on Vital Signs) under Grant No. 19/00264-202, and Prosjektnummer 2020079.