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End-to-End EEG-Based Auditory Attention Decoding for Cochlear Implant Users
Conference proceeding

End-to-End EEG-Based Auditory Attention Decoding for Cochlear Implant Users

Ian Pope, Jusung Ham, Inyong Choi, Yu-Hsiang Wu, Bijaya Adhikari and Octav Chipara
Proceedings (IEEE International Conference on Healthcare Informatics. Online), pp.214-223
06/01/2026
DOI: 10.1109/ICHI69079.2026.00037

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Abstract

Cochlear implant (CI) users continue to experience difficulty understanding speech in complex listening environments. Auditory attention decoding (AAD) from EEG offers a promising pathway toward neurofeedback-based training, but remains challenging in CI users due to limited data, implant-related artifacts, and high inter-subject variability.In this work, we investigate end-to-end convolutional neural network (CNN) models for decoding auditory attention directly from single-trial EEG, without access to the auditory stimulus. We introduce a sequence of task-aligned, EEGNet-based architectural refinements, culminating in a multi-scale, one-dimensional temporal-spatial model tailored to auditory attention dynamics. Across a cohort of 105 CI users, the proposed architectures substantially outperform traditional temporal response function (TRF) baselines, achieving average improvements of 17.30% in accuracy and 28.10% in AUC.Strategies targeting label noise, data scarcity, and inter-subject and inter-trial variability provide complementary gains, but architectural design is the primary driver of performance. These results demonstrate that carefully designed EEG-only CNN architectures can reliably decode auditory attention in CI users, supporting practical neurofeedback systems for real-world listening scenarios.
 Electroencephalography Auditory attention decoding cochlear implant convolutional neural network Convolutional neural networks Decoding eeg hearing loss Labeling Modeling Neurofeedback Printing Speech Streams Training

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