Brain-Informed Speech Separation for Cochlear Implants

Tom Gajecki; Jonas Althoff; Waldo Nogueira

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arXiv · 2601.22260

Brain-Informed Speech Separation for Cochlear Implants

Tom Gajecki·Jonas Althoff·Waldo Nogueira

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Abstract

We propose a brain-informed speech separation method for cochlear implants (CIs) that uses electroencephalography (EEG)-derived attention cues to guide enhancement toward the attended speaker. An attention-guided network fuses audio mixtures with EEG features through a lightweight fusion layer, producing attended-source electrodograms for CI stimulation while resolving the label-permutation ambiguity of audio-only separators. Robustness to degraded attention cues is improved with a mixed curriculum that varies cue quality during training, yielding stable gains even when EEG-speech correlation is moderate. In multi-talker conditions, the model achieves higher signal-to-interference ratio improvements than an audio-only electrodogram baseline while remaining slightly smaller (167k vs. 171k parameters). With 2 ms algorithmic latency and comparable cost, the approach highlights the promise of coupling auditory and neural cues for cognitively adaptive CI processing.

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BibTeX RIS

Tom Gajecki, Jonas Althoff, Waldo Nogueira. 2026-01-29. Brain-Informed Speech Separation for Cochlear Implants. https://arxiv.org/abs/2601.22260

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