A direct comparison of simultaneously recorded scalp, around-ear and in-ear EEG for neural selective auditory attention decoding to speech

Current assistive hearing devices, such as hearing aids and cochlear implants, lack the ability to adapt to the listener's focus of auditory attention, limiting their effectiveness in complex acoustic environments like cocktail party scenarios where multiple conversations occur simultaneously. Neuro-steered hearing devices aim to overcome this limitation by decoding the listener's auditory attention from neural signals, such as electroencephalography (EEG). While many auditory attention decoding (AAD) studies have used high-density scalp EEG, such systems are impractical for daily use as they are bulky and uncomfortable. Therefore, AAD with wearable and unobtrusive EEG systems that are comfortable to wear and can be used for long-term recording are required. Around-ear EEG systems like cEEGrids have shown promise in AAD, but in-ear EEG, recorded via custom earpieces offering superior comfort, remains underexplored. We present a new AAD dataset with simultaneously recorded scalp, around-ear, and in-ear EEG, enabling a direct comparison. Using a classic linear stimulus reconstruction algorithm, a significant performance gap between all three systems exists, with AAD accuracies of 83.4% (scalp EEG), 67.2% (around-ear EEG), and 61.1% (in-ear EEG) on 60s decision windows. These results highlight the trade-off between decoding performance and practical usability. Yet, while the ear-based EEG systems using basic algorithms might currently not yield accurate enough performances for a decision speed-sensitive application in hearing aids, their significant performance suggests potential for attention monitoring on longer timescales. Furthermore, adding an external reference or a few scalp electrodes via greedy forward selection substantially and quickly boosts accuracy by over 10 percent point, especially for in-ear EEG. These findings position in-ear EEG as a promising component in EEG sensor networks for AAD.