EEG miniaturization limits for stimulus decoding with EEG sensor networks
- 13 September 2021
- journal article
- research article
- Published by IOP Publishing in Journal of Neural Engineering
- Vol. 18 (5), 056042
- https://doi.org/10.1088/1741-2552/ac2629
Abstract
Objective. Unobtrusive EEG monitoring in everyday life requires the availability of highly miniaturized EEG devices (mini-EEGs), which ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. By attaching a multitude of mini-EEGs at relevant positions on the scalp, a wireless 'EEG sensor network' (WESN) can be formed. However, each mini-EEG in the network only has access to its own local electrodes, thereby recording local scalp potentials with short inter-electrode distances. This is unlike using traditional cap-EEG, which by the virtue of re-referencing can measure EEG across arbitrarily large distances on the scalp. We evaluate the implications and limitations of such far-driven miniaturization on neural decoding performance. Approach. We collected 255-channel EEG data in an auditory attention decoding (AAD) task. As opposed to previous studies with a lower channel density, this new high-density dataset allows emulation of mini-EEGs with inter-electrode distances down to 1 cm in order to identify and quantify the lower bound on miniaturization for EEG-based stimulus decoding. Main Results. We demonstrate that the performance remains reasonably stable for inter-electrode distances down to 3 cm, but decreases quickly for shorter distances, if the mini-EEG nodes can be placed at optimal scalp locations and orientations selected by a data-driven algorithm. Significance. The results indicate the potential for the use of mini-EEGs in a WESN context for AAD applications and provide guidance on inter-electrode distances while designing such devices for neuro-steered hearing devices.Funding Information
- Vlaamse regering (Onderzoek- sprogramma Artificiële Intelligentie ()
- Fonds Wetenschappelijk Onderzoek (G0A4918N)
- H2020 European Research Council (802895)
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