EEG miniaturization limits for stimulus decoding with EEG sensor networks. (4th October 2021)

• Record Type: Journal Article
• Authors: Mundanad Narayanan, Abhijith; Zink, Rob; Bertrand, Alexander
• Is Part Of: Journal of neural engineering. Volume 18:Number 5(2021)
• Publication Date: 2021-10-04
• Subjects: EEG -- auditory attention decoding -- miniaturization -- neural decoding -- neural signal processing; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac2629
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Unobtrusive electroencephalography (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.