EEG miniaturization limits for stimulus decoding with EEG sensor networks
| Autor: | Alexander Bertrand, Rob Zink, Abhijith Mundanad Narayanan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
0206 medical engineering Biomedical Engineering neural signal processing ComputerApplications_COMPUTERSINOTHERSYSTEMS Context (language use) 02 engineering and technology Electroencephalography auditory attention decoding miniaturization 03 medical and health sciences Cellular and Molecular Neuroscience 0302 clinical medicine Miniaturization medicine Wireless Computer vision Attention EEG Electrodes Scalp medicine.diagnostic_test business.industry 020601 biomedical engineering neural decoding ComputingMethodologies_PATTERNRECOGNITION Node (circuits) Artificial intelligence business Wireless sensor network 030217 neurology & neurosurgery Decoding methods Neural decoding |
| Zdroj: | Journal of neural engineering. 18(5) |
| ISSN: | 1741-2552 |
| Popis: | 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. ispartof: Journal Of Neural Engineering vol:18 issue:5 ispartof: location:England status: published |
| Databáze: | OpenAIRE |
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