EEG miniaturization limits for stimulus decoding with EEG sensor networks.
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| Název: | EEG miniaturization limits for stimulus decoding with EEG sensor networks. |
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| Autoři: | Mundanad Narayanan A; KU Leuven, Dept. of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.; Leuven.AI-KU Leuven institute for AI, B-3000 Leuven, Belgium., Zink R; KU Leuven, Dept. of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Kasteelpark Arenberg 10, B-3001 Leuven, Belgium., Bertrand A; KU Leuven, Dept. of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics (STADIUS), Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.; Leuven.AI-KU Leuven institute for AI, B-3000 Leuven, Belgium. |
| Zdroj: | Journal of neural engineering [J Neural Eng] 2021 Oct 04; Vol. 18 (5). Date of Electronic Publication: 2021 Oct 04. |
| Způsob vydávání: | Journal Article; Research Support, Non-U.S. Gov't |
| Jazyk: | English |
| Informace o časopise: | Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004- |
| Výrazy ze slovníku MeSH: | Attention* , Electroencephalography*, Electrodes ; Miniaturization ; Scalp |
| Abstrakt: | 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. (© 2021 IOP Publishing Ltd.) |
| Contributed Indexing: | Keywords: EEG; auditory attention decoding; miniaturization; neural decoding; neural signal processing |
| Entry Date(s): | Date Created: 20210913 Date Completed: 20211021 Latest Revision: 20211021 |
| Update Code: | 20250114 |
| DOI: | 10.1088/1741-2552/ac2629 |
| PMID: | 34517358 |
| Databáze: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstrakt: | 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.<br /> (© 2021 IOP Publishing Ltd.) |
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| ISSN: | 1741-2552 |
| DOI: | 10.1088/1741-2552/ac2629 |