Minimizing Biosignal Recording Sites for Noninvasive Hybrid Brain/Neural Control

Noninvasive brain/neural controlled robots are pro-mising tools to improve autonomy and quality of life in severe paralysis, but require biosignal recordings, such as electroencephalography (EEG) and electrooculography (EOG), from various sites distributed over the user's head. This limits the...

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Veröffentlicht in:IEEE systems journal 2021-06, Vol.15 (2), p.1540-1546
Hauptverfasser: Cavallo, Alessia, Roth, Vincent, Haslacher, David, Nann, Marius, Soekadar, Surjo R.
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Sprache:eng
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Zusammenfassung:Noninvasive brain/neural controlled robots are pro-mising tools to improve autonomy and quality of life in severe paralysis, but require biosignal recordings, such as electroencephalography (EEG) and electrooculography (EOG), from various sites distributed over the user's head. This limits the applicability and practicality of noninvasive brain/neural robot control on an everyday basis. It would thus be very desirable to minimize the number of necessary recording sites paving the way for miniaturized, headset-like EEG/EOG systems that users with hemiplegia can mount by themselves. Here, we introduce a novel EEG/EOG brain/neural control strategy using only scalp electrodes placed near cortical sensorimotor areas. The strategy was tested across 16 healthy volunteers who engaged in an EEG/EOG brain/neural control task. Classification accuracies were compared using scalp electrodes only versus the conventional electrode placements across the scalp and face. To evaluate whether cranial muscle artifacts impede classification accuracy, participants were asked to clench their teeth during the task. We found that brain/neural classification accuracy was comparable and that clenching had no impact on classification accuracy across both conditions. Our results suggest that the proposed new strategy allows for reliable EEG/EOG-based brain/neural control, a critical prerequisite to broaden the use of noninvasive brain/neural assistive and rehabilitative technologies.
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2020.3021751