Abstract
Sensorimotor rhythms-based Brain–Computer Interfaces (BCIs) have successfully been employed to address upper limb motor rehabilitation after stroke. In this context, becomes crucial the choice of features that would enable an appropriate electroencephalographic (EEG) sensorimotor activation/engagement underlying the favourable motor recovery. Here, we present a novel feature selection algorithm (GUIDER) designed and implemented to integrate specific requirements related to neurophysiological knowledge and rehabilitative principles. The GUIDER algorithm was tested on an EEG dataset collected from 13 subacute stroke participants. The comparison between the automatic feature selection procedure by means of GUIDER algorithm and the manual feature selection executed by an expert neurophysiologist returned similar performance in terms of both feature selection and classification. Our preliminary findings suggest that the choices of experienced neurophysiologists could be reproducible by an automatic approach. The proposed automatic algorithm could be apt to support the professional end-users not expert in BCI such as therapist/clinicians and, to ultimately foster a wider employment of the BCI-based rehabilitation after stroke.
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Data Availability
The datasets analysed during the current study are not publicly available because complete anonymisation cannot be fulfilled. Data will be made available on reasonable request.
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Funding
This work was supported by the Italian Ministry of Health (Ricerca Corrente IRCCS Fondazione Santa Lucia, Grant Numbers RF-2018-12365210, RF-2019-12369396, GR-2018-12365874) and by Sapienza University of Rome, Progetti di Ateneo 2020 (RM120172B8899B8C).
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Colamarino, E., Pichiorri, F., Toppi, J. et al. Automatic Selection of Control Features for Electroencephalography-Based Brain–Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm. Brain Topogr 35, 182–190 (2022). https://doi.org/10.1007/s10548-021-00883-9
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DOI: https://doi.org/10.1007/s10548-021-00883-9