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What External Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?

Overview
Specialty Health Services
Date 2023 Jul 10
PMID 37427002
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Abstract

Description Sensorimotor rhythm-based brain-computer interfaces (SMR-BCIs) are used for the acquisition and translation of motor imagery-related brain signals into machine control commands, bypassing the usual central nervous system output. The selection of optimal external variable configuration can maximize SMR-BCI performance in both healthy and disabled people. This performance is especially important now when the BCI is targeted for everyday use in the environment beyond strictly regulated laboratory settings. In this review article, we summarize and critically evaluate the current body of knowledge pertaining to the effect of the external variables on SMR-BCI performance. When assessing the relationship between SMR-BCI performance and external variables, we broadly characterize them as elements that are less dependent on the BCI user and originate from beyond the user. These elements include such factors as BCI type, distractors, training, visual and auditory feedback, virtual reality and magneto electric feedback, proprioceptive and haptic feedback, carefulness of electroencephalography (EEG) system assembling and positioning of EEG electrodes as well as recording-related artifacts. At the end of this review paper, future developments are proposed regarding the research into the effects of external variables on SMR-BCI performance. We believe that our critical review will be of value for academic BCI scientists and developers and clinical professionals working in the field of BCIs as well as for SMR-BCI users.

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References
1.
Horowitz A, Guger C, Korostenskaja M . What Internal Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?. HCA Healthc J Med. 2023; 2(3):163-179. PMC: 10324829. DOI: 10.36518/2689-0216.1196. View

2.
Monge-Pereira E, Ibanez-Pereda J, Alguacil-Diego I, Serrano J, Spottorno-Rubio M, Molina-Rueda F . Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review. PM R. 2017; 9(9):918-932. DOI: 10.1016/j.pmrj.2017.04.016. View

3.
McCreadie K, Coyle D, Prasad G . Learning to modulate sensorimotor rhythms with stereo auditory feedback for a brain-computer interface. Annu Int Conf IEEE Eng Med Biol Soc. 2013; 2012:6711-4. DOI: 10.1109/EMBC.2012.6347534. View

4.
Brumberg J, Pitt K, Burnison J . A Noninvasive Brain-Computer Interface for Real-Time Speech Synthesis: The Importance of Multimodal Feedback. IEEE Trans Neural Syst Rehabil Eng. 2018; 26(4):874-881. PMC: 5906041. DOI: 10.1109/TNSRE.2018.2808425. View

5.
Zich C, Debener S, Kranczioch C, Bleichner M, Gutberlet I, De Vos M . Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery. Neuroimage. 2015; 114:438-47. DOI: 10.1016/j.neuroimage.2015.04.020. View