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Scholarly Commons > HCA Healthcare Journal > Vol. 2 > Iss. 3 (2021)

 

Keywords

adoption rates; auditory feedback; brain-computer interfaces (BCIs); BCI accuracy; BCI literacy; BCI optimization; BCI performance; classification accuracy; distractors; dry electrodes; drone control; electrocorticography (ECoG); electroencephalography (EEG); EEG artifacts; EEG waveforms; external factors; external variables; event-related potentials; exoskeleton; haptic feedback; information transfer rate (ITR); internal factors; internal variables; magnetoencephalography (MEG); motor imagery; neurophysiology; odd-ball paradigm; P300 response; sensorimotor rhythm (SMR); sensory feedback; steady-state somatosensory evoked potential (SSSEP); steady-state visually evoked potentials (SSVEPs); motor imagery training; neuroprosthetics; transcranial magnetic stimulation (TMS); repetitive transcranial magnetic stimulation (rTMS); multimodal feedback; virtual reality (VR); vibrotactile feedback; visual feedback

Disciplines

Bioelectrical and Neuroengineering | Engineering | Medical Biotechnology | Medicine and Health Sciences | Neurology | Neurosciences

Abstract

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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