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

Alex J Horowitz1,2, Christoph Guger3, Milena Korostenskaja1,4,5,6

  • 1Functional Brain Mapping and Brain Computer Interface Lab, Neuroscience Institute, AdventHealth Orlando, Orlando, FL, USA.

HCA Healthcare Journal of Medicine
|July 10, 2023
PubMed
Summary
This summary is machine-generated.

Optimizing external variables is key to improving sensorimotor rhythm-based brain-computer interface (SMR-BCI) performance for everyday use. This review examines factors like feedback and setup to enhance SMR-BCI effectiveness.

Keywords:
BCI accuracyBCI literacyBCI optimizationBCI performanceEEG artifactsEEG waveformsP300 responseadoption ratesauditory feedbackbrain-computer interfaces (BCIs)classification accuracydistractorsdrone controldry electrodeselectrocorticography (ECoG)electroencephalography (EEG)event-related potentialsexoskeletonexternal factorsexternal variableshaptic feedbackinformation transfer rate (ITR)internal factorsinternal variablesmagnetoencephalography (MEG)motor imagerymotor imagery trainingmultimodal feedbackneurophysiologyneuroprostheticsodd-ball paradigmrepetitive transcranial magnetic stimulation (rTMS)sensorimotor rhythm (SMR)sensory feedbacksteady-state somatosensory evoked potential (SSSEP)steady-state visually evoked potentials (SSVEPs)transcranial magnetic stimulation (TMS)vibrotactile feedbackvirtual reality (VR)visual feedback

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Brain-Computer Interface (BCI) Technology

Background:

  • Sensorimotor rhythm-based brain-computer interfaces (SMR-BCIs) translate motor imagery signals for machine control, bypassing the central nervous system.
  • Optimizing SMR-BCI performance is crucial for their transition from laboratory settings to everyday applications for both healthy and disabled individuals.

Purpose of the Study:

  • To critically review and summarize the existing knowledge on how external variables influence SMR-BCI performance.
  • To identify key external factors that can be manipulated to enhance SMR-BCI effectiveness.

Main Methods:

  • Comprehensive literature review and critical evaluation of studies investigating external variables affecting SMR-BCI performance.
  • Categorization of external variables into user-independent factors originating beyond the individual.

Main Results:

  • External variables such as BCI type, distractors, training protocols, and feedback modalities (visual, auditory, virtual reality, haptic) significantly impact SMR-BCI performance.
  • Proper electroencephalography (EEG) system assembly, electrode placement, and minimizing recording artifacts are critical for optimal SMR-BCI function.

Conclusions:

  • Understanding and optimizing external variables are essential for maximizing SMR-BCI performance and enabling widespread adoption.
  • Future research should continue to explore the nuanced effects of these external factors to further advance SMR-BCI technology.