Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment

Josef Faller; Reinhold Scherer; Elisabeth V C Friedrich; Ursula Costa; Eloy Opisso; Josep Medina; Gernot R Müller-Putz

doi:10.3389/fnins.2014.00320

Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment

Front Neurosci. 2014 Oct 14:8:320. doi: 10.3389/fnins.2014.00320. eCollection 2014.

Authors

Josef Faller¹, Reinhold Scherer¹, Elisabeth V C Friedrich², Ursula Costa³, Eloy Opisso⁴, Josep Medina⁴, Gernot R Müller-Putz¹

Affiliations

¹ Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology Graz, Austria.
² Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of Technology Graz, Austria ; Cognitive Neuroscience Lab, University of California, San Diego San Diego, CA, USA.
³ Department of Functional Rehabilitation, Guttmann Institute, Neurorehabilitation University Institute Affiliated with the UAB Barcelona, Spain.
⁴ Department of Functional Rehabilitation, Guttmann Institute, Neurorehabilitation University Institute Affiliated with the UAB Barcelona, Spain ; Health Science Research Institute, "Germans Trias i Pujol" Foundation Barcelona, Spain.

Abstract

Individuals with severe motor impairment can use event-related desynchronization (ERD) based BCIs as assistive technology. Auto-calibrating and adaptive ERD-based BCIs that users control with motor imagery tasks ("SMR-AdBCI") have proven effective for healthy users. We aim to find an improved configuration of such an adaptive ERD-based BCI for individuals with severe motor impairment as a result of spinal cord injury (SCI) or stroke. We hypothesized that an adaptive ERD-based BCI, that automatically selects a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration ("Auto-AdBCI") could allow for higher control performance than a conventional SMR-AdBCI. To answer this question we performed offline analyses on two sessions (21 data sets total) of cue-guided, five-class electroencephalography (EEG) data recorded from individuals with SCI or stroke. On data from the twelve individuals in Session 1, we first identified three bipolar derivations for the SMR-AdBCI. In a similar way, we determined three bipolar derivations and four mental tasks for the Auto-AdBCI. We then simulated both, the SMR-AdBCI and the Auto-AdBCI configuration on the unseen data from the nine participants in Session 2 and compared the results. On the unseen data of Session 2 from individuals with SCI or stroke, we found that automatically selecting a user specific class-combination from motor-related and non motor-related mental tasks during initial auto-calibration (Auto-AdBCI) significantly (p < 0.01) improved classification performance compared to an adaptive ERD-based BCI that only used motor imagery tasks (SMR-AdBCI; average accuracy of 75.7 vs. 66.3%).

Keywords: adaptive brain-computer interface (BCI); assistive technology; electroencephalography (EEG); event-related desynchronization (ERD); mental tasks; spinal cord injury (SCI); stroke.