Prediction of brain-computer interface aptitude from individual brain structure.

S Halder,A Kübler,B Varkuti,N Birbaumer,W Rosenstiel,R Sitaram,M Bogdan

doi:10.3389/fnhum.2013.00105

Abstract

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary.Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance.Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error).Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance.Significance: This confirms that structural brain traits contribute to individual performance in BCI use.

Highlights

Brain-computer interface (BCI) provide a non-muscular control channel that can be used for communication, device control, or rehabilitation
The structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance
This confirms that structural brain traits contribute to individual performance in BCI use

Summary

Introduction

Brain-computer interface (BCI) provide a non-muscular control channel that can be used for communication, device control, or rehabilitation. Most non-invasive BCI rely on control signals that are extracted from components of the electroencephalogram (EEG), first described by Berger (1929), that can be voluntarily modulated. One of the earliest control signals used successfully for communication with severely paralyzed amyotrophic lateral sclerosis (ALS) patients were slow cortical potential (SCP) (Birbaumer et al, 1990, 1999). P300 BCI have the advantage of requiring almost no user training. Steady state-evoked potential (SSEP) share this advantage and have been successfully used for BCI (Regan, 1977; Middendorf et al, 2000; Allison et al, 2008). Novel approach utilize the principles of semantic classical conditioning to establish a communication channel (Furdea et al, 2012; De Massari et al, 2013; Ruf et al, 2013)

Methods

Results

Discussion

Conclusion