TY - JOUR
T1 - Brain-computer interfacing under distraction
T2 - An evaluation study
AU - Brandl, Stephanie
AU - Frølich, Laura
AU - Höhne, Johannes
AU - Muller, Klaus
AU - Samek, Wojciech
PY - 2016/8/31
Y1 - 2016/8/31
N2 - Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this 'simulated' out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.
AB - Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this 'simulated' out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.
KW - brain-computer interfacing
KW - common spatial patterns
KW - nonstationarity
KW - robustness
UR - http://www.scopus.com/inward/record.url?scp=84989809358&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84989809358&partnerID=8YFLogxK
U2 - 10.1088/1741-2560/13/5/056012
DO - 10.1088/1741-2560/13/5/056012
M3 - Article
C2 - 27578310
AN - SCOPUS:84989809358
VL - 13
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
SN - 1741-2560
IS - 5
M1 - 056012
ER -