The neural correlates of BCI performance variations in ALS: a pilot study

Abstract

Event Abstract Back to Event The neural correlates of BCI performance variations in ALS: a pilot study Moritz Grosse-Wentrup1* 1 Max Planck Institute for Intelligent Systems, Empirical Inference, Germany Brain-Computer Interfaces (BCIs) hold the promise of enabling completely locked-in subjects, e.g., those in late stages of amyotrophic lateral sclerosis (ALS), to communicate by pure thought. To date, this promise has not been fulfilled. While healthy subjects and subjects in early to middle stages of ALS can learn how to operate a non-invasive BCI (Grosse-Wentrup et al., IEEE Transactions on Biomedical Engineering 56(4), 2009; Kübler et al., Neurology (64), 2005), no successful communication with a completely locked-in patient has been reported in literature. We believe that in order to enable completely locked-in patients to operate a BCI, we first need to understand the neuro-physiological causes of good and bad BCI performance. These insights might then be used induce a state-of-mind that is beneficial for operating a BCI, e.g., by neuro-feedback or electrical stimulation. In a series of recent studies, we have investigated the neuro-physiological causes of performance variations in healthy subjects operating a binary motor-imagery BCI. We found that gamma-range oscillations (between 55-85 Hz) modulate the sensorimotor-rhythm (Grosse-Wentrup et al., NeuroImage 56(2), 2011), resulting in group-average decoding differences of up to 22.2% depending on the state of fronto-parietal gamma-power (Grosse-Wentrup, 5th IEEE/EMBS International Conference on Neural Engineering, 2011). Here, we report results of a pilot study that reproduces this effect in one ALS patient. We recorded a 128-channel EEG in an ALS patient performing motor-imagery of the left or right hand. The patient was artificially ventilated, with residual control of the right wrist. We trained a support vector machine in classifying left- vs. right hand motor imagery, based on bandpower features over sensorimotor-areas. This resulted in a cross-validated accuracy of 59.2%, which is sufficient to reject the null-hypothesis of chance-level performance with p = 0.0178 (N=120). To identify brain areas related to BCI-performance, we first performed an Independent Component Analysis, manually rejected independent components (ICs) representing artifactual muscle activity, and then correlated gamma-power of the remaining ICs with a trial-wise measure of motor-imagery performance (cf. Grosse-Wentrup et al. (2011) for a description of this performance measure). In this way, we identified two ICs whose gamma-power exhibited a strong negative correlation with BCI-performance (rho = -0.3161/-0.2888, p = 0.0180/0.0258 (corrected for multiple comparisons), N = 120). A subsequent source localization procedure identified the origin of these gamma-range oscillations in the right superior parietal lobule, consistent with our findings in healthy subjects. While it remains to be seen how consistently this effect can be reproduced in ALS, its investigation over the course of ALS progression may provide novel insights into the current failure of completely locked-in patients to communicate by means of a BCI. Keywords: Amyotrophic Lateral Sclerosis, Motor Imagery, Neurotechnology and brain-machine interface Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Poster Topic: neurotechnology and brain-machine interface (please use "neurotechnology and brain-machine interface" as keyword) Citation: Grosse-Wentrup M (2011). The neural correlates of BCI performance variations in ALS: a pilot study. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00117 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Dr. Moritz Grosse-Wentrup, Max Planck Institute for Intelligent Systems, Empirical Inference, Tübingen, 72076, Germany, moritz.grosse-wentrup@tuebingen.mpg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Moritz Grosse-Wentrup Google Moritz Grosse-Wentrup Google Scholar Moritz Grosse-Wentrup PubMed Moritz Grosse-Wentrup Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.