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Proceedings Of The 7Th Graz Brain-Computer Interface Conference 2017, From Vision To Reality, September 18-22, 2017 Graz University Of Technology, Austria

Brain-machine interfaces (BMI) have largely been demonstrated in laboratory conditions involving, mainly, healthy users. We have recently carried out a series of studies with a substantial number of motor-disabled end-users operating different brain-controlled devices in ecological conditions and without the assistance of BMI experts.

One of the problems of non-invasive BCI applications is the noise and change of EEG recordings that can result in performance degradation. Signal noise and changes can for instance be the result of electrode misplacements, impedances degradation or complete loss of connectivity. Moreover, a more extended use of BCI systems outside research lab by lay operators necessitates the need of a systematic approach to evaluate signal reliability during online BCI operation to notify the operator to take a reaction. In this paper, we propose a method for quantifying how much each channel is deviating from its expected behavior. The proposed method is applied on a motor imagery dataset and the results show the correlation between the accuracy changes of the BCI and the feature deviation value.

Introduction Diagnosis and prediction of recovery in the acute phase of disorders of consciousness (DOC) are critical for subsequent medical decisions. However, reliable assessment remains elusive due to the inability of current validated clinical scales to take into account motor and drive deficits. Recently, the Motor Behaviour Tool (MBT), a novel clinical scale, has been developed to address this caveat (1). In this context, neuroimaging and brain-computer interface (BCI) have also been proposed to improve the diagnosis and prognosis of these patients.(2). Objectives This pilot study aims to investigate the diagnostic and prognostic value of two electroencephalography (EEG)-based paradigms in patients with DOC, i.e., coma, Unresponsive Awareness Syndrome (UWS) or Minimally Conscious State (MCS). As a second step, we will employ them as evidence to further establish the added value of the MBT (i.e., assessment of minimal responses suggesting remaining conscious processing) combined with the Coma Recovery Scale-Revised (CRS-R), a standardized validated scale commonly used to assess consciousness in this population (3). Patients & Methods Acute DOC patients undergo CRS-R and MBT assessment prior to two EEG paradigms. Firstly, a motor attempt EEG-BCI coupled with Functional Electrical Stimulation (FES) is used (4). We hypothesize that replacing the need for overt movements with motor attempt can alleviate the tendency of CRS-R to underestimate the level of awareness in case of cognitive-motor dissociation (CMD) (5). In addition, a second EEG protocol presents patients with FES-tactile (T), auditory (A), and audio-tactile (AT) stimuli both in actionable and non-actionable space. EEG evoked potentials observed in the actionable space are expected to show a non-linear addition of sensory stimuli (i.e., A+T ≠ AT) indicating multisensory integration and the capacity of conscious processing (6). Results Pending elaborate analysis, preliminary findings show (Fig. 1) that BCI accuracy is significantly above chance only for a patient who was diagnosed as UWS by the CRS-R evaluation, but exhibited a motor behavior classified as CMD confirmed by the MBT tool, and not for one in real UWS (same diagnosis based on CRS-R and MBT), implying the presence of the hypothesized relation between motor EEG correlates and awareness (7). Average EEG evoked potentials of 8 patients during the second EEG paradigm highlight a difference between within (solid line) vs. outside (dashed line) the actionable space (Fig. 2), suggesting awareness-dependent modulation. Future analyses will explore correlations of such EEG descriptors with the clinical outcomes. Conclusion EEG correlates extracted from these EEG paradigms are promising tools for diagnosis of DOC and may supplement current clinical scales to help the validation of new tools like the MBT. References J. M. Pignat, E. Mauron, J. Jöhr, C. Gilart De Keranflec'h, D. Van De Ville, M. G. Preti, D. E. Meskaldji, V. Hörnberg, S. Laureys, B. Draganski, R. Frackowiak, K. Diserens, Outcome prediction of consciousness disorders in the acute stage based on a complementary motor behavioural tool, PLOS ONE 11(6), e0156882 (2016). A. M. Owen, M. R. Coleman, M. Boly, M. H. Davis, S. Laureys, J. D. Pickard, Detecting awareness in the vegetative state, Science, 313, 1402 (2006). J. T. Giacino, K. Kalmar, J. Whyte, The JFK Coma Recovery Scale-Revised: measurement characteristics and diagnostic utility, Arch. Phys. Med. Rehabil. 85(12), 2020-9 (2004). T. Corbet, R. Leeb, A. Biasiucci, S. Perdikis, J. del R. Millán et al. BCI-NMES therapy enhances effective connectivity in the damaged hemisphere in stroke patients. 6th International Brain-Computer Interface Meeting, Asilomar, California, USA (2016). N. D. Schiff, Cognitive motor dissociation following severe brain injuries, JAMA Neurol. 72(12), 1413–1415 (2015). J. P. Noel, C. Pfeiffer, O. Blanke, A. Serino, Full body peripersonal space as the space of the bodily self, Cognition 144, 49-57 (2015). A. M . Goldfine, J. D. Victor, M. M. Conte, J. C. Bardin, N. D. Schiff, Determination of awareness in patients with severe brain injury using EEG power spectral analysis, Clin. Neurophysiol. 122(11), 2157-68 (2011).

The non-invasive Brain-Computer Interface (BCI) developed in our lab targets asynchronous operation of devices by monitoring electroencephalographic (EEG) activity and identifying oscillatory patterns that the user can voluntary modulate through the execution of motor imagery (MI) tasks. Successful self-paced interaction under this framework requires the incorporation of an evidence accumulation module to eliminate the uncertainty of single-sample classification and to drive an efficient feedback visualization. In this work, we motivate the need for this additional module, describe its role in a closed-loop MI BCI and present a comparative study of two different frameworks for evidence accumulation.

One of the problems of non-invasive Brain-Computer Interface (BCI) applications is the occurrence of anomalous (unexpected) signals that might degrade BCI performance. This situation might slip the operator’s attention since raw signals are not usually continuously visualized and monitored during BCI-actuated device operation. Anomalous data can for instance be the result of electrode misplacement, degrading impedance or loss of connectivity. Since this problem can develop at run-time, there is a need of a systematic approach to evaluate electrode reliability during online BCI operation. In this paper, we propose two metrics detecting how much each channel is deviating from its expected behavior. This quantifies electrode reliability at run-time which could be embedded into BCI data processing to increase performance. We manifest the effectiveness of these metrics in quantifying signal degradation by conducting three experiments: electrode swap, electrode manipulation and offline artificially degradation of P300 signals.

Proceedings Of The 6Th International Bci Meeting: Bci Past, Present, And Future

One important source of performance degradation in BCIs is bias towards one of the mental classes. Recent literature has focused on the general problem of classification accuracy drop, identifying non-stationarity as the generating factor, thus leading to several classifier adaptation approaches suggested as of today. In this work, we explicitly focus on bias elimination, demonstrating that the problem has two separate components, one related to non-stationarity and another one attributed to the nature of the feature distributions and the assumptions made by the classification methods. We propose a cued recalibration protocol including a supervised adaptation method and a novel framework for unbiased classification with a modified, unbiased Linear Discriminant Analysis classifier. Preliminary results show that our protocol can assist the subject to achieve quickly accurate and unbiased control of the BCI.

Brain-Computer Interfaces (BCIs) are no longer only used by healthy subjects under control conditions in laboratory environments, but by patients controlling applications at their homes, without the BCI experts around. But are the technology and the field mature enough for this? In this work, we want to summarize the experiences gained and the lessons we learned while transferring BCI technologies from the lab to the user's home. These lessons range from pure BCI issues (technical and handling), to common communication problems between different people involved, and lessons encountered while controlling the applications. The points raised are very general and will be faced similarly by other groups, if they move on to bring the BCI technology to the end-user.