On the design of EEG-based movement decoders for completely paralyzed stroke patients

Background: Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question. Methods: In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance. We analyze the data of 37 chronic stroke patients who underwent 4 weeks of BMI intervention with different types of association between their brain activity and the proprioceptive feedback. We simulate the pseudo-online performance that a BMI would have under different conditions, varying: (1) the cortical source of activity (i.e., ipsilesional, contralesional, bihemispheric), (2) the type of spatial filter applied, (3) the EEG frequency band, (4) the type of classifier; and also evaluated the use of residual EMG activity to decode the movement intentions. Results: We observed a significant influence of the different BMI designs on the obtained performances. Our results revealed that using bihemispheric beta activity with a common average reference and an adaptive support vector machine led to the best classification results. Furthermore, the decoding results based on brain activity were significantly higher than those based on muscle activity. Conclusions: This paper underscores the relevance of the different parameters used to decode movement, using EEG in severely paralyzed stroke patients. We demonstrated significant differences in performance for the different designs, which supports further research that should elucidate if those approaches leading to higher accuracies also induce higher motor recovery in paralyzed stroke patients.This study was funded by the Baden-Württemberg Stiftung (GRUENS ROB-1), the Deutsche Forschungsgemeinschaft (DFG, Koselleck and Grant SP 1533/2– 1), the Bundesministerium für Bildung und Forschung BMBF: MOTORBIC (FKZ 13GW0053) and AMORSA (FKZ 16SV7754), the fortüne-Program of the University of Tübingen (2422-0-1 and 2452-0-0) and the Basque Government Science Program (EXOTEK: KK 2016/00083)

Classification of different reaching movements from the same limb using EEG

Objective. Brain–computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.This study was funded by the Baden-Württemberg Stiftung (GRUENS), the Deutsche Forschungsgemeinschaft (DFG, Koselleck and SP-1533/2-1), Bundes Ministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053), the fortune-Program of the University of Tübingen (2422-0-0), and AMORSA (FKZ 16SV7754). A Sarasola-Sanz’s work is supported by the La Caixa-DAAD scholarship, and N IrastorzaLanda’s work by the Basque Government and IKERBASQUE, Basque Foundation for Science

Design and effectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients

The motor impairment occurring after a stroke is characterized by pathological muscle activation patterns or synergies. However, while robot-aided myoelectric interfaces have been proposed for stroke rehabilitation, they do not address this issue, which might result in inefficient interventions. Here, we present a novel paradigm that relies on the correction of the pathological muscle activity as a way to elicit rehabilitation, even in patients with complete paralysis. Previous studies demonstrated that there are no substantial inter-limb differences in the muscle synergy organization of healthy individuals. We propose building a subject-specific model of muscle activity from the healthy limb and mirroring it to use it as a learning tool for the patient to reproduce the same healthy myoelectric patterns on the paretic limb during functional task training. Here, we aim at understanding how this myoelectric model, which translates muscle activity into continuous movements of a 7-degree of freedom upper limb exoskeleton, could transfer between sessions, arms and tasks. The experiments with 8 healthy individuals and 2 chronic stroke patients proved the feasibility and effectiveness of such myoelectric interface. We anticipate the proposed method to become an efficient strategy for the correction of maladaptive muscle activity and the rehabilitation of stroke patients.This study was funded by the Baden-Württemberg Stiftung (GRUENS ROB-1), the Deutsche Forschungsgemeinschaft (DFG, Koselleck), the Fortüne-Program of the University of Tübingen (2422-0-0), and the Bundes Ministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053), AMORSA (FKZ 16SV7754), Gipuzkoa Regional Government (INKRATEK), Ministry of Science of the Basque Country (Elkartek: EXOTEK). A. Sarasola-Sanz’s work was supported by La Caixa-DAAD scholarship and N. Irastorza-Landa’s work by the Basque Government and IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

Questioning the evidence for BCI-based communication in the complete locked-in state.

<p>Questioning the evidence for BCI-based communication in the complete locked-in state</p

A High-Speed Brain-Computer Interface (BCI) Using Dry EEG Electrodes

This dataset contains the EEG data from 12 subjects using a c-VEP Brain-Computer Interface using dry EEG electrodes.<br><br><div>Spüler M (2017) A high-speed brain-computer interface (BCI) using dry EEG electrodes. <i>PLoS ONE</i> 12(2): e0172400. doi:10.1371/journal.pone.0172400</div

Average bitrate for the c-VEP BCI with dry EEG electrodes.

<p>Average bitrate for the c-VEP BCI with dry EEG electrodes.</p

Average correct letters per minute (CLM) for the c-VEP BCI with dry EEG electrodes.

<p>Average correct letters per minute (CLM) for the c-VEP BCI with dry EEG electrodes.</p

Screenshot of the c-VEP BCI.

<p><b>A:</b> screenshot during a trial. The letter in the lower right corner serves as a backspace symbol during the free-spelling condition and allows the user to correct mistakes. <b>B:</b> screenshot of the letter N being selected and the other characters being grayed out to indicate a selection.</p

Comparison of dry EEG with gel-based EEG.

<p>Comparison of dry EEG with gel-based EEG.</p