Neural mechanisms of brain–computer interface control

Halder, Sebastian; Agorastos, D.; Veit, Ralf; Hammer, Eva Maria; Lee, S.; Várkuti, Bálint; Bogdan, Martin; Rosenstiel, Wolfgang; Birbaumer, Niels; Kübler, Andrea

doi:10.1016/j.neuroimage.2011.01.021

Cited by 150 publications

(123 citation statements)

References 74 publications

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“…Studies in healthy volunteers and patients with stroke show that motor imagery results in similar parietofrontal functional network interactions to those observed for execution of hand motor tasks (Sharma et al, 2009a;Gao et al, 2010). Moreover, the ability of healthy volunteers to acquire volitional control of sensorimotor rhythm modulation appears to be related to the degree that the constituent regions of this network are recruited by the motor imagery strategy used (Halder et al, 2011). Thus, it would be reasonable to expect that volitional control of neural activity through operant conditioning using motor imagery would relate to architectural features of this network.…”

Section: Global Functional Network Cost-efficiencymentioning

confidence: 78%

Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke

Buch

Shanechi

Fourkas

et al. 2012

Brain

125

110

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Chronic stroke patients with heterogeneous lesions, but no direct damage to the primary sensorimotor cortex, are capable of longitudinally acquiring the ability to modulate sensorimotor rhythms using grasping imagery of the affected hand. Volitional modulation of neural activity can be used to drive grasping functions of the paralyzed hand through a brain-computer interface. The neural substrates underlying this skill are not known. Here, we investigated the impact of individual patient's lesion pathology on functional and structural network integrity related to this volitional skill. Magnetoencephalography data acquired throughout training was used to derive functional networks. Structural network models and local estimates of extralesional white matter microstructure were constructed using T 1 -weighted and diffusion-weighted magnetic resonance imaging data. We employed a graph theoretical approach to characterize emergent properties of distributed interactions between nodal brain regions of these networks. We report that interindividual variability in patients' lesions led to differential impairment of functional and structural network characteristics related to successful post-training sensorimotor rhythm modulation skill. Patients displaying greater magnetoencephalography global cost-efficiency, a measure of information integration within the distributed functional network, achieved greater levels of skill. Analysis of lesion damage to structural network connectivity revealed that the impact on nodal betweenness centrality of the ipsilesional primary motor cortex, a measure that characterizes the importance of a brain region for integrating visuomotor information between frontal and parietal cortical regions and related thalamic nuclei, correlated with skill. Edge betweenness centrality, an analogous measure, which assesses the role of specific white matter fibre pathways in network integration, showed a similar relationship between skill and a portion of the ipsilesional superior longitudinal fascicle connecting premotor and posterior parietal visuomotor regions known to be crucially involved in normal grasping behaviour. Finally, estimated white matter microstructure integrity in regions of the contralesional superior longitudinal fascicle adjacent to primary sensorimotor and posterior parietal cortex, as well as grey matter volume co-localized to these specific regions, positively correlated with sensorimotor rhythm modulation leading to successful brain-computer interface control. Thus, volitional modulation of ipsilesional neural activity leading to control of paralyzed hand grasping function through a brain-computer interface after longitudinal training relies on structural and functional connectivity in both ipsilesional and contralesional parietofrontal pathways involved in visuomotor information processing. Extant integrity of this structural network may serve as a future predictor of response to longitudinal therapeutic interventions geared towards training sensorimotor rhythms in the lesioned bra...

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Section: Global Functional Network Cost-efficiencymentioning

confidence: 78%

Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke

Buch

Shanechi

Fourkas

et al. 2012

Brain

125

110

View full text Add to dashboard Cite

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“…Motor imagery is defined as the mental simulation of a kinesthetic movement (Decety and Inqvar, 1990;Neuper et al, 2005). Signal processing algorithms, individual users' characteristics, such as psychosocial and physiological parameters (e.g., fine motor skills) or brain structures, can predict performances for SMR-based BCIs Halder et al, 2011Halder et al, , 2013Hammer et al, 2011;Randolph, 2012). Besides these factors, feedback is a necessary feature for initial learning of the BCI skill (Brown, 1970;Kuhlman, 1978;McFarland et al, 1998;Wolpaw et al, 1991Wolpaw et al, , 2002.…”

Section: Introductionmentioning

confidence: 99%

The effect of multimodal and enriched feedback on SMR-BCI performance

Sollfrank

Ramsay

Perdikis

et al. 2016

Clinical Neurophysiology

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“…Most of these studies focus on inter-subject variability from a physiological [2][3][4][5][6], anatomical [7,8], or psychological [9,10] perspectives. Although precise distinction between user-related and system-related causes of performance variations may not be simple [11], these studies provide a better understanding of these causes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

Saeedi

Chavarriaga

Leeb

et al. 2016

IEEE Comput. Intell. Mag.

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Abstract. One of the challenges of using brain-computer interfaces (BCIs) over extended periods of time is the variation of the users' performance from one experimental day to another. The goal of the current study is to propose a performance estimator for an electroencephalography-based motor imagery BCI by assessing the reliability of a command (i.e., predicting a 'short' or 'long' command delivery time, CDT). Using a short time window (< 1.5 s, shorter than the delivery time) of the mental task execution and a linear discriminant analysis classifier, we could reliably differentiate between short and long CDT (Area under the sensitivity-specificity curve, AUC ≈ 0.8) for 9 healthy subjects. Moreover, we assessed the feasibility of providing online adaptive assistance using the performance estimator in a BCI game by comparing two conditions: (i) allowing a 'fixed timeout' to deliver each command or (ii) providing 'adaptive assistance' by giving more time if the performance estimator detects a long CDT. The results revealed that providing adaptive assistance increases the ratio of correct commands significantly (p < 0.01). Moreover, the task load index (measured via the NASA TLX questionnaire) shows a significantly higher user acceptance in case of providing adaptive assistance (p < 0.01). Furthermore, the results obtained in this study were used to simulate a robotic navigation scenario, which showed how adaptive assistance improved performance.

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Neural mechanisms of brain–computer interface control

Cited by 150 publications

References 74 publications

Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke

Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke

The effect of multimodal and enriched feedback on SMR-BCI performance

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

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