Paired Associative Stimulation with Brain-Computer Interfaces: A New Paradigm for Stroke Rehabilitation

Sabathiel, Nikolaus; Irimia, Dănuţ-Constantin; Allison, Brendan Z.; Guger, Christoph; Edlinger, Günter

doi:10.1007/978-3-319-39955-3_25

Cited by 8 publications

(11 citation statements)

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“…We also found that older patients had difficulty with the bar feedback when classification was incorrect, because it was hard to associate the corresponding movement with the feedback. Some recent work has validated VR technology within the context of MI BCIs for stroke rehabilitation (Luu et al, 2015 ; Soekadar et al, 2015 ; Remsik et al, 2016 ; Sabathiel et al, 2016 ). Therefore, a newer version of the recoveriX system uses a virtual avatar, and the patient sees the left and right hands in a first-person perspective (Sabathiel et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…Since closed-loop feedback that is effectively paired with the desired mental activity is a critical facet of any feedback system (Neuper and Allison, 2014 ), the “paired stimulation” (PS) made possible through motor-imagery BCI research could improve therapy outcomes. Recent research from several groups has supported this hypothesis (Ang et al, 2011 ; Pichiorri et al, 2011 , 2015 ; Ortner et al, 2012 ; Luu et al, 2015 ; Sburlea et al, 2015 ; Soekadar et al, 2015 ; Remsik et al, 2016 ; Sabathiel et al, 2016 ; Serrano et al, 2017 ). Furthermore it is known that a closed feedback loop increases the users performance (Wolpaw et al, 2002 ).…”

Section: Introductionmentioning

confidence: 95%

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High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training

et al. 2018

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Motor imagery (MI) based brain-computer interfaces (BCI) extract commands in real-time and can be used to control a cursor, a robot or functional electrical stimulation (FES) devices. The control of FES devices is especially interesting for stroke rehabilitation, when a patient can use motor imagery to stimulate specific muscles in real-time. However, damage to motor areas resulting from stroke or other causes might impair control of a motor imagery BCI for rehabilitation. The current work presents a comparative evaluation of the MI-based BCI control accuracy between stroke patients and healthy subjects. Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10-24 sessions lasting about 1 h each with the recoveriX system. The participants' EEG data were classified while they imagined left or right hand movements, and real-time feedback was provided on a monitor. If the correct imagination was detected, the FES was also activated to move the left or right hand. The grand average mean accuracy was 87.4% for all patients and sessions. All patients were able to achieve at least one session with a maximum accuracy above 96%. Both the mean accuracy and the maximum accuracy were surprisingly high and above results seen with healthy controls in prior studies. Importantly, the study showed that stroke patients can control a MI BCI system with high accuracy relative to healthy persons. This may occur because these patients are highly motivated to participate in a study to improve their motor functions. Participants often reported early in the training of motor improvements and this caused additional motivation. However, it also reflects the efficacy of combining motor imagination, seeing continuous bar feedback, and real hand movement that also activates the tactile and proprioceptive systems. Results also suggested that motor function could improve even if classification accuracy did not, and suggest other new questions to explore in future work. Future studies will also be done with a first-person view 3D avatar to provide improved feedback and thereby increase each patients' sense of engagement.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training

et al. 2018

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“…It is then just a natural step to try and incorporate available models of synaptic plasticity to underpin the desired adaptive behavior of neuroprosthetic or BCI systems; the involved strategies for closed-loop, real-time hybrid system, and the associated development of "neuromorphic" chips, also have potential to possibly augment PAS, endowing them with adaptive capabilities. In the PAS domain, such options just begin to be considered (Royter et al, 2016;Sabathiel et al, 2016); in the following, to provide a hint at available methodologies to make progress towards closed-loop, adaptive PAS systems, we first briefly touch upon the present status of implementable synaptic plasticity models, and next…”

Section: Section 4: Prospects In Neuromorphic Circuitsmentioning

confidence: 99%

The associative brain at work: Evidence from paired associative stimulation studies in humans

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Quartarone

Siebner

et al. 2017

Clinical Neurophysiology

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The original protocol of Paired Associative Stimulation (PAS) in humans implies repetitive cortical and peripheral nerve stimuli, delivered at specific inter-stimulus intervals, able to elicit non-invasively long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity in the human motor cortex. PAS has been designed to drive cortical LTP/LTD according to the Hebbian rule of associative plasticity. Over the last two decades, a growing number of researchers have increasingly used the PAS technique to assess cortical associative plasticity in healthy humans and in patients with movement disorders and other neuropsychiatric diseases. The present review covers the physiology, pharmacology, pathology and motor effects of PAS. Further sections of the review focus on new protocols of "modified PAS" and possible future application of PAS in neuromorphic circuits designed for brain-computer interface.

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“…By adding the MI BCI into the control loop, rewarding feedback such as avatar movement and FES activation is only possible when the patient performs the correct MI. This BCI-based feedback is much more tightly coupled to each patient's MI than conventional means, which should increase the functional improvement from therapy training (Remsik et al, 2016 ; Sabathiel et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

A New Method to Generate Artificial Frames Using the Empirical Mode Decomposition for an EEG-Based Motor Imagery BCI

et al. 2018

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EEG-based Brain-Computer Interfaces (BCIs) are becoming a new tool for neurorehabilitation. BCIs are used to help stroke patients to improve the functional capability of the impaired limbs, and to communicate and assess the level of consciousness in Disorder of Consciousness (DoC) patients. BCIs based on a motor imagery paradigm typically require a training period to adapt the system to each user's brain, and the BCI then creates and uses a classifier created with the acquired EEG. The quality of this classifier relies on amount of data used for training. More data can improve the classifier, but also increases the training time, which can be especially problematic for some patients. Training time might be reduced by creating new artificial frames by applying Empirical Mode Decomposition (EMD) on the EEG frames and mixing their Intrinsic Mode Function (IMFs). The purpose of this study is to explore the use of artificial EEG frames as replacements for some real ones by comparing classifiers trained with some artificial frames to classifiers trained with only real data. Results showed that, in some subjects, it is possible to replace up to 50% of frames with artificial data, which reduces training time from 720 to 360 s. In the remaining subjects, at least 12.5% of the real EEG frames could be replaced, reducing the training time by 90 s. Moreover, the method can be used to replace EEG frames that contain artifact, which reduces the impact of rejecting data with artifact. The method was also tested on an out of sample scenario with the best subjects from a public database, who yielded very good results using a frame collection with 87.5% artificial frames. These initial results with healthy users need to be further explored with patients' data, along with research into alternative IMF mixing strategies and using other BCI paradigms.

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Paired Associative Stimulation with Brain-Computer Interfaces: A New Paradigm for Stroke Rehabilitation

Cited by 8 publications

References 11 publications

High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training

High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training

The associative brain at work: Evidence from paired associative stimulation studies in humans

A New Method to Generate Artificial Frames Using the Empirical Mode Decomposition for an EEG-Based Motor Imagery BCI

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