Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients

Spüler, Martin; Grimm, Florian; Gharabaghi, Alireza; Bogdan, Martin

doi:10.1007/978-3-319-26242-0_9

Cited by 8 publications

(8 citation statements)

References 30 publications

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“…Even though both methods employ similar technology, restorative interfaces differ in concept substantially from brain-controlled assistive devices, which aim to compensate for lost function (Hochberg et al, 2012 ; Collinger et al, 2013 ). While the latter approach intends to maximize speed and classification accuracy for high-dimensional control (Spüler et al, 2014 , 2016 ), the former aims to facilitate self-regulation of brain activity, which is considered beneficial for recovery and might ultimately lead to persistent functional gains (Naros and Gharabaghi, 2015 ). Such a restorative goal necessitates methodological specifications, e.g., in the areas of constrained feature space, regularized feature weights, cognitive load, feedback modality, and threshold adaptation to facilitate reinforcement learning of brain self-regulation and corticospinal connectivity (Bauer et al, 2016a , b ; Bauer and Gharabaghi, under review).…”

Section: Discussionmentioning

confidence: 99%

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton

Grimm

Walter²,

Spüler³

et al. 2016

Front. Neurosci.

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Brain-machine interface-controlled (BMI) neurofeedback training aims to modulate cortical physiology and is applied during neurorehabilitation to increase the responsiveness of the brain to subsequent physiotherapy. In a parallel line of research, robotic exoskeletons are used in goal-oriented rehabilitation exercises for patients with severe motor impairment to extend their range of motion (ROM) and the intensity of training. Furthermore, neuromuscular electrical stimulation (NMES) is applied in neurologically impaired patients to restore muscle strength by closing the sensorimotor loop. In this proof-of-principle study, we explored an integrated approach for providing assistance as needed to amplify the task-related ROM and the movement-related brain modulation during rehabilitation exercises of severely impaired patients. For this purpose, we combined these three approaches (BMI, NMES, and exoskeleton) in an integrated neuroprosthesis and studied the feasibility of this device in seven severely affected chronic stroke patients who performed wrist flexion and extension exercises while receiving feedback via a virtual environment. They were assisted by a gravity-compensating, seven degree-of-freedom exoskeleton which was attached to the paretic arm. NMES was applied to the wrist extensor and flexor muscles during the exercises and was controlled by a hybrid BMI based on both sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity. The stimulation intensity was individualized for each targeted muscle and remained subthreshold, i.e., induced no overt support. The hybrid BMI controlled the stimulation significantly better than the offline analyzed ERD (p = 0.028) or EMG (p = 0.021) modality alone. Neuromuscular stimulation could be well integrated into the exoskeleton-based training and amplified both the task-related ROM (p = 0.009) and the movement-related brain modulation (p = 0.019). Combining a hybrid BMI with neuromuscular stimulation and antigravity assistance augments upper limb function and brain activity during rehabilitation exercises and may thus provide a novel restorative framework for severely affected stroke patients.

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Section: Discussionmentioning

confidence: 99%

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton

Grimm

Walter²,

Spüler³

et al. 2016

Front. Neurosci.

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“…The application of non-linear regression models such as Support Vector Machine Regression (SVR) (Mehring et al, 2003 ; Kim K. H. et al, 2006 ) or ANN models (Sanchez et al, 2002 ; Hatsopoulos et al, 2004 ; Kim K. H. et al, 2006 ; Kim S.-P. et al, 2006 ) has been additionally proposed for SUA/MUA decoding, and tested in offline preliminary studies or, more recently, in online motor BCI studies (Sussillo et al, 2016 ). SVR- (Spüler et al, 2016 ) and Gaussian Processes-based (Wang et al, 2010 ) trajectory reconstruction has also been reported in ECoG-driven offline BCI studies.…”

Section: Data-driven Decodersmentioning

confidence: 97%

“…When it is based on Burg AR parameters, it is referred to as a maximum-entropy spectral estimation. Maximum-entropy spectral estimation has been performed in several offline ECoG studies (Anderson et al, 2012 ; Bundy et al, 2016 ; Spüler et al, 2016 ) and in an online motor EEG-based BCI system (Bundy et al, 2017 ).…”

Section: Feature Extractionmentioning

confidence: 99%

“…The reduction of the dimension of neural feature representations is mainly performed in offline or online motor BCI studies by means of projection methods, such as the principal component analysis and its variants (Devulapalli, 1996 ; Wu et al, 2003b ; Kim S.-P. et al, 2006 ; Aggarwal et al, 2008 ; Ke and Li, 2009 ; Wang W. et al, 2009 ; Argunşah and Çetin, 2010 ; Suk and Lee, 2010 ; Bhattacharyya et al, 2011 ; Kao et al, 2013 , 2017 ) or by means of feature selection methods, such as stepwise forward (Brunner et al, 2007 ; Liang and Bougrain, 2012 ; Wang et al, 2012 ; Hotson et al, 2014 ) or forward-backward (McFarland et al, 2010 ) selection procedures, LASSO-based sparse modeling methods (Least Absolute Shrinkage and Selection Operator) (Fazli et al, 2011 ; Kelly et al, 2012 ; Wang et al, 2015 ), so-called filter methods (Schalk et al, 2007 ; Spüler et al, 2016 ), genetic algorithms (Flotzinger et al, 1994 ; Graimann et al, 2004 ; Wei et al, 2006 ; Boostani et al, 2007 ; Fatourechi et al, 2007 ; Wei and Tu, 2008 ) or alternative approaches such as distinctive sensitive learning vector quantization (Flotzinger et al, 1994 ).…”

Section: Feature Extractionmentioning

confidence: 99%

“…For example, several teams have investigated the use of non-linear models for neural signal decoding (Li, 2014 ), assuming that y t = f ( x t ) + ϵ with f non-linear. These studies mainly consisted of offline trajectory reconstructions (Kim K. H. et al, 2006 ; Eliseyev and Aksenova, 2014 ; Spüler et al, 2016 ).…”

Section: Data-driven Decodersmentioning

confidence: 99%

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Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Schaeffer

Aksenova

2018

Front. Neurosci.

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Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.

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Central and Peripheral Neural Interfaces for Control of Upper Limb Actuators for Motor Rehabilitation After Stroke: Technical and Clinical Considerations

Irastorza-Landa

Sarasola-Sanz

Bibian

et al. 2022

Handbook of Neuroengineering

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Comparing Methods for Decoding Movement Trajectory from ECoG in Chronic Stroke Patients

Cited by 8 publications

References 30 publications

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton

Hybrid Neuroprosthesis for the Upper Limb: Combining Brain-Controlled Neuromuscular Stimulation with a Multi-Joint Arm Exoskeleton

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Central and Peripheral Neural Interfaces for Control of Upper Limb Actuators for Motor Rehabilitation After Stroke: Technical and Clinical Considerations

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