Motor Rehabilitation for Hemiparetic Stroke Patients Using a Brain-Computer Interface Method

Cho, Woosang; Heilinger, Alexander; Ortner, Rupert; Swift, James; Edlinger, Gnter; Guger, Christoph; Murovec, Nensi; Xu, Ruidong; Zehetner, Manuela; Schobesberger, Stefan

doi:10.1109/smc.2018.00178

Cited by 14 publications

(7 citation statements)

References 16 publications

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“…One promising approach is based on motor-imagery (MI), which is the cognitive process of thinking about the motion of a body part, e.g., the left hand, without actually performing it. MI-BMIs assist people with impairments to regain independence, e.g., by steering a wheelchair [2], controlling a prosthesis [3], [4], or by enabling motor rehabilitation [5].…”

Section: Introductionmentioning

confidence: 99%

EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

Ingolfsson¹,

Hersche²,

Wang³

et al. 2020

Preprint

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In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brainmachine interfaces (MI-BMIs) based on electroencephalography (EEG). While achieving high classification accuracy, DL models have also grown in size, requiring a vast amount of memory and computational resources. This poses a major challenge to an embedded BMI solution that guarantees user privacy, reduced latency, and low power consumption by processing the data locally. In this paper, we propose EEG-TCNET, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters. Its low memory footprint and low computational complexity for inference make it suitable for embedded classification on resource-limited devices at the edge. Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNET achieves 77.35% classification accuracy in 4-class MI. By finding the optimal network hyperparameters per subject, we further improve the accuracy to 83.84%. Finally, we demonstrate the versatility of EEG-TCNET on the Mother of All BCI Benchmarks (MOABB), a large scale test benchmark containing 12 different EEG datasets with MI experiments. The results indicate that EEG-TCNET successfully generalizes beyond one single dataset, outperforming the current state-of-the-art (SoA) on MOABB by a meta-effect of 0.25.

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Section: Introductionmentioning

confidence: 99%

EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

Ingolfsson¹,

Hersche²,

Wang³

et al. 2020

Preprint

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“…The main objective of the study was not to demonstrate the efficacy of the BCI system in neurorehabilitation, nor to compare BCI-based therapy to other forms of therapy. The relationship between BCI stroke therapy and functional outcomes has been addressed in numerous studies (Ramos-Murguialday et al, 2013;Pichiorri et al, 2015;Remsik et al, 2016;Biasiucci et al, 2018;Cho et al, 2018). However, we would like to add to the existing literature by discussing our observations.…”

Section: Lc In Beta Bandmentioning

confidence: 99%

EEG Biomarkers Related With the Functional State of Stroke Patients

et al. 2020

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Introduction: Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. Methods: Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps. Results: The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P < 0.001) and FMA of lower extremity (ρ = −0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (FMA = 1 median [IQR: 0-8], P = 0.002). Conclusion: The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.

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“…Latterly, Pfurtscheller et al [92] reported an EEG-controlled FES device for restoring hand grasp using noninvasive surface electrodes. In following-up studies, motor function improvement using the EEG-controlled closed-loop FES systems has been detected [93][94][95][96]. Not only equipped with FES, the motor BCIs combined with robotic devices, e.g.…”

Section: A Neurorehabilitationmentioning

confidence: 99%

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights

Wang,

Bi,

Fei

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed.

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Motor Rehabilitation for Hemiparetic Stroke Patients Using a Brain-Computer Interface Method

Cited by 14 publications

References 16 publications

EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

EEG Biomarkers Related With the Functional State of Stroke Patients

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights

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