Neural decoding of robot-assisted gait during rehabilitation after stroke

Contreras-Vidal, José L.; Bôrtole, Magdo; Zhu, Fangshi; Nathan, Kevin; Venkatakrishnan, Anusha; Francisco, Gerard E.; Soto, Rogelio; Pons, José L.

doi:10.1109/werob.2017.8383869

Cited by 9 publications

(45 citation statements)

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“…The filtered signals, which are called slow cortical potentials, were confirmed to be particularly informative for decoding motor-related parameters [27-32]. We used multiple linear models, also referred to as Wiener filter, to decode individual muscle and muscle synergy activations from the slow cortical potentials, as used in previous studies decoding motor parameters [27-32]. Figure 4 provides examples of real and reconstructed muscle synergy activations (Figure 4A) and individual muscle activations (Figure 4B) from a participant.…”

Section: Resultsmentioning

confidence: 99%

“…For example, delta band cortical activity plays a role in decision-making about somatosensory discrimination [45] and prediction of sensory events [46]. Additionally, neural decoding studies in humans have demonstrated that delta band activity is particularly informative for decoding kinematics parameters [27-32] and muscle activity [30]. In recent rodent studies, multisensory integration in widespread brain networks through slow cortical waves was suggested by calcium imaging [47].…”

Section: Discussionmentioning

confidence: 99%

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Cortical control of locomotor muscle activity through muscle synergies in humans: a neural decoding study

Yokoyama

Kaneko

Ogawa

et al. 2018

Preprint

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24Walking movements are orchestrated by the activation of a large number of muscles. The 25 control of numerous muscles during walking is believed to be simplified by flexible activation 26 of groups of muscles called muscle synergies. Although significant corticomuscular 27 connectivity during walking has been reported, the level at which the cortex controls locomotor 28 muscle activity (i.e., muscle synergy or individual muscle level) remains unclear. Here, we 29 examined cortical involvement in muscle control during walking by brain decoding of the 30 activation of muscle synergies and individual muscles from electroencephalographic (EEG) 31 signals using linear decoder models. First, we demonstrated that activation of locomotor muscle 32 synergies was decoded from slow cortical waves with significant accuracy. In addition, we 33 found that decoding accuracy for muscle synergy activation was greater than that for individual 34 muscle activation and that decoding of individual muscle activation was based on muscle 35 synergy-related cortical information. Taken together, these results provide indirect evidence that 36 the cerebral cortex hierarchically controls multiple muscles through a few muscle synergies 37 during walking. Our findings extend the current understanding of the role of the cortex in 38 muscular control during walking and could accelerate the development of effective 39 brain-machine interfaces for people with locomotor disabilities. 40 41 42 43 44 45 46 47 48 49 3 50 Introduction 51Human locomotor movement is organized by the coordinated activation of a large 52 number of muscles. It has been suggested that complex muscle activity is generated from a 53 small number of groups of muscle activations called muscle synergies [1][2][3][4][5]. Locomotor muscle 54 synergies are thought to be structured in the spinal circuitry [6, 7]. Based on previous studies 55 examining synergy activation among different subject groups, it has been suggested that the 56 cortex activates locomotor muscle synergies [1, 2, 7]. These studies reported that locomotor 57 muscle synergy in healthy adults exhibited activation that was sharply timed around gait events 58 [1], whereas locomotor muscle synergy in neonates [2] and complete spinal cord injury (SCI) 59 patients [7] exhibited smooth prolonged activation. The differences in the patterns in neonates 60 and SCI patients could be caused by immature and injured corticospinal pathways, respectively. 61Based on these findings, it is thought that cortical descending commands modulate basic 62 locomotor muscle synergy activation generated by subcortical structures, particularly in the 63 spinal cord. However, there is currently no direct evidence of cortico-muscle synergy 64 relationships supported by simultaneous recordings of cortical activity and muscle synergy 65 activation during walking. 66Unlike quadruped animals [8, 9], human bipedal walking is characterized by 67 significant cortical activity even during undemanding steady-state walking [10][11][12][13][14][15][16]...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Cortical control of locomotor muscle activity through muscle synergies in humans: a neural decoding study

Yokoyama

Kaneko

Ogawa

et al. 2018

Preprint

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“…In preparation for neural decoding, EEG signals were band-pass filtered in the delta band (0.5-4Hz). The filtered signals, which are called slow cortical potentials, were confirmed to be informative for decoding actual movements (25)(26)(27)(28)(29) and motor intention (30)(31)(32). We used regularized linear discriminant analysis (rLDA) to decode the gait phases from cortical signals, as used in previous EEG decoding studies (33)(34)(35).…”

Section: Decoding Accuracy Of the Neural Decodersmentioning

confidence: 99%

“…In the current study, fluctuations in the amplitude of slow cortical potentials (0.5−4 Hz in the time domain) were used for the neural decoding analysis based on a similar methodology used in previous studies on actual movements (25)(26)(27)(28)(29) and motor intention (30)(31)(32). An overview of the EEG processing steps is shown in Figure 2.…”

Section: Data Analysis Eeg Pre-processingmentioning

confidence: 99%

“…Subsequently, each participant's EEG signals were subjected to independent component analysis (ICA) to remove artifacts in the EEG signals (67,68). From the extracted ICs, artifact ICs were visually identified and removed based on the following criteria: (1) muscle artifact: power in the 20-100 Hz was higher than that in the alpha bands (8-12 Hz) (69); (2) eye-blink and horizontal eye-movement artifact: power of the frequency below 3Hz was higher than that of the faster frequency -there was no clear power peak in the alpha (8)(9)(10)(11)(12) and beta (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) bands, and the topological map and the time-series data corresponded to previously reported features of these components (69,70). Examples of the artifact and brain components are presented in Figure 2A.…”

Section: Data Analysis Eeg Pre-processingmentioning

confidence: 99%

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Neural decoding of gait phase information during motor imagery and improvement of the decoding accuracy by concurrent action observation

Yokoyama

Kaneko

Watanabe

et al. 2020

Preprint

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Brain decoding of motor imagery (MI) is crucial for the control of neuroprosthesis, and it provides insights into the underlying neural mechanisms. Walking consists of stance and swing phases, which are associated with different biomechanical and neural control features. However, previous studies on the decoding of the MI of walking focused on the classification of more simple information (e.g., walk and rest). Here, we investigated the feasibility of electroencephalogram (EEG) decoding of the two gait phases during the MI of walking and whether the combined use of MI and action observation (AO) would improve decoding accuracy. We demonstrated that the stance and swing phases could be decoded from EEGs during AO or MI alone. Additionally, the combined use of MI and AO improved decoding accuracy. The decoding models indicated that the improved decoding accuracy following the combined use of MI and AO was facilitated by the additional information resulting from the concurrent cortical activations by multiple regions associated with MI and AO. This study is the first to show that decoding the stance versus swing phases during MI is feasible. The current findings provide fundamental knowledge for neuroprosthetic design and gait rehabilitation, and they expand our understanding of the neural activity underlying AO, MI, and AO+MI of walking.

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Opportunities and challenges in the development of exoskeletons for locomotor assistance

et al. 2022

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Neural decoding of robot-assisted gait during rehabilitation after stroke

Cited by 9 publications

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Cortical control of locomotor muscle activity through muscle synergies in humans: a neural decoding study

Cortical control of locomotor muscle activity through muscle synergies in humans: a neural decoding study

Neural decoding of gait phase information during motor imagery and improvement of the decoding accuracy by concurrent action observation

Opportunities and challenges in the development of exoskeletons for locomotor assistance

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