Challenges of neural interfaces for stroke motor rehabilitation

Vidaurre, Carmen; Irastorza-Landa, Nerea; Sarasola-Sanz, Andrea; Insausti-Delgado, Ainhoa; Ray, Andreas M.; Bibián, Carlos; Helmhold, Florian; Mahmoud, Wala J.; Ortego-Isasa, Iñaki; López-Larraz, Eduardo; Lozano Peiteado, Héctor; Ramos-Murguialday, Ander

doi:10.3389/fnhum.2023.1070404

Cited by 4 publications

(4 citation statements)

References 275 publications

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“…Neural interfaces have great potential for the rehabilitation of upper-limb paralysis after a stroke 1–4 . Many research laboratories worldwide are currently developing and testing body actuators controlled by brain and muscle activity 5–11 .…”

Section: Introductionmentioning

confidence: 99%

Unveiling Movement Intention after Stroke: Integrating EEG and EMG for Motor Rehabilitation

López-Larraz,

Sarasola-Sanz,

Birbaumer

et al. 2024

Preprint

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Detecting attempted movements of a paralyzed limb is a key step for neural interfaces for motor rehabilitation and restoration after a stroke. In this paper, we present a systematic evaluation of electroencephalographic (EEG) and electromyographic (EMG) activity to decode when stroke patients with severe upper-limb paralysis attempt to move their affected arm. EEG and EMG recordings of 35 chronic stroke patients were analyzed. We trained classifiers to discriminate between rest and movement attempt states relying on brain, muscle, or both types of features combined. Our results reveal that: i) EEG and residual EMG features provide complementary information to detect attempted movements, obtaining significantly higher decoding accuracy when both sources of activity are combined; ii) EMG-based, but not EEG-based, decoding accuracy correlates with the degrees of impairment of the patient; and iii) the percentage of patients that achieve decoding accuracy above the chance level strongly depends on the type of features considered, and can be as low as 50% of them if only ipsilesional EEG is used. These results offer new perspectives to develop improved neurotechnologies that establish a more accurate contingent link between the central and peripheral nervous system after a stroke, leveraging Hebbian learning and facilitating functional plasticity and recovery.

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Section: Introductionmentioning

confidence: 99%

Unveiling Movement Intention after Stroke: Integrating EEG and EMG for Motor Rehabilitation

López-Larraz,

Sarasola-Sanz,

Birbaumer

et al. 2024

Preprint

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“…Taken together, innovative methodologies are needed. Recent studies endorse the use of therapies that use exoskeletons ( Hohl et al, 2022 ), brain-machine interfaces ( Ramos-Murguialday et al, 2013 ; 2019 ), and VR devices ( Vourvopoulos et al, 2019 ; Qiu et al, 2020 ; Fluet et al, 2021 ) to restore the function of the paretic limb after stroke ( Stinear et al, 2020 ; Vidaurre et al, 2023 ; Amin et al, 2024 ). These technologies provide new opportunities in rehabilitation as they stimulate intense practice or engage patients in immersive exercises.…”

Section: Introductionmentioning

confidence: 99%

“…These technologies provide new opportunities in rehabilitation as they stimulate intense practice or engage patients in immersive exercises. Although these approaches offer an environment in which the many variables that influence motor behavior can be controlled and show great potential for motor rehabilitation, we know little about what aspects of training are more effective in promoting functional reorganization of the central nervous system (CNS) ( Vidaurre et al, 2023 ; Amin et al, 2024 ).…”

Section: Introductionmentioning

confidence: 99%

Myoelectric control and virtual reality to enhance motor rehabilitation after stroke

Berger,

d’Avella

2024

Front. Bioeng. Biotechnol.

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Effective upper-limb rehabilitation for severely impaired stroke survivors is still missing. Recent studies endorse novel motor rehabilitation approaches such as robotic exoskeletons and virtual reality systems to restore the function of the paretic limb of stroke survivors. However, the optimal way to promote the functional reorganization of the central nervous system after a stroke has yet to be uncovered. Electromyographic (EMG) signals have been employed for prosthetic control, but their application to rehabilitation has been limited. Here we propose a novel approach to promote the reorganization of pathological muscle activation patterns and enhance upper-limb motor recovery in stroke survivors by using an EMG-controlled interface to provide personalized assistance while performing movements in virtual reality (VR). We suggest that altering the visual feedback to improve motor performance in VR, thereby reducing the effect of deviations of the actual, dysfunctional muscle patterns from the functional ones, will actively engage patients in motor learning and facilitate the restoration of functional muscle patterns. An EMG-controlled VR interface may facilitate effective rehabilitation by targeting specific changes in the structure of muscle synergies and in their activations that emerged after a stroke—offering the possibility to provide rehabilitation therapies addressing specific individual impairments.

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“…Rehabilitation involving movement is marked by prolonged exercises and gradual recovery, particularly for the fine motor skills of the distal extremities. Numerous studies have concentrated on various aspects of motor training, including motor paradigms, training duration, intensity, and types of exercises (active, passive, or resistive), aiming to enhance motor function ( Breceda and Dromerick, 2013 ; Kim et al, 2020 ; Vidaurre et al, 2023 ). However, motivation is significantly neglected in both clinical practice and research studies on motor rehabilitation ( Robertson, 2013 ; Verrienti et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

The effect of reward on motor learning: different stage, different effect

Zhao,

Zhang,

2024

Front. Hum. Neurosci.

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Motor learning is a prominent and extensively studied subject in rehabilitation following various types of neurological disorders. Motor repair and rehabilitation often extend over months and years post-injury with a slow pace of recovery, particularly affecting the fine movements of the distal extremities. This extended period can diminish the motivation and persistence of patients, a facet that has historically been overlooked in motor learning until recent years. Reward, including monetary compensation, social praise, video gaming, music, and virtual reality, is currently garnering heightened attention for its potential to enhance motor motivation and improve function. Numerous studies have examined the effects and attempted to explore potential mechanisms in various motor paradigms, yet they have yielded inconsistent or even contradictory results and conclusions. A comprehensive review is necessary to summarize studies on the effects of rewards on motor learning and to deduce a central pattern from these existing studies. Therefore, in this review, we initially outline a framework of motor learning considering two major types, two major components, and three stages. Subsequently, we summarize the effects of rewards on different stages of motor learning within the mentioned framework and analyze the underlying mechanisms at the level of behavior or neural circuit. Reward accelerates learning speed and enhances the extent of learning during the acquisition and consolidation stages, possibly by regulating the balance between the direct and indirect pathways (activating more D1-MSN than D2-MSN) of the ventral striatum and by increasing motor dynamics and kinematics. However, the effect varies depending on several experimental conditions. During the retention stage, there is a consensus that reward enhances both short-term and long-term memory retention in both types of motor learning, attributed to the LTP learning mechanism mediated by the VTA-M1 dopaminergic projection. Reward is a promising enhancer to bolster waning confidence and motivation, thereby increasing the efficiency of motor learning and rehabilitation. Further exploration of the circuit and functional connections between reward and the motor loop may provide a novel target for neural modulation to promote motor behavior.

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Challenges of neural interfaces for stroke motor rehabilitation

Cited by 4 publications

References 275 publications

Unveiling Movement Intention after Stroke: Integrating EEG and EMG for Motor Rehabilitation

Unveiling Movement Intention after Stroke: Integrating EEG and EMG for Motor Rehabilitation

Myoelectric control and virtual reality to enhance motor rehabilitation after stroke

The effect of reward on motor learning: different stage, different effect

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