Monitoring Neuro-Motor Recovery From Stroke With High-Resolution EEG, Robotics and Virtual Reality: A Proof of Concept

Comani, Silvia; Velluto, Lucia; Schinaia, Lorenzo; Cerroni, Gianluigi; Serio, Antonio; Buzzelli, S.; Sorbi, Sandro; Guarnieri, Biancamaria

doi:10.1109/tnsre.2015.2425474

Cited by 50 publications

(39 citation statements)

References 51 publications

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“…In addition, it is possible to use EEG during task performance in an ecological environment. This framework allows exploration of the brain's functional changes and monitoring of brain activity in response to rehabilitation tasks [201][202][203], potentially shedding light on specific brain-behavior relationships.…”

Section: Neural and Muscular Correlates Of The Sensorimotor Performancementioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots' capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients' recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. The term "robotic technology" in this application domain refers to any mechatronic device with a certain degree of intelligence that can physically intervene on the behavior of the patient, optimizing and speeding up his/her sensorimotor recovery. The two key capabilities of these robots are: (1) Assessing the human sensorimotor function; and (2) re-training the human brain in order to improve the patient's quality of life. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient. Usually, most of the rehabilitation devices implement a passive training modality (robot-driven, position control strategy), where the robot imposes the trajectories, and an active training modality (patient-driven), where the robot modulates its trajectory in response to the subject's intention to move [7,8]. However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walki...

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Section: Neural and Muscular Correlates Of The Sensorimotor Performancementioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

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“…It was found that neurophysiological data can be used to recognize the patterns of external stimuli (Speier et al, 2014; Bertrand, 2015; Comani et al, 2015). In this section, the CTL classifier is used to design an AA system that is able to realize adaptive function allocation between human operator and machine.…”

Section: Design and Simulation Of Adaptive Human-machine Systemmentioning

confidence: 99%

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load

2017

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This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

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“…By collecting electroencephalography (EEG) signals from the scalp, Comani et al detected real-time cortical electrical activity and identified instant and synchronized changes in the cortex ( 17 ). To date, several studies have reported relationships between EEG features and spasticity.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalography Mu Rhythm Changes and Decreased Spasticity After Repetitive Peripheral Magnetic Stimulation in Patients Following Stroke

Chen

Shu

et al. 2020

Front. Neurol.

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Background: Spasticity is common among patients with stroke. Repetitive peripheral magnetic stimulation (rPMS) is a painless and noninvasive therapy that is a promising approach to reducing spasticity. However, the central mechanism of this therapy remains unclear. Changes in cortical activity and decreased spasticity after rPMS intervention require further exploration. The aim of this study was to explore the electroencephalography (EEG) mu rhythm change and decrease in spasticity after rPMS intervention in patients with stroke. Materials and methods: A total of 32 patients with spasticity following stroke were recruited in this study and assigned to the rPMS group ( n = 16) or sham group ( n = 16). The modified Ashworth scale, modified Tardieu scale, and Fugl–Meyer assessment of the upper extremity were used to assess changes in upper limb spasticity and motor function. Before and after the rPMS intervention, EEG evaluation was performed to detect EEG mu rhythm changes in the brain. Results: After one session of rPMS intervention, spasticity was reduced in elbow flexors ( p < 0.05) and wrist flexors ( p < 0.05). Upper limb motor function measured according to the Fugl–Meyer assessment was improved ( p < 0.05). In the rPMS group, the power of event-related desynchronization decreased in the mu rhythm band (8–12 Hz) in the contralesional hemisphere ( p < 0.05). Conclusions: The results indicate that rPMS intervention reduced spasticity. Cortical activity changes may suggest this favorable change in terms of its neurological effects on the central nervous system.

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Monitoring Neuro-Motor Recovery From Stroke With High-Resolution EEG, Robotics and Virtual Reality: A Proof of Concept

Cited by 50 publications

References 51 publications

Perspectives and Challenges in Robotic Neurorehabilitation

Perspectives and Challenges in Robotic Neurorehabilitation

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load

Electroencephalography Mu Rhythm Changes and Decreased Spasticity After Repetitive Peripheral Magnetic Stimulation in Patients Following Stroke

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