Laterality Coefficient: An EEG parameter related with the functional improvement in stroke patients

Sebastián-Romagosa, Marc; Ortner, Rupert; Udina-Bonet, Esther; Dinarès-Ferran, Josep; Mayr, Katrin; Cao, Fan; Guger, Christoph

doi:10.1109/bhi.2019.8834472

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…For each frequency band, active voxels were summed up within each of three areas BA 4, BA 6, and BA 1–3, over each hemisphere separately, and a weighted laterality index (wLI) value was calculated according to Equation (1) for each of these three areas [ 31 ]. These specific areas were chosen because they exhibit cortical mapping of body parts on the contralateral side; hence, the laterality of activity could potentially be used to distinguish between left and right lower-limb activity.…”

Section: Methodsmentioning

confidence: 99%

Source-Based EEG Neurofeedback for Sustained Motor Imagery of a Single Leg

Zulauf-Czaja

Osuagwu

Vučković

2023

Sensors

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The aim of the study was to test the feasibility of visual-neurofeedback-guided motor imagery (MI) of the dominant leg, based on source analysis with real-time sLORETA derived from 44 EEG channels. Ten able-bodied participants took part in two sessions: session 1 sustained MI without feedback and session 2 sustained MI of a single leg with neurofeedback. MI was performed in 20 s on and 20 s off intervals to mimic functional magnetic resonance imaging. Neurofeedback in the form of a cortical slice presenting the motor cortex was provided from a frequency band with the strongest activity during real movements. The sLORETA processing delay was 250 ms. Session 1 resulted in bilateral/contralateral activity in the 8–15 Hz band dominantly over the prefrontal cortex while session 2 resulted in ipsi/bilateral activity over the primary motor cortex, covering similar areas as during motor execution. Different frequency bands and spatial distributions in sessions with and without neurofeedback may reflect different motor strategies, most notably a larger proprioception in session 1 and operant conditioning in session 2. Single-leg MI might be used in the early phases of rehabilitation of stroke patients. Simpler visual feedback and motor cueing rather than sustained MI might further increase the intensity of cortical activation.

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Section: Methodsmentioning

confidence: 99%

Source-Based EEG Neurofeedback for Sustained Motor Imagery of a Single Leg

Zulauf-Czaja

Osuagwu

Vučković

2023

Sensors

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“…The sensorimotor "mu" rhythms and sensorimotor beta rhythms mainly originated in the somatosensory postcentral gyrus and precentral motor cortex, respectively [19,20]. Some clinical studies have elucidated that EEG brain oscillatory activities (mainly referred to "mu" and beta rhythms) can be acted as a biomarker of motor recovery in stroke patients [21][22][23][24][25]. In these studies, the event-related synchronization and event-related desynchronization (ERS/ERD, referred to the increase/decrease of relative power) overlying sensorimotor cortex in the "mu" and beta band was usually used to assess the activation of the sensorimotor cortex.…”

Section: Introductionmentioning

confidence: 99%

“…In the clinic experiment, FV was applied over the antagonist muscle belly to the spastic muscle and EEG was used to monitor the brain activity during FV. All sub-beta MRPD at C3 and C4, including beta1 MRPD (13-18 Hz), beta2 MRPD (18)(19)(20)(21), and beta3 MRPD (21)(22)(23)(24)(25)(26)(27)(28)(29)(30), as well as the difference index of all sub-beta MRPD between C3 and C4, was calculated to evaluate the activation patterns of S1-M1. The relative changes of muscle displacement between the spastic muscle and its antagonist, as well as muscle compliance, were used to evaluate the relief of SPP.…”

Section: Introductionmentioning

confidence: 99%

Brain oscillatory activity correlates with the relief of post-stroke spasticity following focal vibration

Li¹,

Luo²,

Xu³

et al. 2022

J. Integr. Neurosci.

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Background: Some evidence has demonstrated that focal vibration (FV) contributes to the relief of post-stroke spasticity (PSS). Although the changes of cortical activity correlating with the relief of PSS induced by FV have been explored using transcranial magnetic stimulation, brain oscillatory activity during the above-mentioned process has not been fully understood. Objective: The main purpose of this study is to explore the correlation between the changes in brain oscillatory activity and the relief of PSS following FV. Methods: A clinical experiment was carried out, in which FV (87 Hz, 0.28 mm) was applied over the antagonist muscle's belly of the spastic muscle of ten chronic spastic stroke patients. An electroencephalogram was recorded following before-FV and three sessions of FV. Muscle properties to assess the relief of PSS were tested before-FV and immediately after three sessions of FV. Results: EEG analysis has shown that FV can lead to the significant decrease in the relative power at C3 and C4 in the beta1 (13, 18 Hz), as well as C3 and C4 in the beta3 band (21, 30 Hz), indicating the activation of primary sensorimotor cortex (S1-M1). Muscle properties analysis has shown that, in the state of flexion of spastic muscle, muscle compliance and muscle displacement of the spastic muscle significantly increased right after FV, illustrating the relief of the spasticity. Moreover, the increase of muscle compliance is positively correlated with the reduction of difference index of the activation of bilateral S1-M1. Conclusions: This finding indicated that the relief of PSS can be associated with the activation of bilateral S1-M1 where the activation of the ipsilesional S1-M1 was higher than that of the contralesional one. This study showed the brain oscillatory activity in the bilateral S1-M1 correlating with the relief of PSS following FV, which could contribute to establishing cortex oscillatory activity as a biomarker of the relief of PSS and providing a potential mechanism explanation of the relief of PSS.

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“…clinical scales assessments(Sebastián-Romagosa et al, 2019) Comment on current/potential applications: LC is being used as a relevant parameter to evaluate new technology-based approaches for stroke rehabilitation(Sebastian-Romagosa et al, 2020), such as combined action observation-and motor imagery-based using BCI(Yuan et al, 2020;Rungsirisilp and Wongsawat, 2021) (not limited to EEG-based assessments;Yuan et al, 2020), functional electrical stimulation…”

mentioning

confidence: 99%

Neuromechanical Biomarkers for Robotic Neurorehabilitation

et al. 2021

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One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

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Laterality Coefficient: An EEG parameter related with the functional improvement in stroke patients

Cited by 5 publications

References 12 publications

Source-Based EEG Neurofeedback for Sustained Motor Imagery of a Single Leg

Source-Based EEG Neurofeedback for Sustained Motor Imagery of a Single Leg

Brain oscillatory activity correlates with the relief of post-stroke spasticity following focal vibration

Neuromechanical Biomarkers for Robotic Neurorehabilitation

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