EEG Biomarkers Related With the Functional State of Stroke Patients

Abstract

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.

Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients—A Feasibility Study

“…Fourth, while we used a broad range of scales, different scales and assessment methods could yield different results. Fifth, emerging qEEG approaches (Sebastián-Romagosa et al, 2020) or other analysis tools could also provide further insight.…”

Intensity and Dose of Neuromuscular Electrical Stimulation Influence Sensorimotor Cortical Excitability

“…The sensorimotor oscillations that mainly comprise the rolandic alpha [(7–13) Hz] and beta [(14–30) Hz] rhythms have been thoroughly used to study cortical involvement during sensorimotor tasks ( Ramos-Murguialday and Birbaumer, 2015 ; López-Larraz et al, 2018 ), being quantified as the event-related (de)synchronization (ERD/ERS) ( Pfurtscheller and Lopes da Silva, 1999 ). Furthermore, it has also been used as a feature for neuromodulation of sensorimotor neural network via proprioception and haptics ( Ray et al, 2020 ; Sebastián-Romagosa et al, 2020 ). To date, only a few studies have reported how oscillatory activity measured with EEG is modulated by NMES ( Vidaurre et al, 2016 , 2019 ; Tu-Chan et al, 2017 ; Corbet et al, 2018 ).…”

An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors

“…Abnormalities such as a decrease in rapid frequencies are found when cerebral blood flow (CBF) is diminished during ischemia [13]. In stroke patients, EEG power is significantly impacted, with an increase in delta (1-4 Hz) power accompanied by decreases in alpha (8)(9)(10)(11)(12)(13)(14) and beta (14-30 Hz) power, resulting in a diffuse slow-wave EEG pattern. Recently, few studies have emphasized on the usability of EEG to support and understand brain changes at rehab centers.…”

“…Recently, few studies have emphasized on the usability of EEG to support and understand brain changes at rehab centers. ese tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses [14]. us, we have made an attempt to have a real-time monitoring of few of the parameter with the use of an IoT-based system.…”