Quantitative EEG (qEEG) is a useful tool to extract features from the EEG signals and thereby help clinicians understand each patient’s clinical state. Recent studies explored new methods to process and analyze brain signals acquired by conventional techniques like electroencephalography (EEG) ( Kanda et al., 2009 Leon-Carrion et al., 2009 Foreman and Claassen, 2012 Rabiller et al., 2015 Wu et al., 2016) or magnetoencephalography (MEG) ( Mäkelä et al., 2015 Ikkai et al., 2016 Krauss et al., 2018). Diagnostic imaging tools like Computational Tomography (CT) or Functional magnetic resonance imaging (fMRI) are normally used to evaluate brain damage in the acute and sub-acute phases, offering valuable information about the diagnostic and functional prognosis for each case. The clinical consequences after a stroke vary, depending largely on the location and the cause of the damage ( Prabhakaran et al., 2008). Stroke can cause devastating effects in survivors, including severe motor and sensory impairments that hinder their activities of daily living ( Kim et al., 2020). Stroke is one of the most prevalent pathologies around the world. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (ΔFMA = 1 median, P = 0.002).Ĭonclusion: The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. Other important significant correlations between LC and functional scales were observed. 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). The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852).
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. Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences ( P = 0.027). No significant differences were found between the healthy group and the Cortical group ( P = 0.505). 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). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps.
Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. Methods: Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. 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. Introduction: Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals.
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