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

Stroke is one of the most prevalent pathologies around the world, with severe effects to the motor and sensory system that hinder the daily living activities. Brain Computer Interface (BCI) systems can help the stroke survivors to relearn the lost movements inducing neuroplastic changes in the affected motor cortex. The event-related synchronization and even-related desynchronization (ERD/ERS) calculated with the brain signals during the motor imagery tasks, could be related with the functional state of the stroke patients. The Laterality Coefficient (LC) is a parameter calculated using the ERD/ERS changes in the mu wave. The goal of this study is to test how useful the LC is for the functional assessment of stroke patients. Fifteen stroke patients with hemiparesis in the upper limbs have been enrolled on this study and performed 25 sessions of BCI therapy. All of them performed assessment sessions before and after the therapy. The results showed significant correlation between the LC and functional scales, like the Fugl-Meyer Assessment (FMA) or Box and Block Test (BBT). The LC could be a good biomarker for the functional assessment in stroke patients.

[1]  Brendan Z. Allison,et al.  recoveriX: A new BCI-based technology for persons with stroke , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[2]  G. Pfurtscheller,et al.  ERD/ERS patterns reflecting sensorimotor activation and deactivation. , 2006, Progress in brain research.

[3]  G Pfurtscheller,et al.  Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data , 2002, Clinical Neurophysiology.

[4]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[5]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[6]  G. Pfurtscheller,et al.  Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. , 1979, Electroencephalography and clinical neurophysiology.

[7]  B. Dobkin Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation , 2007, The Journal of physiology.

[8]  Jong-Hwan Lee,et al.  EEG response varies with lesion location in patients with chronic stroke , 2016, Journal of NeuroEngineering and Rehabilitation.

[9]  S. Black,et al.  The Fugl-Meyer Assessment of Motor Recovery after Stroke: A Critical Review of Its Measurement Properties , 2002, Neurorehabilitation and neural repair.

[10]  Alessandro Scano,et al.  A Multiparameter Approach to Evaluate Post-Stroke Patients: An Application on Robotic Rehabilitation , 2018, Applied Sciences.

[11]  C. Neuper,et al.  Relationship Between Electrical Brain Responses to Motor Imagery and Motor Impairment in Stroke , 2012, Stroke.

[12]  J R Wolpaw,et al.  EEG-Based Brain-Computer Interfaces. , 2017, Current opinion in biomedical engineering.