Surface electromyography and electroencephalography processing in dysarthric patients for verbal commands or speaking intention characterization. (April 2021)

• Record Type: Journal Article
• Authors: Galego, Juliet Sánchez; Casas, Omar Valle; Rossato, Daniele; Simões, Alexandre; Balbinot, Alexandre
• Is Part Of: Measurement. Volume 175(2021)
• Publication Date: 2021-04
• Subjects: Electromyography -- Electroencephalography -- Assistive Technologies -- Bayesian Networks -- Support Vector Machine; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109147
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Studied and evaluated signals from dysarthric and non-dysarthric subjects. SVM method for sEMG and NB method for EEG classification were synchronized in time. Mental tasks identification in healthy and dysarthric subjects was achieved. Factorial experiment with healthy and dysarthric subjects was carried out. Right, Left, Forward and Backward commands were efficiently identified. Abstract: Assistance systems for people with Cerebrovascular Accident (CVA) after-effects such as dysarthria are gaining interest due to the increasing proportion of the population with these disorders. This paper proposes the acquisition and processing of surface Electromyography (sEMG) on the facial muscles activated during the process of speaking and Electroencephalography (EEG), synchronized in time by means of an audio file. Data was collected in seven healthy volunteers and seven patients with dysarthria at the Physiotherapy Department of the Porto Alegre Clinical Hospital. The main objective is to classify the biosignals for verbal commands established by Support Vector Machine (SVM) method for the sEMG and Naïve Bayes (NB) method for the EEG. Both methods were compared with the Linear Discriminant Analysis (LDA) method. Extracted features of sEMG signal were: standard deviation, arithmetic mean, skewness, kurtosis and RMS. For the EEG signal, characteristics were extracted in frequency domain such as: minimum, maximum, average and standard deviation, and skewness and kurtosis, in the time domain. As part of the processing method, Common Spatial Pattern (CSP) filters were employed in order to increase the discriminating activity between motion classes in the EEG signal. Mental task identification in healthy and dysarthric subjects reached an accuracy of 77.6–80.8%.