Work

Classification of opening/closing hand motor imagery induced by left and right robotic gloves through EEG signals

Year: 2024

Type: article

Abstract: This study presents a novel strategy for classifying Motor Imagery (MI) related to hand opening/closing actions using electroencephalography signals. This approach combines the passive motion induced by a robotic glove and action observation. Two groups of subjects executed a protocol based on left and right hand movement MI to address this. Subsequently, spectral features were used on $mu$ and $beta$ bands, and machine-learning algorithms were used for classification. The results showed better performance for right-hand motion recognition using k-Nearest Neighbors (kNN), which achieved the highest performance metrics of 0.71, 0.76, and 0.28 for Accuracy (ACC), true positive rate, and false positive rate, respectively. These findings demonstrate the feasibility of the proposed methodology for improving the recognition of MI tasks of the same limb, which can contribute to the design of more robust brain-computer interfaces for the enhancement of rehabilitation therapy for post-stroke patients.

Source: Transactions on Energy Systems and Engineering Applications

DOI: 10.32397/tesea.vol5.n2.579

Authors: Aura Ximena Gonzalez Cely, Cristian Felipe Blanco-Diaz, Cristian David Guerrero Mendez, Ana Cecilia Villa Parra, Teodiano Freire Bastos-Filho

Institutions: Universidade Federal do Espírito Santo, Politecnica Salesiana University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa e Inovação do Espírito Santo