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Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals.
Blanco-Díaz, Cristian Felipe; Guerrero-Mendez, Cristian David; Delisle-Rodriguez, Denis; Jaramillo-Isaza, Sebastián; Ruiz-Olaya, Andrés Felipe; Frizera-Neto, Anselmo; Ferreira de Souza, Alberto; Bastos-Filho, Teodiano.
Afiliación
  • Blanco-Díaz CF; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil.
  • Guerrero-Mendez CD; Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia.
  • Delisle-Rodriguez D; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil.
  • Jaramillo-Isaza S; Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia.
  • Ruiz-Olaya AF; Edmond and Lily Safra International Institute of Neurosciences, Macaíba, Brazil.
  • Frizera-Neto A; Universidad del Rosario, School of Medicine and Health Sciences, Bogotá, Colombia.
  • Ferreira de Souza A; Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia.
  • Bastos-Filho T; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil.
Biomed Phys Eng Express ; 10(3)2024 Mar 08.
Article en En | MEDLINE | ID: mdl-38417162
ABSTRACT
Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Accidente Cerebrovascular / Interfaces Cerebro-Computador Límite: Humans Idioma: En Revista: Biomed Phys Eng Express Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Accidente Cerebrovascular / Interfaces Cerebro-Computador Límite: Humans Idioma: En Revista: Biomed Phys Eng Express Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Reino Unido