On the Comparison of Multilayer Perceptron and Extreme Learning Machine for Pedaling Recognition Using EEG

Cristian Felipe Blanco-Díaz, Cristian David Guerrero-Mendez, Teodiano Bastos-Filho, Andrés Felipe Ruiz-Olaya, Sebastián Jaramillo-Isaza

Producción científica: Capítulo en Libro/ReporteContribución a la conferencia

Resumen

Brain-Computer Interfaces (BCIs) have gained significant attention in recent years for their role in connecting individuals with external devices using neural signals. Electroencephalography (EEG)-based BCIs, in combination with Motorized Mini Exercise Bikes (MMEBs), have emerged as promising tools for post-stroke patient rehabilitation. Nevertheless, the EEG signal-to-noise ratio (SNR) remains a challenge, susceptible to interference from physical and mental artifacts, thereby compromising the accuracy of motor task recognition, such as pedaling. This limitation hampers the effectiveness of lower-limb rehabilitation devices. In this study, we propose a comparative study which uses Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM) to accurately identify from EEG signals when a subject is engaged in pedaling tasks. The results outperform those reported in the literature, achieving a remarkable Accuracy of 0.97 and a negligible False Positive Rate close to zero, resulting in an overall performance of 0.77 and 0.24, respectively. Additionally, we conducted an evaluation of four distinct frequency bands during the filtering process, with the most promising outcomes achieved within the 3 to 7 Hz frequency band. These findings support the conclusion that our proposed methodology is well-suited for the real-time detection of lower-limb tasks using EEG signals, thus offering potential applications in the control of robotic BCIs for rehabilitation purposes.

Idioma originalInglés estadounidense
Título de la publicación alojadaApplications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers
EditoresAlvaro David Orjuela-Cañón, Jesus A Lopez, Julián David Arias-Londoño
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas19-29
Número de páginas11
ISBN (versión impresa)9783031484148
DOI
EstadoPublicada - 2024
Evento6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia
Duración: jul. 26 2023jul. 28 2023

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1865 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
País/TerritorioColombia
CiudadBogota
Período7/26/237/28/23

Áreas temáticas de ASJC Scopus

  • Ciencia de la Computación General
  • Matemáticas General

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