Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.

Autor: Blanco-Diaz CF; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil. cblanco88@uan.edu.co., Guerrero-Mendez CD; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil., de Andrade RM; Graduate Program in Mechanical Engineering, Federal University of Espirito Santo, Vitoria, Brazil., Badue C; Department of Informatics, Federal University of Espirito Santo, Vitoria, Brazil., De Souza AF; Department of Informatics, Federal University of Espirito Santo, Vitoria, Brazil., Delisle-Rodriguez D; Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Macaiba, RN, Brazil., Bastos-Filho T; Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil.
Jazyk: angličtina
Zdroj: Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Dec; Vol. 62 (12), pp. 3763-3779. Date of Electronic Publication: 2024 Jul 19.
DOI: 10.1007/s11517-024-03147-3
Abstrakt: Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.
Competing Interests: Declarations Competing interests The authors declare no competing interests.
(© 2024. International Federation for Medical and Biological Engineering.)
Databáze: MEDLINE
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