Abstract
Purpose
Brain-machine interfaces (BMIs) have been used for motor rehabilitation of complex movements, such as those based on object manipulation. However, task identification during these movements remains a challenge in the scientific community. Recent research has suggested that corticomuscular connectivity may enhance the BMIs’ performance in task identification. Therefore, this study presents an algorithm that uses power-based connectivity (PBC) as a descriptor to improve the classification of three different weights during object manipulation which was compared with power spectral density (PSD) benchmark algorithm.
Methods
Signals from three electroencephalography (EEG) and five surface electromyography (sEMG) electrodes were analyzed using Welch’s estimator to determine the PSD features and then correlated using Spearman’s correlation. The performance was evaluated using four classifiers that are widely applied in brain-computer interfaces (BCIs). Furthermore, different frequency bands and the influence of EEG and sEMG channels on object weight identification were evaluated using accuracy, F-score, and computational cost metrics.
Results
The proposed algorithm significantly outperforms (\(p<\)0.05) the standard method based on PSD, with a difference in accuracy of 19.15% and F-score of 10.40% and obtaining a computational cost of 6 s less.
Conclusions
These findings demonstrate the promising potential of the PBC method for object weight identification in complex tasks. The implementation of such algorithms can lead to significant improvements in the effectiveness of BMIs for object manipulation, with potential benefits for rehabilitation and other applications.
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Code availability is temporarily not allowed by the authors.
Data Availability
Data set description is mentioned in the EEG-sEMG dataset section.
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Acknowledgements
The authors would like to thank the Antonio Nariño University (UAN) and the Bioengineering Research Group for their support in the development of this work and also thank the Universidad del Rosario (UR), the Federal University of Espírito Santo (UFES/Brazil), and Fundação de Amparo á Pesquisa e Inovação do Espírito Santo/Instituto de Inteligência Computacional Aplicada (FAPES/I2CA) by the MSc scholarships awarded to the first two authors.
Funding
This work was supported by Antonio Nariño University (UAN/ Colombia) under the project number 2021020 “Model based on multimodal EEG-sEMG information to improve motion intention decoding for the control of a BCI system.” Federal University of Espírito Santo (UFES/Brazil) and FAPES/I2CA (Resolution N\(^{\circ }\) 285/2021) by the MSc. scholarships awarded to the first two authors.
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Guerrero-Mendez, C., Blanco-Díaz, C.F., Duarte-Gonzalez, M.E. et al. On the use of power-based connectivity between EEG and sEMG signals for three-weight classification during object manipulation tasks. Res. Biomed. Eng. 40, 99–116 (2024). https://doi.org/10.1007/s42600-023-00333-4
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DOI: https://doi.org/10.1007/s42600-023-00333-4