TY - JOUR
T1 - On the use of power-based connectivity between EEG and sEMG signals for three-weight classification during object manipulation tasks
AU - Guerrero-Mendez, C. D.
AU - Blanco-Díaz, C. F.
AU - Duarte-Gonzalez, M. E.
AU - Bastos-Filho, T. F.
AU - Jaramillo-Isaza, S.
AU - Ruiz-Olaya, A. F.
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85186872180&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85186872180&partnerID=8YFLogxK
U2 - 10.1007/s42600-023-00333-4
DO - 10.1007/s42600-023-00333-4
M3 - Article
AN - SCOPUS:85186872180
SN - 2446-4732
JO - Research on Biomedical Engineering
JF - Research on Biomedical Engineering
ER -