TY - GEN
T1 - Enhancing Classification of Grasping Tasks Using Hybrid EEG-sEMG Features
AU - Ruiz-Olaya, A. F.
AU - Blanco-Diaz, C. F.
AU - Guerrero-Mendez, C. D.
AU - Bastos-Filho, T. F.
AU - Jaramillo-Isaza, S.
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - Systems based on multimodal Human-Machine Interface (HMI) propose a significant advance in rehabilitation engineering. This advance is due to their capacity to acquire information from different sources, allowing greater patient adaptability to the system in daily activities, and a higher accuracy rate in decoding the motor intention. Nowadays, there is the challenge of implementing better techniques to increase the classification rate of HMIs. This paper describes the implementation of two techniques based on a hybrid sEMG and EEG method to improve the classification of three types of grasp (spherical, cylindrical, and lateral). The proposed methods were evaluated on a public database of 25 subjects by using 11 EEG channels and 6 EMG channels, where the signals were segmented into five different time windows. The results show maximum accuracies above 70% for detecting grasping versus resting movements and accuracy above 54% for differentiation between each grasping movement. This performance improvement is obtained in time window between 1 and 2.5 s. The results allow us to conclude that the use of multimodal information allows an improvement in the identification of grasping tasks, and identifies a suitable time window that can be used to improve the performance of a multimodal system in real-time.
AB - Systems based on multimodal Human-Machine Interface (HMI) propose a significant advance in rehabilitation engineering. This advance is due to their capacity to acquire information from different sources, allowing greater patient adaptability to the system in daily activities, and a higher accuracy rate in decoding the motor intention. Nowadays, there is the challenge of implementing better techniques to increase the classification rate of HMIs. This paper describes the implementation of two techniques based on a hybrid sEMG and EEG method to improve the classification of three types of grasp (spherical, cylindrical, and lateral). The proposed methods were evaluated on a public database of 25 subjects by using 11 EEG channels and 6 EMG channels, where the signals were segmented into five different time windows. The results show maximum accuracies above 70% for detecting grasping versus resting movements and accuracy above 54% for differentiation between each grasping movement. This performance improvement is obtained in time window between 1 and 2.5 s. The results allow us to conclude that the use of multimodal information allows an improvement in the identification of grasping tasks, and identifies a suitable time window that can be used to improve the performance of a multimodal system in real-time.
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U2 - 10.1007/978-3-031-49407-9_19
DO - 10.1007/978-3-031-49407-9_19
M3 - Conference contribution
AN - SCOPUS:85181777454
SN - 9783031494062
T3 - IFMBE Proceedings
SP - 182
EP - 191
BT - 9th Latin American Congress on Biomedical Engineering and 28th Brazilian Congress on Biomedical Engineering - Proceedings of CLAIB and CBEB 2022—Volume 3
A2 - Marques, Jefferson Luiz Brum
A2 - Rodrigues, Cesar Ramos
A2 - Suzuki, Daniela Ota Hisayasu
A2 - García Ojeda, Renato
A2 - Marino Neto, José
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022
Y2 - 24 October 2022 through 28 October 2022
ER -