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

Blanco-Diaz, Cristian Felipe; Guerrero-Mendez, Cristian David; de Andrade, Rafhael Milanezi; Badue, Claudine; De Souza, Alberto Ferreira; Delisle-Rodriguez, Denis; Bastos-Filho, Teodiano

doi:10.1007/s11517-024-03147-3

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2024

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Cited by 2 publications

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Deep Learning Approach for EEG Classification in Lower-Limb Movement Phases: Towards Enhanced Brain-Computer Interface Control

Blanco-Diaz,

David Guerrero-Mendez,

Furtado

et al. 2024

2024 20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

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Deep Learning Approach for EEG Classification in Lower-Limb Movement Phases: Towards Enhanced Brain-Computer Interface Control

Blanco-Diaz,

David Guerrero-Mendez,

Furtado

et al. 2024

2024 20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

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EEG-Based Mobile Robot Control Using Deep Learning and ROS Integration

Ghinoiu,

Vlădăreanu,

Travediu

et al. 2024

Technologies

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Efficient BCIs (Brain-Computer Interfaces) harnessing EEG (Electroencephalography) have shown potential in controlling mobile robots, also presenting new possibilities for assistive technologies. This study explores the integration of advanced deep learning models—ASTGCN, EEGNetv4, and a combined CNN-LSTM architecture—with ROS (Robot Operating System) to control a two-wheeled mobile robot. The models were trained using a published EEG dataset, which includes signals from subjects performing thought-based tasks. Each model was evaluated based on its accuracy, F1-score, and latency. The CNN-LSTM architecture model exhibited the best performance on the cross-subject strategy with an accuracy of 88.5%, demonstrating significant potential for real-time applications. Integration with ROS was facilitated through a custom middleware, enabling seamless translation of neural commands into robot movements. The findings indicate that the CNN-LSTM model not only outperforms existing EEG-based systems in terms of accuracy but also underscores the practical feasibility of implementing such systems in real-world scenarios. Considering its efficacy, CNN-LSTM shows a great potential for assistive technology in the future. This research contributes to the development of a more intuitive and accessible robotic control system, potentially enhancing the quality of life for individuals with mobility impairments.

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

Cited by 2 publications

References 64 publications

Deep Learning Approach for EEG Classification in Lower-Limb Movement Phases: Towards Enhanced Brain-Computer Interface Control

Deep Learning Approach for EEG Classification in Lower-Limb Movement Phases: Towards Enhanced Brain-Computer Interface Control

EEG-Based Mobile Robot Control Using Deep Learning and ROS Integration

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