ArticleFrontiers in neurorobotics2020
Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers.
Article in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Multi-phase and Multi-mode Gait Recognition with Few Channels and Few Features Based on the Fusion of sEMG and Acceleration.Annals of biomedical engineering · 2026Article
- The classification of walking and phases of gait using EEG: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Molecular and Structural Changes, and Skeletal Muscle Strength and Endurance in Chronic Obstructive Pulmonary Disease and Interstitial Lung Disease: Practical Applications of Assessment and Management.Bioengineering (Basel, Switzerland) · 2026Review
- A hybrid EMG-EEG interface for robust intention detection and fatigue-adaptive control of an elbow rehabilitation robot.Scientific reports · 2025Article
- Multimodal closed-loop strategies for gait recovery after spinal cord injury and stroke via the integration of robotics and neuromodulation.Frontiers in neuroscience · 2025Review
- AMEEGNet: attention-based multiscale EEGNet for effective motor imagery EEG decoding.Frontiers in neurorobotics · 2025Article
- Multimodal decoding of error processing in a virtual reality flight simulation.Scientific reports · 2024Article
- A hybrid brain-muscle-machine interface for stroke rehabilitation: Usability and functionality validation in a 2-week intensive intervention.Frontiers in bioengineering and biotechnology · 2024Article
- Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy.Bioengineering (Basel, Switzerland) · 2023Review
- Editorial: Hybrid brain-robot interfaces for enhancing mobility.Frontiers in neurorobotics · 2023Article
- Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.Frontiers in neuroscience · 2023Article
- EEG-EMG coupling as a hybrid method for steering detection in car driving settings.Cognitive neurodynamics · 2022Article
- Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model.Frontiers in neuroscience · 2022Article
- Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research.Frontiers in neurorobotics · 2022Review
- A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.Frontiers in human neuroscience · 2022Article
- Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism.Frontiers in neuroscience · 2022Article
- Experimental Protocol to Assess Neuromuscular Plasticity Induced by an Exoskeleton Training Session.Methods and protocols · 2021Article
- A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting.Bioengineering (Basel, Switzerland) · 2021Article
- Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases.Frontiers in neuroscience · 2021Review
- Evaluating Convolutional Neural Networks as a Method of EEG-EMG Fusion.Frontiers in neurorobotics · 2021Article
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Authors and funding
6 authors.
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Abstract
Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interfaces (hHMIs) represent a very recent solution to enhance the performance of single-signal approaches. These are classification approaches that combine multiple human-machine interfaces, normally including at least one BCI with other biosignals, such as the electromyography (EMG). However, their use for the decoding of gait activity is still limited. In this work, we propose and evaluate a hybrid human-machine interface (hHMI) to decode walking phases of both legs from the Bayesian fusion of EEG and EMG signals. The proposed hHMI significantly outperforms its single-signal counterparts, by providing high and stable performance even when the reliability of the muscular activity is compromised temporarily (e.g., fatigue) or permanently (e.g., weakness). Indeed, the hybrid approach shows a smooth degradation of classification performance after temporary EMG alteration, with more than 75% of accuracy at 30% of EMG amplitude, with respect to the EMG classifier whose performance decreases below 60% of accuracy. Moreover, the fusion of EEG and EMG information helps keeping a stable recognition rate of each gait phase of more than 80% independently on the permanent level of EMG degradation. From our study and findings from the literature, we suggest that the use of hybrid interfaces may be the key to enhance the usability of technologies restoring or assisting the locomotion on a wider population of patients in clinical applications and outside the laboratory environment.
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