ArticleScientific reports2020
An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Past, Present, and Future of EEG-Based BCI Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- Tremor-Suppression Orthoses for the Upper Limb: Current Developments and Future Challenges.Frontiers in human neuroscience · 2021Pooled it
- Study of intelligent home environment system based on big data and improved k-means algorithm.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
- A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.Neuroscience bulletin · 2024Article
- Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research.Neuroscience and biobehavioral reviews · 2024Review
- Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.Journal of neuroengineering and rehabilitation · 2024Article
- User- and Speed-Independent Slope Estimation for Lower-Extremity Wearable Robots.Annals of biomedical engineering · 2024Review
- End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging.PLoS computational biology · 2024Article
- The decoder design and performance comparative analysis for closed-loop brain-machine interface system.Cognitive neurodynamics · 2024Article
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- A review of combined functional neuroimaging and motion capture for motor rehabilitation.Journal of neuroengineering and rehabilitation · 2024Review
- Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification.Sensors (Basel, Switzerland) · 2023Article
- The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities.Current environmental health reports · 2023Review
- Survey on the research direction of EEG-based signal processing.Frontiers in neuroscience · 2023Review
- DeePay: deep learning decodes EEG to predict consumer's willingness to pay for neuromarketing.Frontiers in human neuroscience · 2023Article
- Combined Feature Extraction and Random Forest for Laser Self-Mixing Vibration Measurement without Determining Feedback Intensity.Sensors (Basel, Switzerland) · 2022Article
- [Research on gait recognition and prediction based on optimized machine learning algorithm].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- A Motor Imagery Signals Classification MethodFrontiers in neuroscience · 2022Article
- A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.Frontiers in human neuroscience · 2022Article
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5 authors.
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Abstract
Previous studies of Brain Computer Interfaces (BCI) based on scalp electroencephalography (EEG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. In this computational study, we investigated offline decoding analysis with different models and conditions to assess how they influence the performance and stability of the decoder. Specifically, we conducted three computational decoding experiments that investigated decoding accuracy: (1) based on delta band time-domain features, (2) when downsampling data, (3) of different frequency band features. In each experiment, eight different decoder algorithms were compared including the current state-of-the-art. Different tap sizes (sample window sizes) were also evaluated for a real-time applicability assessment. A feature of importance analysis was conducted to ascertain which features were most relevant for decoding; moreover, the stability to perturbations was assessed to quantify the robustness of the methods. Results indicated that generally the Gated Recurrent Unit (GRU) and Quasi Recurrent Neural Network (QRNN) outperformed other methods in terms of decoding accuracy and stability. Previous state-of-the-art Unscented Kalman Filter (UKF) still outperformed other decoders when using smaller tap sizes, with fast convergence in performance, but occurred at a cost to noise vulnerability. Downsampling and the inclusion of other frequency band features yielded overall improvement in performance. The results suggest that neural network-based decoders with downsampling or a wide range of frequency band features could not only improve decoder performance but also robustness with applications for stable use of BCIs.
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