ArticleSensors (Basel, Switzerland)2017
A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 51 citations in OpenAlex.
- Motion Neural Monitoring Based on Flexible Materials: From Signal Acquisition to Training Enhancement.ACS omega · 2026Review
- Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications.Journal of advanced research · 2026Review
- Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques.Biomimetics (Basel, Switzerland) · 2025Review
- Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals.Frontiers in bioengineering and biotechnology · 2024Article
- EEG-EMG coupling as a hybrid method for steering detection in car driving settings.Cognitive neurodynamics · 2022Article
- A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.Frontiers in human neuroscience · 2022Article
- Biopotential Signal Monitoring Systems in Rehabilitation: A Review.Sensors (Basel, Switzerland) · 2021Review
- Embedded Brain Computer Interface: State-of-the-Art in Research.Sensors (Basel, Switzerland) · 2021Review
- Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques.Sensors (Basel, Switzerland) · 2021Article
- Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios.Frontiers in neuroergonomics · 2021Article
- Evaluating Convolutional Neural Networks as a Method of EEG-EMG Fusion.Frontiers in neurorobotics · 2021Article
- Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection-Relevance for Neuroscience and Clinical Applications.Frontiers in neuroscience · 2018Article
- Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient's upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.