ArticleJournal of neuroengineering and rehabilitation2017
Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques.
Article in Journal of neuroengineering and rehabilitation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Exploring Kinematics Information Decoding from EEG Slow Cortical Potentials During Movement Imagination and Observation.Sensors (Basel, Switzerland) · 2026Article
- Inferring Arm Movement Direction from EEG Signals Using Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Identification of key upper-limb muscles during a standardized reach-to-grasp task toward simplified clinical protocols.Frontiers in bioengineering and biotechnology · 2026Article
- Electroencephalography Enables Continuous Decoding of Hand Motion Angles in Polar Coordinates.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Test-retest reliability of kinematic and EEG low-beta spectral features in a robot-based arm movement task.Biomedical physics & engineering express · 2025Article
- Article
- Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing.Scientific reports · 2024Article
- Transfer Learning in Trajectory Decoding: Sensor or Source Space?Sensors (Basel, Switzerland) · 2023Article
- Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability.Journal of neuroengineering and rehabilitation · 2023Review
- A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.Frontiers in neuroscience · 2023Review
- Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.Frontiers in neuroscience · 2023Article
- Continuous Decoding of Hand Movement From EEG Signals Using Phase-Based Connectivity Features.Frontiers in human neuroscience · 2022Article
- Applying Dimensionality Reduction Techniques in Source-Space ElectroencephalographyFrontiers in human neuroscience · 2022Article
- Using Non-linear Dynamics of EEG Signals to Classify Primary Hand Movement Intent Under Opposite Hand Movement.Frontiers in neurorobotics · 2022Article
- Low-Cost Robotic Guide Based on a Motor Imagery Brain-Computer Interface for Arm Assisted Rehabilitation.International journal of environmental research and public health · 2020Article
- Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework.Sensors (Basel, Switzerland) · 2019Article
- Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury.Scientific reports · 2019Article
- Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems : Guidelines Derived from Simulation and Real-World Data.Neuroinformatics · 2019Article
- Characteristics of Kinematic Parameters in Decoding Intended Reaching Movements Using Electroencephalography (EEG).Frontiers in neuroscience · 2019Article
- Classification of Movement Intention Using Independent Components of Premovement EEG.Frontiers in human neuroscience · 2019Article
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4 authors.
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Abstract
backgroundOne of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements.
methodsThe decoding of arm movement was performed using a multidimensional linear regression. Passive movements were analyzed using the same methodology to study the influence of proprioceptive sensory feedback in the decoding. Finally, we evaluated the possible advantages of classifying reaching targets, instead of continuous trajectories.
resultsThe results showed that arm movement decoding was significantly above chance levels. The results also indicated that EEG slow cortical potentials carry significant information to decode active center-out movements. The classification of reached targets allowed obtaining the same conclusions with a very high accuracy. Additionally, the low decoding performance obtained from passive movements suggests that discriminant modulations of low-frequency neural activity are mainly related to the execution of movement while proprioceptive feedback is not sufficient to decode upper limb kinematics.
conclusionsThis paper contributes to the assessment of feasibility of using linear regression methods to decode upper limb kinematics from EEG signals. From our findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas. We also show that the classification of reached targets from decoding approaches may be a more suitable real-time methodology than a direct decoding of hand position.
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