ReviewFrontiers in human neuroscience2022
Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.
Review in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 21 citations in OpenAlex.
- Advances in mechanisms of neuroplasticity induced by multimodal closed-loop brain-computer interfaces after stroke.Frontiers in human neuroscience · 2026Review
- A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces.Scientific data · 2025Article
- Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.Frontiers in human neuroscience · 2025Review
- 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
- Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance.Sensors (Basel, Switzerland) · 2023Article
- Detection of motor imagery based on short-term entropy of time-frequency representations.Biomedical engineering online · 2023Article
- Case-Based and Quantum Classification for ERP-Based Brain-Computer Interfaces.Brain sciences · 2023Article
- Toward passive BCI: asynchronous decoding of neural responses to direction- and angle-specific perturbations during a simulated cockpit scenario.Scientific reports · 2022Article
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Authors and funding
11 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project "Feel Your Reach". In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram (EEG). In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the "Feel Your Reach" framework to people with cervical spinal cord injury and evaluate the decoders' performance while participants
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