Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals

Joana Pereira, Reinmar Kobler, Patrick Ofner, Andreas Schwarz, Gernot R Müller-Putz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain-computer interfaces (BCIs) for people with motor disabilities.Objective.The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG features, concretely on movement-related cortical potentials. The paradigm must be easily transferable to people without any residual upper-limb movement function and the BCI must be independent of upper-limb movement onset measurements and external cues.Approach. In a study with non-disabled participants, we evaluated a novel BCI paradigm to detect self-initiated reach-and-grasp movements. Two experimental conditions were involved. In one condition, participants performed reach-and-grasp movements to a target and simultaneously shifted their gaze towards it. In a control condition, participants solely shifted their gaze towards the target (oculomotor task). The participants freely decided when to initiate the tasks. After eye artefact correction, the EEG signals were time-locked to the saccade onset and the resulting amplitude features were exploited on a hierarchical classification approach to detect movement asynchronously.Main results. With regards to BCI performance, 54.1% (14.4% SD) of the movements were correctly identified, and all participants achieved a performance above chance-level (around 12%). An average of 21.5% (14.1% SD) of the oculomotor tasks were falsely detected as upper-limb movement. In an additional rest condition, 1.7 (1.6 SD) false positives per minute were measured. Through source imaging, movement information was mapped to sensorimotor, posterior parietal and occipital areas.Significance. We present a novel approach for movement detection using EEG signals which does not rely on upper-limb movement onset measurements or on the presentation of external cues. The participants' behaviour closely matches the natural behaviour during goal-directed reach-and-grasp movements, which also constitutes an advantage with respect to current BCI protocols.

Original languageEnglish
Article number046095
JournalJournal of Neural Engineering
Issue number4
Publication statusPublished - Aug 2021


  • Brain-Computer Interfaces
  • Electroencephalography
  • Evoked Potentials
  • Hand Strength
  • Humans
  • Movement
  • electroencephalography
  • low-frequency time-domain
  • movement-related cortical potentials
  • brain-computer interface
  • movement detection
  • reach-and-grasp

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Biomedical Engineering

Fields of Expertise

  • Human- & Biotechnology


Dive into the research topics of 'Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals'. Together they form a unique fingerprint.

Cite this