Abstract
In this study, we investigate the neurophysiological signature of the interacting processes which lead to a single reach-and-grasp movement imagination (MI). While performing this task, the human healthy participants could either define their movement targets according to an external cue, or through an internal selection process. After defining their target, they could start the MI whenever they wanted. We recorded high density electroencephalographic (EEG) activity and investigated two neural correlates: the event-related potentials (ERPs) associated with the target selection, which reflect the perceptual and cognitive processes prior to the MI, and the movement-related cortical potentials (MRCPs), associated with the planning of the self-paced MI. We found differences in frontal and parietal areas between the late ERP components related to the internally-driven selection and the externally-cued process. Furthermore, we could reliably estimate the MI onset of the self-paced task. Next, we extracted MRCP features around the MI onset to train classifiers of movement vs. rest directly on self-paced MI data. We attained performance significantly higher than chance level for both time-locked and asynchronous classification. These findings contribute to the development of more intuitive brain-computer interfaces in which movement targets are defined internally and the movements are self-paced.
Highlights
Brain-computer interfaces (BCIs) provide a way of interaction with the external world by replacing the brain’s neuromuscular output pathways
A run was composed of trials in which the subjects were instructed to define the movement target according to the condition and perform the movement imagination (MI) of a single reach-and-grasp directed towards that target, in a self-paced manner (Fig. 1b)
The glasses with water in both externally-cued condition (EC) and internally-driven condition (IDII) conditions were pseudo-randomly positioned and all glass positions were covered with the same frequency
Summary
Brain-computer interfaces (BCIs) provide a way of interaction with the external world by replacing the brain’s neuromuscular output pathways. On a self-paced MI task, it is not possible to align the EEG activity to a cue, nor to a measurable myographic or kinematic onset This alignment is important if one wants to extract MRCP features to train movement detectors (i.e. classification of movement vs rest). We conducted an experiment in which healthy participants performed a self-paced single reach-and-grasp MI towards one of five targets displayed on a screen The participants defined their target either by the direct influence of a cue, or by an internally-driven selection process. Understanding the neural processes of MI tasks, in which movement targets are defined internally and the movements are self-paced, can help us to develop more intuitive BCIs
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