Abstract
How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.
Highlights
Understanding how the human brain encodes movements is essential for the development of an intuitive and natural control of a motor neuroprosthesis or a robotic arm
The mov-vs-mov average classification accuracy over all subjects reached a maximum of 42% (9% standard deviation) at 0.13 s after movement onset and the mov-vs-rest average classification accuracy reached a maximum of 81% (7% standard deviation) at movement onset (0.0 s)
Classification accuracies are statistically significant above 24% and 65% for a single subject, and above 18% and 54% for the average (α = 0.05, adjusted wald interval [40,41], Bonferroni corrected for the length of the analyzed time window)
Summary
Understanding how the human brain encodes movements is essential for the development of an intuitive and natural control of a motor neuroprosthesis or a robotic arm. Neuroprostheses based on functional electrical stimulation (FES) [1] can be already used to restore movement function of spinal cord injured (SCI) persons [2] These neuroprostheses often rely on a shoulder joystick as a control signal, and end users with SCI need to learn to control movements, such as grasping, with contralateral shoulder movements. This control would have a more natural feel for the end user if the movement intention is decoded with a brain-computer interface (BCI), and subsequently translated into a control signal for a neuroprosthesis or robotic arm. They often rely on power modulations of sensorimotor rhythms (SMR)
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