Upper limb movements can be decoded from the time-domain of low-frequency EEG

• Published in: PloS one Vol. 12; no. 8; p. e0182578
• Main Authors: Ofner, Patrick, Schwarz, Andreas, Pereira, Joana, Müller-Putz, Gernot R.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.08.2017; Public Library of Science (PLoS)
• Subjects: Adult; Analysis; Arm - physiology; Biology and Life Sciences; Brain; Brain Waves; Classification; Classifiers; Cortex (motor); Cortex (parietal); Cortex (premotor); Cortex (somatosensory); Decoding; Disabled persons; EEG; Elbow; Electroencephalography; Engineering; Engineering and Technology; Extremities (Anatomy); Female; Health aspects; Humans; Male; Medical examination; Medicine and Health Sciences; Motor Cortex - physiology; Motor task performance; Movement; Movement - physiology; Prosthetics; Research and Analysis Methods; Robot arms; Robotics; Signal Processing, Computer-Assisted; Somatosensory cortex; Spinal cord; Time domain analysis; Velocity; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0182578

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.