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BioMed Research International
Volume 2014 (2014), Article ID 783203, 9 pages
Research Article

Movement Type Prediction before Its Onset Using Signals from Prefrontal Area: An Electrocorticography Study

1MEG Center, Department of Neurosurgery, Seoul National University Hospital, Seoul 110-744, Republic of Korea
2Interdisciplinary Program in Neuroscience, Seoul National University College of Natural Sciences, Seoul 151-747, Republic of Korea
3Department of Neurosurgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 110-799, Republic of Korea
4Sensory Organ Research Institute, Seoul National University, Seoul 151-742, Republic of Korea
5School of Design and Human Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
6Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 151-747, Republic of Korea

Received 28 March 2014; Revised 29 May 2014; Accepted 24 June 2014; Published 14 July 2014

Academic Editor: Yiwen Wang

Copyright © 2014 Seokyun Ryun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area’s premovement signals (−2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13–30 Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.