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BioMed Research International
Volume 2014, Article ID 176857, 8 pages
Research Article

A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography

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 151-742, Republic of Korea
3The Planet SK Co., Ltd., Seongnam 463-400, Republic of Korea
4School of Design and Human Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
5Department of Neurosurgery, Seoul National University College of Medicine, Seoul 110-744, Republic of Korea
6Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 151-742, Republic of Korea
7Sensory Organ Research Institute, Seoul National University, Seoul 151-742, Republic of Korea

Received 28 March 2014; Accepted 21 May 2014; Published 22 June 2014

Academic Editor: Yiwen Wang

Copyright © 2014 Hong Gi Yeom 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.


Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.