A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography
Joint Authors
Hong, Wonjun
Kim, June Sic
Chung, Chun Kee
Yeom, Hong Gi
Kim, Sung-Phil
Kang, Da-Yoon
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-22
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yeom, Hong Gi& Hong, Wonjun& Kang, Da-Yoon& Chung, Chun Kee& Kim, June Sic& Kim, Sung-Phil. 2014. A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography. BioMed Research International،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-452086
Modern Language Association (MLA)
Yeom, Hong Gi…[et al.]. A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography. BioMed Research International No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-452086
American Medical Association (AMA)
Yeom, Hong Gi& Hong, Wonjun& Kang, Da-Yoon& Chung, Chun Kee& Kim, June Sic& Kim, Sung-Phil. A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-452086
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-452086