BROWSE

Related Researcher

Author

Kim, Sung-Phil
Brain-Computer Interface (BCI) Lab
Research Interests
  • Brain-computer interface, Statistical Signal Processing, Neural Code, Neuromarketing

ITEM VIEW & DOWNLOAD

A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography
Author
Yeom, Hong GiHong, WonjunKang, Da-YoonChung, Chun KeeKim, June SicKim, Sung-Phil
Keywords
NONINVASIVE ELECTROENCEPHALOGRAPHIC SIGNALS; BRAIN-COMPUTER INTERFACES; PRIMARY MOTOR CORTEX; MOVEMENT PARAMETERS; GRASP; REACH; MEG; ARM; REPRESENTATION; TETRAPLEGIA
Issue Date
201406
Publisher
HINDAWI PUBLISHING CORPORATION
Citation
BIOMED RESEARCH INTERNATIONAL, v.2014, no., pp.1 - 8
Abstract
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.
URI
Go to Link
DOI
http://dx.doi.org/10.1155/2014/176857
ISSN
2314-6133
Appears in Collections:
DHE_Journal Papers
Files in This Item:
84904114611.pdfDownload

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qr_code

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU