Computational Methods in Neuroengineering

Copyright © 2013 Chang-Hwan Im 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. Neuroengineering is an emerging discipline in the field of medical and biological engineering with the aim of understanding , modulating, enhancing, or repairing neuronal systems , which are obviously the most complex systems of the human body. During the last decade or so, neuroengineering has been growing rapidly and expanded its applications from interpreting and processing neuronal signals to interfacing the neural systems with external devices to restore lost functions. Despite its short history, neuroengineering has now become one of the most important topics in the current biomedical engineering research. As in other interdisci-plinary fields, computational methods have played key roles in the development of neuroengineering. Considering the aforementioned trends, it seems natural that neuroengineering is selected as the theme of this special issue. This special issue includes eleven high-quality, peer-reviewed articles that might provide researchers in the field of neuroscience, engineering, psychology, and computational sciences with the current state-of-the-art knowledge of this emerging interdisciplinary research field. The paper " Trial-by-trial adaptation of movements during mental practice under force field " by M. N. Anwar and S. H. Khan studied how motor imagery influences trial-to-trial learning in a robot based adaptation task. The results showed that reaching movements performed with motor imagery have relatively a more focused generalization pattern and a higher learning rate in training direction. The paper " Evaluation of EEG features in decoding individual finger movements from one hand " by R. Xiao and L. Ding investigates the existence of a broadband feature in EEG to discriminate individual fingers from one hand, with the significantly higher average decoding accuracy than guess level by the spectral principal component analysis (PCA). The paper " Coercively adjusted auto regression model for forecasting in epilepsy EEG " by S.-H. Kim et al. proposes a coercively adjusted auto regression (CA-AR) method to forecast future values from a multivariate epilepsy EEG time series with higher accuracy and improved computational efficiency. The paper " A mixed L2 norm regularized HRF estimation method for rapid event-related fMRI experiments " by Y. Lei et al. presents a new regularization framework to identify trial-specific BOLD responses in extremely rapid event-related fMRI experiments, where BOLD responses are heavily overlapped from adjacent …