Editorial Computational Methods in Neuroengineeringdownloads.hindawi.com/journals/cmmm/2013/617347.pdf · Neuroengineering is an emerging discipline in the eld of medical and biological

Hindawi Publishing CorporationComputational and Mathematical Methods in MedicineVolume 2013, Article ID 617347, 2 pageshttp://dx.doi.org/10.1155/2013/617347

EditorialComputational Methods in Neuroengineering

Chang-Hwan Im,1 Lei Ding,2 Yiwen Wang,3 and Sung-Phil Kim4

1 Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 133-791, Republic of Korea2 School of Electrical & Computer Engineering, University of Oklahoma, Norman, OK 73019-1023, USA3Qiushi Academy of Advanced Studies, Zhejiang University, Hangzhou 310027, China4Research and Business Foundation, Korea University, Seoul 136-713, Republic of Korea

Correspondence should be addressed to Chang-Hwan Im; [email protected]

Received 9 June 2013; Accepted 9 June 2013

Copyright © 2013 Chang-Hwan Im et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Neuroengineering is an emerging discipline in the field ofmedical and biological engineering with the aim of under-standing, modulating, enhancing, or repairing neuronal sys-tems, which are obviously the most complex systems of thehuman body. During the last decade or so, neuroengineeringhas been growing rapidly and expanded its applications frominterpreting and processing neuronal signals to interfacingthe neural systems with external devices to restore lost func-tions. Despite its short history, neuroengineering has nowbecome one of the most important topics in the currentbiomedical engineering research. As in other interdisci-plinary fields, computational methods have played key rolesin the development of neuroengineering.

Considering the aforementioned trends, it seems naturalthat neuroengineering is selected as the theme of this specialissue. This special issue includes eleven high-quality, peer-reviewed articles that might provide researchers in the fieldof neuroscience, engineering, psychology, and computationalsciences with the current state-of-the-art knowledge of thisemerging interdisciplinary research field.

The paper “Trial-by-trial adaptation of movements duringmental practice under force field” by M. N. Anwar and S.H. Khan studied how motor imagery influences trial-to-triallearning in a robot based adaptation task.The results showedthat reaching movements performed with motor imageryhave relatively a more focused generalization pattern and ahigher learning rate in training direction.

The paper “Evaluation of EEG features in decoding indi-vidual 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 thesignificantly higher average decoding accuracy than guesslevel by the spectral principal component analysis (PCA).

The paper “Coercively adjusted auto regression model forforecasting in epilepsy EEG” by S.-H. Kim et al. proposes acoercively adjusted auto regression (CA-AR) method to fore-cast future values from a multivariate epilepsy EEG timeseries with higher accuracy and improved computationalefficiency.

The paper “A mixed L2 norm regularized HRF estimationmethod for rapid event-related fMRI experiments” by Y. Leiet al. presents a new regularization framework to identifytrial-specific BOLD responses in extremely rapid event-related fMRI experiments, where BOLD responses are heavilyoverlapped from adjacent trials. It is demonstrated that thetechnique significantly improves the classification accuracyin decoding brain tasks in a rapid four-category objectclassification experiment.

The paper “Development of the complex general linearmodel in the Fourier domain: application to fMRI multipleinput-output evoked responses on single subjects” by D. E. Rioet al. describes a statistical time series analysis in the Fourierdomain based on a linear time invariant model to processmultivariate data from fMRI experiments, particularly forsingle subjects.

The paper “Continuous- and discrete-time stimulussequences for high stimulus rate paradigm in evoked potentialstudies” by T. Wang et al. suggests using continuous-time stimulus sequences for the high stimulus rateparadigm to obtain auditory evoked potentials (AEPs) from

2 Computational and Mathematical Methods in Medicine

electroencephalography (EEG). Both the analytic results andsimulation experiments revealed the advantages of using thecontinuous-time sequences over traditional discrete-timesequences in terms of the reliability of reconstructed AEPs.

The review paper “A review on the computational methodsfor emotional state estimation from the human EEG” by M.-K. Kim et al. summarizes the state-of-the-art computationalmethods used to extract features for emotional states fromEEG signals and to classify those features into one of manyemotional states. The readers of the journal would find thisreview helpful for understanding howmathematicalmethodsare employed for affective computing based on EEG.

The paper “Information analysis on neural tuning indorsal premotor cortex for reaching and grasping” by Y. Caoet al. investigates the contribution of the dorsal premotorcortex (PMd) in reaching and grasping experiments withmonkeys. It is identified that, within PMd, there are neuronsthat only engage in reaching, neurons that only engage ingrasping, and neurons that engage in both.This phenomenonis further validated in a decoding experiment using differentsets/subsets of neurons in PMd.

The paper “Modiolus-hugging intracochlear electrodearray with shape memory alloy” by K. S. Min et al. presentsa novel intracochlear electrode which can deliver a moreefficient electrical stimulation in human cochlea to aid peoplewith a severe hearing loss. They propose to use a shape-memory-alloy embedded electrode to reduce the distancebetween the electrode and the target cells.

The paper “Corticomuscular coherence analysis on handmovement distinction for active rehabilitation” by X. Lou et al.evaluated corticomuscular coherence (CMC) between EEGand EMG signals recorded during voluntary hand openingtasks and found that the CMC values can detect voluntaryhand opening with high accuracy. Their results suggest thatCMC analysis can be a promising tool for active handrehabilitation of patients with stroke.

The paper “A sound processor for cochlear implant usinga simple dual path nonlinear model of basilar membrane” byK. H. Kim et al. proposed a new active nonlinear model ofthe frequency response of the basilar membrane in biologicalcochlea called the simple dual path nonlinear (SDPN) modeland a novel sound processing strategy for cochlear implants(CIs) based upon this model.

Acknowledgments

We would like to express our deepest gratitude to manyreviewers, whose professional comments guaranteed the highquality of the selected papers. In addition, we would also liketo express our appreciation to the editorial board membersand publishing office of the journal for their help and supportthroughout the preparation of this special issue.

Chang-Hwan ImLei Ding

Yiwen WangSung-Phil Kim

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Editorial Computational Methods in Neuroengineeringdownloads.hindawi.com/journals/cmmm/2013/617347.pdf · Neuroengineering is an emerging discipline in the eld of medical and biological