EEG/MEG Signal Processingdownloads.hindawi.com/journals/cin/2007/097026.pdf · 1Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Saitama 351-0198, Japan

Hindawi Publishing CorporationComputational Intelligence and NeuroscienceVolume 2007, Article ID 97026, 2 pagesdoi:10.1155/2007/97026

EditorialEEG/MEG Signal Processing

A. Cichocki1 and S. Sanei2

1 Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Saitama 351-0198, Japan2 Centre of Digital Signal Processing, Cardiff University, Cardiff CF24 3AA, Wales, UK

Correspondence should be addressed to S. Sanei, [email protected]

Received 13 November 2007; Accepted 13 November 2007

Copyright © 2007 A. Cichocki and S. Sanei. 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.

Since its invention by the Hans Berger of the electroencepha-lography (EEG) in 1929, it was a strong scientific curiosityin analysis of human brain activity. In fact, the electroen-cephalography (EEG) and magnetoencephalography (MEG)have developed into one of the most important and widelyused quantitative diagnostic tools in analysis of brain sig-nals and patterns. EEG and MEG potentially contain a richsource of information related to functional, physiological,and pathological status of the brain. In particularly, they areessential for the identification of mental disorders and brainrhythms extremely useful for the diagnosis and monitoringof brain activity and offer not only the functional but alsopathological, physiological, and metabolic changes withinthe brain and perhaps other parts in the body.

Recording and analysis of the EEG and MEG now in-volve a considerable amount of signal processing for S/N en-hancement, feature detection, source localization, automatedclassification, compression, hidden information extraction,and dynamic modeling. These involve a variety of innovativesignal processing methods, including adaptive techniques,time-frequency and time-scale procedures, artificial neuralnetworks and fuzzy logic, higher-order statistics and nonlin-ear schemes, fractals, hierarchical trees, Bayesian approaches,and parametric modeling. This special issue contributes tothe current status of EEG and MEG signal processing andanalysis, with particular regard to recent innovations. It re-ports some promising achievements by academic and com-mercial research institutions and individuals, and providesan insight into future developments within this exciting andchallenging area of functional brain imaging.

Noninvasive functional brain imaging has become an im-portant tool used by neurophysiologists, cognitive psycholo-gists, cognitive scientists, and other researchers interested inbrain function. In the last five decades the technology of non-

invasive functional imaging has flowered, and researchers to-day can choose from EEG, MEG, PET, SPECT, MRI, NIRS,and fMRI. Each method has its own strengths and weak-nesses. Development of signal processing tools mitigates theproblems and alleviates some of the weaknesses.

This issue includes the following contributions whichcover a wide range of signal processing techniques for anal-ysis, understanding, and recognition of EEG/MEG informa-tion.

The first paper, “Canonical source reconstruction forMEG” by J. Mattout et al., describes a new, simple but effi-cient solution to the problem of reconstructing electromag-netic sources into a canonical or standard anatomical space.Electromagnetic lead fields are computed using the warpedmesh, in conjunction with a spherical head-model (whichdoes not rely on individual anatomy). The ensuing forwardmodel is inverted using an empirical Bayesian scheme thatwas described previously in several publications. This enablesthe pooling of data from multiple subjects and the reportingof results in stereotactic coordinates. Furthermore, it allowsthe graceful fusion of fMRI and MEG data within the sameanatomical framework.

The second paper, “A subspace method for dynamical es-timation of evoked potentials” by S. Georgiadis et al., de-scribes method for single-channel trial-to-trial EP charac-teristics estimation. Prior information about phase-lockedproperties of the EPs is assessed by means of estimated signalsubspace and eigenvalue decomposition. Then for those situ-ations that dynamic fluctuations from stimulus-to-stimuluscould be expected, prior information can be exploited bymeans of state-space modeling and recursive Bayesian meansquare estimation methods (Kalman filtering and smooth-ing). The authors demonstrate that a few dominant eigen-vectors of the data correlation matrix are able to model

2 Computational Intelligence and Neuroscience

trend-like changes of some component of the EPs, and thatKalman smoother algorithm is to be preferred in terms ofbetter tracking capabilities and mean square error reduction.They also demonstrate the effect of strong artifacts, partic-ularly eye blinks, on the quality of the signal subspace andEP estimates by means of independent component analysis(ICA) applied as a prepossessing step to the multichannelmeasurements.

The third paper, “Inferring functional brain states usingtemporal evolution of regularized classifiers,” by A. Zhdanovet al., proposes a framework for functional brain state infer-ence problem that utilizes the temporal information presentin the brain signals. This application suggests that the rela-tion between the regularization parameters and the temporalprofile of the classifier helps improving the classifier accu-racy.

In the fourth paper, “Removing ocular movement arte-facts by a joint smoothened subspace estimator,” by R. RobertPhlypo et al., a joint smoothened subspace estimator calcu-lates the low- and high-order statistic information subjectto the constraint that the resulting estimated ocular move-ment artifact source is smooth in time domain. This re-sults in combination of blind source separation with differ-ent order statistics. The results have been compared to thoseof well-known blind source separation methods and haveshown the capability of the system in mitigating the ocularartefacts automatically.

The fifth contribution, “A framework to support auto-mated classification and labeling of brain electromagneticpatterns,” by G. A. Frishkoff et al., focuses on patterns in av-eraged EEG (ERP) data to define high-level rules and con-cepts for ERP components and to design an automated dataprocessing system that implements these rules. This is with abroader objective of designing an oncology-based system tosupport cross laboratory, cross paradigm, and cross modalintegration of brain functional data.

The next paper, “Statistical modeling and analysis oflaser-evoked potentials of electrocorticogram recordingsfrom awake humans,” by Z. Chen et al., provides a compre-hensive analysis of electrocorticogram recorded using inva-sive laser stimulation. Both averaging and single trial laser-evoked potentials (LEP) have been considered. Then theLEPs have been extracted from both types of trials, and thevariations in power, amplitude, and latency have been stud-ied using probabilistic modeling, factor analysis, indepen-dent component analysis, wavelet domain, and quantitativeand qualitative analyses.

The seventh paper“A Novel constrained topographic in-dependent component analysis for separation of epilepticseizure signals,” by Min Jing and Saeid Sanei, addresses a con-strained source separation method which exploits the corre-lation among the nearby brain sources as well as character-istics of the seizure signals in space and frequency domainsto highlight the sources of interest. In this method the space-frequency characteristics of the data is utilized as the con-straint term in the update equation of the topographic ICAsystem. The results clearly show that the synchronously gen-erated seizure sources are grouped together.

The next paper, “Clustering approach to long termspatio-temporal interactions in epileptic electroencephalo-graph,” by A. Hegde et al., attempts to identify the spatio-temporal interactions of an epileptic brain using an exist-ing nonlinear dependency measure based on a clustering ap-proach. The mutual interactions have been analyzed usingan index measure based on a self-organizing map (SOM)network. The results report a long-term structural connec-tivity related to various seizure states. In addition, the au-thors have aimed at developing engineering tools to deter-mine spatiotemporal groupings in a multivariate epilepticbrain.

The ninth paper, “Automatic seizure detection based ontime-frequency analysis and artificial neural networks,” By A.T. Tzallas et al., uses an artificial neural network system fordetection of epileptic seizures from a set of features estimatedfrom time-frequency domain EEG data.

Next paper, “Canonical decomposition of ictal scalp EEGand accurate source localisation: principles and simulationstudy,” by M. De Vos et al., uses a dipole-based method forlocalization of epileptic seizure sources. In this methoda canonical decomposition procedure extracts the seizuresource by a three-way model assumption.

The eleventh paper, “The implicit function as squash-ing time model a novel parallel nonlinear EEG analysistechnique distinguishing mild cognitive impairment andAlzheimer’s disease subjects with high degree of accuracy,”by M. Buscema et al., introduces an ANN-based method inwhich the MCI and AD can be classified based on the spatialinformation content of the restino EEGs. In this procedurethe ANNs do not use EEGs as the input; rather, the inputs forthe classification are the weights of the connections withinthe ANN to generate the recorded EEG data. The introducedTWIST system selects the best features.

The last paper, “The P300 as a marker of waning atten-tion and error propensity,” By Avijit Kumar Datta, RhodriCusack, Kari Hawkins, Joost Heutink, Christopher Rorden,Ian Robertson, and Tom Manly, studies and examines thevariation of P300 ERP with respect to the error in respond-ing to the stimuli. During the course of this research it hasbeen found that errors are associated with significant reduc-tion in the amplitude of preceding P300, and the fluctuationsin P300 amplitude across the task formed a reliable associateof individual error propensity, supporting its use as a markerof our sustained control over action.

ACKNOWLEDGMENTS

The guest editors are extremely grateful to all the reviewerswho took time and consideration to assess the submittedmanuscripts. Their diligence has contributed greatly toensuring that final papers have conformed to the highstandards expected in this publication. The guest editors ofthis special issue are much indebted to their authors andreviewers, who put a tremendous amount of effort anddedication to make this issue a reality.

A. CichockiS. Sanei

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