A. Cichocki

S. Sanei

EEG/MEG Signal Processing

Action errors can occur when routine responses are triggered inappropriately by familiar cues. Here, EEG was recorded as volunteers performed a “go/no-go” task of long duration that occasionally and unexpectedly required them to withhold a frequent, routine response. EEG
components locked to the onset of relevant go trials were sorted according to whether participants erroneously responded to immediately subsequent no-go trials or correctly withheld
their responses. Errors were associated with a significant relative reduction in the amplitude of
the preceding P300, that is, a judgement could be made bout whether a response-inhibition
error was likely before it had actually occurred. Furthermore, fluctuations in P300 amplitude across the task formed a reliable associate of individual error propensity, supporting its use as a
marker of sustained control over action.

Avijit Datta

Rhodri Cusack

Kari Hawkins

Joost Heutink

Chris Rorden

Ian H. Robertson

Tom Manly

The P300 as a Marker of Waning Attention  and Error Propensity

This paper describes a framework for automated classification and labeling of patterns in 
    electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We describe recent progress 
    on four goals: 1) specification of  rules and concepts that capture expert knowledge of event-related
     potentials (ERP) patterns in visual word recognition; 2) implementation of rules in an automated data 
     processing and labeling stream; 3) data mining techniques that lead to refinement of rules; and 4) iterative 
     steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven,
      methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and
       can lead to development of tools for pattern classification and labeling that are robust and conceptually 
       transparent to researchers. The present application focuses on patterns in averaged EEG (ERP) data. We also
        describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source
         (anatomical) space. The broader aim of this work is to design an ontology-based system to support 
         cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for
          this project are implemented in MATLAB and are freely available on request.

Gwen A. Frishkoff

Robert M. Frank

Jiawei Rong

Dejing Dou

Joseph Dien

Laura K. Halderman

A Framework to Support Automated Classification and Labeling of Brain Electromagnetic Patterns

The recording of seizures is of primary interest in the evaluation of epileptic
 patients.  Seizure is the phenomenon of rhythmicity  discharge from either a local area 
 or the whole brain and the individual behavior usually lasts from seconds to minutes.  Since seizures, 
 in general, occur infrequently and unpredictably, automatic detection of seizures during long-term
  electroencephalograph (EEG) recordings is highly recommended.  As EEG signals are nonstationary, the
   conventional methods of frequency analysis are not successful for diagnostic purposes.  This paper presents
    a method of analysis of EEG signals, which is based on time-frequency analysis.  Initially, selected segments 
    of the EEG signals are analyzed using time-frequency methods and several features are extracted for each 
    segment, representing the energy distribution in the time-frequency plane.  Then, those features are used as 
    an input in an artificial neural network (ANN), which provides the final classification of the EEG segments
     concerning the existence of seizures or not.  We used a publicly available dataset in order to evaluate our
      method and the evaluation results are very promising indicating overall accuracy from 97.72&#37; to 
      100&#37;.

A. T. Tzallas

M. G. Tsipouras

D. I. Fotiadis

Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG) by 
    ocular movement artefacts, we present a method which combines lower-order, short-term and 
    higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE) calculates the joint 
    information in both statistical models, subject to the constraint that the resulting estimated source should
     be sufficiently smooth in the time domain (i.e., has a large autocorrelation or self predictive power). It is
      shown that the JSSE is able to estimate a component from simulated data that is superior with respect 
      to methodological artefact suppression to those of FastICA, SOBI, pSVD, or JADE/COM1 algorithms
       used for blind source separation (BSS). Interference and distortion suppression are of comparable order 
       when compared with the above-mentioned methods. Results on patient data demonstrate that the method 
       is able to suppress blinking and saccade artefacts in a fully automated way.

Ronald Phlypo

Paul Boon

Yves D'Asseler

Ignace Lemahieu

Removing Ocular Movement Artefacts by a Joint Smoothened Subspace Estimator

Long-term electroencephalographic (EEG) recordings are important in the presurgical evaluation
of refractory partial epilepsy for the delineation of the ictal onset zones. In this paper, we introduce 
a new concept for an automatic, fast, and objective localisation of the ictal onset zone in ictal EEG
 recordings. Canonical decomposition of ictal EEG decomposes the EEG in atoms. One or more atoms 
 are related to the seizure activity. A single dipole was then fitted to model the potential distribution of 
 each epileptic atom. In this study, we performed a simulation study in order to estimate the dipole
  localisation error. Ictal dipole localisation was very accurate, even at low signal-to-noise ratios, was not 
  affected by seizure activity frequency or frequency changes, and was minimally affected by the waveform 
  and depth of the ictal onset zone location. Ictal dipole localisation error using 21 electrodes was around 
  10.0 mm and
improved more than tenfold in the range of 0.5–1.0 mm using 148 channels. In conclusion, our
simulation study of canonical decomposition of ictal scalp EEG allowed a robust and accurate
localisation of the ictal onset zone.

Maarten De Vos

Lieven De Lathauwer

Bart Vanrumste

Sabine Van Huffel

W. Van Paesschen

Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study

Objective. This paper presents the results obtained 
 using a protocol based on special types of artificial neural networks 
 (ANNs) assembled in a novel methodology able to compress the temporal sequence 
 of electroencephalographic (EEG) data into spatial invariants for the automatic 
 classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. With 
 reference to the procedure reported in our previous study
(2007), this protocol includes a new type of artificial organism, named 
TWIST. The working hypothesis was that compared to the results presented by the workgroup (2007); the
 new artificial organism TWIST could produce a better classification between AD and MCI. Material
 and methods. Resting eyes-closed EEG data were recorded in 180 AD patients and in 115
 MCI subjects. The data inputs for the classification, instead of being the EEG data, were the weights
 of the connections within a nonlinear autoassociative ANN trained to generate the recorded data. The 
 most relevant features were selected and coincidently the datasets were split in
 the two halves for the final binary classification (training and testing) performed by a supervised ANN. 
Results. The best results distinguishing between AD 
and MCI were equal to 94.10% and they are considerable better than the ones 
reported in our previous study (<mml:math id="E1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>∼</mml:mo></mml:math>92%) (2007). Conclusion. The results 
 confirm the working hypothesis that a correct automatic classification of MCI and AD subjects can 
 be obtained by extracting spatial information content of the resting EEG voltage by ANNs and represent 
 the basis for research aimed at integrating spatial and temporal information content of the EEG.

Massimo Buscema

Massimiliano Capriotti

Francesca Bergami

Claudio Babiloni

Paolo Rossini

Enzo Grossi

The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

Abnormal dynamical coupling between brain structures is believed to be primarily
responsible for the generation of epileptic seizures and their propagation. In this study, we
attempt to identify the spatio-temporal interactions of an epileptic brain using a previously
proposed nonlinear dependency measure. Using a clustering model, we determine the average
spatial mappings in an epileptic brain at different stages of a complex partial seizure. Results
involving 8 seizures from 2 epileptic patients suggest that there may be a fixed pattern associated
with regional spatio-temporal dynamics during the interictal to pre-post-ictal transition.

Anant  Hegde

Deniz  Erdogmus

Deng S. Shiau

Jose C. Principe

Chris J. Sackellares

Clustering Approach to Quantify Long-Term Spatio-Temporal Interactions in Epileptic Intracranial Electroencephalography

It is a challenge in evoked potential (EP) analysis to incorporate prior physiological
             knowledge for estimation. In this paper, we address the problem of single-channel trial-to-trial EP 
             characteristics estimation. Prior information about phase-locked properties of the EPs is assesed by
              means of estimated signal subspace and eigenvalue decomposition. Then for those situations that
               dynamic fluctuations from stimulus-to-stimulus could be expected, prior information can be exploited
                by means of state-space modeling and recursive Bayesian mean square estimation methods (Kalman 
                filtering and smoothing). We demonstrate that a few dominant eigenvectors of the data correlation matrix 
                are able to model trend-like changes of some component of the EPs, and that Kalman smoother
                 algorithm is to be preferred in terms of better tracking capabilities and mean square error reduction. We 
                 also demonstrate the effect of strong artifacts, particularly eye blinks, on the quality of the signal 
                 subspace and EP estimates by means of independent component analysis applied as a prepossessing
                  step on the multichannel measurements.

Stefanos D. Georgiadis

Perttu O. Ranta-aho

Mika P. Tarvainen

Pasi A. Karjalainen

A Subspace Method for Dynamical Estimation of  Evoked Potentials

We present a framework for inferring functional brain state from electrophysiological
 (MEG or EEG) brain
signals. Our approach is adapted to the needs of functional brain imaging rather than
 EEG-based brain-computer interface (BCI). This choice leads to a different set of requirements, in
  particular to the demand for more robust inference methods and more sophisticated model 
  validation techniques. We approach the problem from a machine learning perspective, by 
  constructing a classifier from a set of labeled signal examples. We propose a framework that 
  focuses on temporal evolution of regularized classifiers, with cross-validation for optimal
   regularization parameter at each time frame. We demonstrate the inference obtained by this
    method on MEG data recorded from 10 subjects in a simple visual classification experiment, 
    and provide comparison to the classical nonregularized approach.

Andrey  Zhdanov

Talma  Hendler

Leslie  Ungerleider

Nathan  Intrator

Inferring Functional Brain States Using Temporal  Evolution of Regularized Classifiers

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Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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