EEG Papers

New

  • Human Electroencephalograms Seen as Fractal Time Series: Mathematical Analysis and Visualization
    V. Kulish, A. Sourin, O. Sourina
    Computers in Biology and Medicine, Elsevier-Pergamon, 36:3, 291-302, 2005.
    http://staffx.webstore.ntu.edu.sg/personal/assourin/Shared%20Documents/Papers/cbm06.pdf
     

  • Analysis and visualization of human electroencephalograms seen as fractal time series
    V. Kulish, A. Sourin, O. Sourina
    Journal of Mechanics in Medicine & Biology, 6:2 (2006) 175-188
    http://staffx.webstore.ntu.edu.sg/personal/assourin/Shared%20Documents/Papers/jmmb06.pdf
     

  • Nonlinear Considerations in EEG Signal Classification
    Neep Hazarika, Ah Chung Tsoi, Senior Member, IEEE, and Alex A. Sergejew
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 45, NO. 4, APRIL 1997 pp. 829
     

  •  Phase Correlations in Human EEG Signal: A Case Study
    Gagandeep S. Sandha and Neha Oberoi
    Second IEEE International Workshop on Electronic Design, Test and Applications
     

  • Quantifying physiological data with Lempel-Ziv complexity--certain issues > http://ieeexplore.ieee.org/iel5/10/22436/01046946.pdf?arnumber=1046946&htry=5
     

  • Integrated MEG/EEG and fMRI Model Based on Neural Masses
    Babajani Abbas ; Soltanian-Zadeh Hamid
    Digital Object Identifier: 10.1109/TBME.2006.873748
     

  • Automated Detection of Epilectic Events in the Interictal EEG using the Wavelet Transform.
    N. Coninx
    Bachelor Conference Knowledge Engineering, Maastricht, June 23, 2005
    http://www.fdaw.unimaas.nl/education/bachelor/conference/6.pdf
     

  • Estimating Driving Performance Based on EEG Spectrum Analysis
    Chin-Teng Lin, Ruei-ChengWu, Tzyy-Ping Jung, Sheng-Fu Liang and Teng-Yi Huang
    EURASIP Journal on Applied Signal Processing 2005:19, 3165–3174
    http://www.sccn.ucsd.edu/~jung/pdf/EURASIP2005.pdf 
     

  • Empirical evidence of the linear nature of magnetoencephalograms
    Antti Honkela, Tomas Ostman and Ricardo Vigario
    In Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN 2005), Bruges, Belgium, pp. 285 - 290 (2005)
    http://www.cis.hut.fi/ahonkela/papers/Honkela05ESANN.pdf
     
     

  • Differentiating Between Normal and Abnormal EEG using Independent Components Analysis and the Kohonen Self-Organising Map
    B Ricaud, B W Jervis and J Jarratt
     
    EEGs have to be inspected to distinguish between normal and abnormal EEGs, and an abnormal EEG may include sections of normal EEG. This is a time-consuming process, and decisions on normality or abnormality depend upon the electroencephalographer's judgement, particularly for slight abnormalities. It is therefore desirable to automate this EEG  differentiation by computer, and a method of achieving this is described in this paper. A number of abnormal and normal 21 channel EEG recordings from 21 different subjects were analysed. They were subjected to Independent Components Analysis (ICA). The ICA activations were back-projected to the measurement electrodes and those of largest magnitude for each source selected. These were divided into 4 s segments, which were low-pass filtered to remove drift and frequency components above 14 Hz, sub-sampled, and then high-pass filtered. The segments were next Fourier transformed to obtain their energy spectrum densities. This data was then used to train 21 Kohonen Self-Organising  Maps, there being one map for each of the largest back-projected component of each source. Inspection of these maps revealed detectable differences between those corresponding to normal and those corresponding to abnormal EEGs. When the method was tested on data from additional EEGs not used during the training, classification accuracies between 95 and 100% were obtained.
     

  • PATTERNS IN EEG FOR DISCRIMINATION BETWEEN MENTAL TASKS
    Chuck Anderson (Colorado State University)
    Linear transformations of lagged, multi-channel, spontaneous EEG recorded from subjects performing different mental tasks reveal spatial and temporal patterns that are similar across tasks and other patterns that are dissimilar. The similar patterns may be due to noise in the recording process and to mental activity common to the tasks. The dissimilar patterns form a basis for identifying the mental tasks that underlie the recorded EEG. Results are described for transforms based on singular value decomposition, maximum signal fraction, canonical correlation analysis, and independent components analysis.
    http://ida.first.fraunhofer.de/bbci/nips04_workshop/
      

  • Rodrigo Quian Quiroga (Lecturer in Bioengineering Dept. Engineering. University of Leicester, UK)
    List of publications: http://www.vis.caltech.edu/%7Erodri/public.htm
     

  • Time-frequency analysis of EEG series
    Blanco S, Quian Quiroga R., Rosso O. and Kochen S.
    Physical Review E 51: 2624; 1995.
    http://www.vis.caltech.edu/%7Erodri/papers/pre1.pdf
      

  • PCA+HMM+SVM FOR EEG PATTERN CLASSIFICATION
    Hyekyung Lee and Seungjin Choi
    http://www.postech.ac.kr/~seungjin/publications/isspa03.pdf
     

  • EEG coherency II: experimental comparisons of multiple measures 
    Paul L. Nunez, Richard B. Silberstein, Zhiping Shi, Matthew R. Carpenter, Ramesh Srinivasan, Don M. Tucker, Scott M. Doran, Peter J. Cadusch and Ranjith S. Wijesinghe
    Clinical Neurophysiology, Volume 110, Issue 3, 1 March 1999, Pages 469-486
    SummaryPlus | Full Text + Links | PDF (3894 K)
     

  • EEG coherency: I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales 
    Paul L. Nunez, Ramesh Srinivasan, Andrew F. Westdorp, Ranjith S. Wijesinghe, Don M. Tucker, Richard B. Silberstein and Peter J. Cadusch
    Electroencephalography and Clinical Neurophysiology, Volume 103, Issue 5, November 1997, Pages 499-515
    SummaryPlus | Full Text + Links | PDF (991 K)
     

  • The stone of madness’ and the search for the cortical sources of brain diseases with non-invasive EEG techniques 
    F. Babiloni,C. Babiloni, F. Carducci, F. Cincotti, P.M. Rossini
    Clinical Neurophysiology 114 (2003) 1775–1780
    http://www.tc.umn.edu/~binhe/publications/ref4_Editorial.pdf 
     

  • Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function
    F. Babilon et al
    NeuroImage 24 (2005) 118– 131
    http://www.tc.umn.edu/~binhe/publications/multimodal1.pdf 
     

  • Indications of nonlinear structures in brain electrical activity 
    Temujin Gautama, Danilo P. Mandic and Marc M. Van Hulle
    PHYSICAL REVIEW E 67, 046204 2003 
    http://www.pspc.dibe.unige.it/ecovision/pubs/papers/physrevE03.pdf 
     

  • Modeling Common Dynamics in Multichannel Signals With Applications to Artifact and Background Removal in EEG Recordings
    DeClercq, W. Vanrumste, B. Papy, J.-M. VanPaesschen, W. VanHuffel, S. 
     

  • A neural mass model for MEG/EEG: coupling and neuronal dynamics
    Olivier David* and Karl J. Friston 
    NeuroImage 20 (2003) 1743–1755 
    http://www.fil.ion.ucl.ac.uk/spm/doc/papers/od_neural_mass.pdf     
     

  • Nonlinear multivariate analysis of Neurophysiological Signals
    Ernesto Pereda, Rodrigo Quian Quiroga, Joydeep Bhattacharya
    to appear in Progress in Neurobiology
    http://arxiv.org/abs/nlin/0510077 
     

  • Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony
    Michel Le Van Quyen, Jack Foucher, Jean-Philippe Lachaux, Eugenio Rodriguez, Antoine Lutz, Jacques Martinerie and Francisco J. Varela 
    J Neurosci Methods. 2001 Oct 30;111(2):83-98.
     

  • Detection of n:m phase locking from noisy data: application to magnetoencephalography
    Tass, P., Rozenblum, M. G., Weule, J., Kurths, J., Pikovsky, A., Volkmann, J., Schnitzler, A., and Freund, H.-J.
    Phys.Rev.Lett., vol. 81, no. 15, pp. 3291-3294, Oct.1998. 
     
    EEGLab : gamma = abs(mean(exp(i*PhaseDiff))); 
      

  • Randomization tests for ERP topographies and whole spatiotemporal data matrices
    Eric Maris
    Psychophysiology, January 2004 - Vol. 41 Issue 1
     

  • An artificial intelligence approach to classify and analyse EEG traces 
    C. Castellaro, G. Favaro, A. Castellaro, A. Casagrande, S. Castellaro, D.V. Puthenparampil, C. Fattorello Salimbeni
    Neurophysiol Clin 2002 ; 32 : 193-214 
    http://ibogeo.df.unibo.it/silvia/Articoli/NNpubblicato.pdf 
    NNet: 855-2000-1800-32 ... 5.367.600 weights !
     

  • Lecture 11: The Action Potential & Nerves 
    http://members.aol.com/Bio50/LecNotes/lecnot11.html 
     

  • A Wavelet Based Approach for the Detection of Coupling in EEG Signals R. Saab, M.J. McKeown, L.J. Myers,and R. Abu-Gharbieh
    Proceedings of the 2nd International IEEE EMBS Conference on Neural Engineering, Arlington, Virginia · March 16 - 19, 2005
    http://www.ece.ubc.ca/~rafeef/papers/neural2005a.pdf  
     

  • Évaluation de la modélisation réaliste en MEG. 
    Crouzeix, A., Yvert, B., Bertrand, O., Echallier, J.-F. and Pernier, J. 
    Journée sur l'imagerie Fonctionnelle Cérébrale du GRD- PRC ISIS, Caen, 11-12 décembre 1997.
    [html] [pdf] 
     

  • Sensitivity Distributions of EEG and MEG Measurements
    Jaakko Malmivuo, Veikko Suihko and Hannu Eskola
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 44, NO. 3. MARCH 1997, pp. 196-208 
    http://butler.cc.tut.fi/~malmivuo/bem/eegmeg/ 
     

  • Comparison of different methods of time shift measurement in EEG
    Premysl Jiruška, Jan Prokš, Ondrej Drbal, Pavel Sovka, Petr Marusic, Pavel Mareš
    http://www.biomed.cas.cz/physiolres/pdf/prepress/716.pdf 
     

Realistic Heads

Compression

Entropy

Data fusion

Referencing

New

Guidelines

qEEG

Unsorted

Quantitative EEG

  • Assessment of digital EEG, quantitative EEG and EEG brain mapping: report of the American Academy
    Nuwer M: 
    of Neurology and the American Clinical Neurophysiology Society.
    Neurology 1997; 49:277–292 
     

  • Limitations of the American Academy of Neurology and American Clinical Neurophysiology Society Paper on QEEG 
    Daniel A. Hoffman et. al
    J Neuropsychiatry Clin Neurosci 11:3, Summer 1999 pp.401-407. 
     

  • Quantitative Spectral Electroencephalography in Predicting Survival in Patients with Early Alzheimer Disease
    Jules J. Clauss et. al
    Arch. Neurol./Vol 55, Aug. 1998 pp. 1105-1111 
     

  • Beta activity in aging and dementia
    DP Holschneider, AF Leuchter
    Brain Topogr. 1995 Winter;8(2):169-80 
     

  • Future directions for epilepsy research
    M.P. Jacobs
    November (1 of 2) 2001 NEUROLOGY 57, pp.1536-1541. 
     

  • Computer –assisted EEG Diagnosis: Pattern Recognition and Brain Mapping
    Fernando Lopes da Silva
    in E. Niedermeyer; F. Lopez da Silva: Electroencephalography. Basic Principles,
    Clinical Applications, and Related Fields, 4 edition pp. 1164-1189. 
     

  • EEG Analysis: Theory and Practice
    Fernando Lopes da Silva
    in E. Niedermeyer; F. Lopez da Silva: Electroencephalography.
    Basic Principles, Clinical Applications, and Related Fields 4 edition pp. 1135-1163. 
     

  • The clinical Use of EEG Topography
    Ernst Rodin
    in E. Niedermeyer; F. Lopez da Silva: Electroencephalography.
    Basic Principles, Clinical Applications, and Related Fields, 4 edition pp. 1135-1163.
     

EEG Classification

Brain Computer Interface

Seizure detection

Analysis and Classification

Ph.D. Thesis

  • Detection of epileptiform activity in the electroencephalogram using artificial neural networks (1997)
    James, C. J. (PhD Thesis, University of Canterbury, Christchurch, New Zealand, 258 pages, February 1997.)
    http://www.bierg.aston.ac.uk/Down/CJJs_PhD_thesis.ps.gz 
     

  • Detection of Seizure Onset in Epileptic Patients from Intracranial EEG Signals
    Rosana Esteller (School of Electrical and Computer Engineering, Georgia Institute of Technology, June, 1999) 
     

  • Another

Posters

M.Sc. Thesis

  • A Hybrid System for Intelligent Detection and Analysis of EEG Events
    Cristin Bigan Politehnica University of Bucharest
    Unirii 15 ap.9 sect.5 Bucharest, Romania phone 00401-3362939, email
    ibigan@pcnet.pcnet.ro     

    EEG signal, Time-frequency, Neural network, Events signature Various events in the EEG are widely used to diagnose patients who suffer of different diseases including epilepsy. The EEG during an event will exhibit characteristic temporal, and spectral properties depending upon the type, and the cause. Identifying an EEG with a specific event and his nature can help support a diagnosis, and may also be used to classify the type of specific event (normal, artefact, spike, seizure, K-complexes, sleep spindles , etc.). From this work, based on A Time-Frequency Analysis Pre-processing of EEG epochs, we got some good results about the best frequency changes resolution for feature extraction used to NN input. Together with the other features (from the same data mining) the system performs a NN and knowledge based detection and according to our knowledge there is no such a method reported in the literature about how to determine a signature for an EEG event.
     

  • Optical Signal Recognition from Printed Traces of EEG Data 
    Cristin Bigan Politehnica University of Bucharest
    Unirii 15 bl.3 ap.9 sect.5 Bucharest, Romania phone +40-1-3362939
    email ibigan@pcnet.pcnet.ro 

    Plot, Signal traces, Pictorial pattern, Recognition, Text file Advanced OCR software includes sophisticated features for image decomposition and recognition of characters but dealing with pure graphical sequences from scanned documents fails handling the graph, providing just the recognition of existing characters. The paper shows the use of some image processing techniques completed by the use of a proposed algorithm for numerical data series recognition out of single or multiple traces image recorded plots of continuous biomedical signals. Examples are on EEG paper recorded signals. The method starts with approaches of edge detection, shape analysis, contour tracing and thinning algorithms to produce continuous curves of single pixel thickness and as a software tool can be useful besides making old paper biomedical records being able to be processed as digital signals but also to include it into advanced scanning applications as an OSR (Optical Signal Recognition) feature.
     

  • SIGNAL FRACTION ANALYSIS AND ARTIFACT REMOVAL IN EEG (2003)
    James N. Knight (Department of Computer Science, Colorado State University, Fort Collins, Colorado)
    http://www.cs.colostate.edu/eeg/publications/natethesis.pdf 
     

  • Tool for bio-signal analysis - Application to multichannel single trial estimation of evoked potentials
    Perttu Ranta-aho
    http://it.uku.fi/biosignal/pdf/PRa_gradu.pdf  
     

  • Analysis of LVQ in the Context of Spontaneous EEG Signal Classification
    Daniel Kermit Ford
    http://www.cs.hmc.edu/~bjc/research/papers/ford-ms_ps.gz  
     

  • Non-Linear Principal Component Analysis and Classification of EEG During Mental Tasks
    Saikumar Devulapalli
     http://www.cs.hmc.edu/~bjc/research/papers/sai-ms_ps.ps  
     

  • BJC Papers
    http://www.cs.hmc.edu/~bjc/research/related-papers.html 
     

Interesting conferences on the topic 

2005 - SPMC / SoCCE / UoP