PRIVATE AREA

[Papers] [Books] [Magazines] [Videos] [Labs] [Conferences] [Public]


Legend
= Sleep & EEG Analysis
= Brain Computer Interface
= Medical Imagery
= Body motion analysis and control
= General

= référence ajoutée dernièrement

Search engine
  • IEEE Explor, to have acces to IEEE review: user=thierry_dutoit, pass=CCTVaeh1
  • NCBI, National Center for Biotechnology Information

Papers
  • P. Allen et al., 1998, Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtractiony, Neuroimage, Vol.8, NO.NI980361, 229–239
  • R. Argwal, Jean Gotman, 1998, Automatic EEG analysis during long-trm monitoring in the ICU, Elsevier Science Electroencephalography and clinical Neurophysiology, Vol. 107, No. 12, 44-58
  • R. Argwal, Jean Gotman, 2001, Computer-Assisted Sleep Staging, IEEE Engineering in Medicine and Biology Magazine, Vol. 48, No. 12, 1412-1423
  • R. Argwal, Jean Gotman, 2001, Long-term EEG compression for intensive-care settings, IEEE transactions on biomedical engineering, 0739-5175, 23-29
  • A. Bar et al., 2003, Evalauation of a portable device based on peripheral arterial tone for unattended home sleep studies, CHEST 2003, 123:695-703
  • Bionics, 2001, Bio-Inspired Information Technologies, ERCIM
  • Jose M. Carmena et al., 2003, Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates, PLoS Biology (http://biology.plosjournals.org) Vol 1, Issue 2, 001-016
  • P. Celka et al., 2001, Preprocessing and time-frequency analysis of newborn EEG seizures, IEEE Engineering in Medicine and Biology Magazine, 0739-5175, 30-39
  • Ming Cheng et al., 2002, Design and implementation of a brain-computer interface with high transfer rates, IEEE Transactions on Biomedical engineering, VOL.49, NO.10, 1181-1186
  • G. Coté et al., 2003, Emerging Biomedical Sensing Technologies and Their Applications, IEEE Sensors Journal, VOL. 3, NO. 3, 251-266
  • S.D. Cranstoun, 2002, Time-frequency spectral estimation of multichannel EEG using the auto-SLEX method, IEEE Transactions on Biomedical engineering, VOL.49, NO.9, 988-996
  • R.J. Croft, R.J Barry, 2000, Removal of ocular artifact from th EEG: a review, Neurophysiol Clin, VOL. 30, 5-19
  • P.J. Durka, K.J. Blinowska, 2001, A unified time-frequency parametrization of EEGs, IEEE Engineering in Medicine and Biology Magazine, 0739-5175, 47-53
  • P. J. Durka et al., 2003, A simple system for detection of EEG artifacts in polysomnographic recordings, IEEE Transactions on Biomedical engineering, Vol. 50, No. 4, 526-528
  • K. Englehart et al., 1999, Classification of the myoelectric signal using time-frequency based representations, Medical Engineering and Physics, 21, 431-438
  • A. Fingelkurts et al., 2003,The regularities of the discrete nature of multi-variability of EEG spectral patterns , International Journal of Psychophysiology , vol.47, 23–41
  • Shawn P. Garbett, 2003, Cleanroom Software Engineering: verifying a program's correctness, Dr Dobb's journal, Août 2003, 24-28
  • B. Geva,H. Kerem, 1999, Forecasting Generalized Epileptic Seizures from the EEG Signal by Wavelet Analysis and Dynamic Unsupervised Fuzzy Clustering, IEEE Transactions on Biomedical engineering, Vol. 45, No.10, 1205-1216
  • M. Gonzalez Mendoza, 2002, Système de Diagnostic par des Machines à Vecteurs de Support, 3ème congrès des doctorants de l'Ecole Doctorale SYSTEMES
  • C. Guger et al., 2001, Rapid Prototyping of an EEG-based Brain-Computer Interface (BCI), IEEE Trans Neural Syst Rehabil Eng, Mar 2001, 49-58
  • A.A. Handzel, P.S. Krishnaprasad, 2002, Biomimetic sound-source localization, IEEE Sensors Journal, VOL.2, NO.6, 607-616
  • C. S. Herrmann et al., 2001, Adaptive frequency decomposition of EEG with subsequent expert system analysis, Computers in Biology and Medicine, 31, 407-427
  • He Sheng Liu et al., 2002, A multistage, multimethod approach for automatic detection and classification of epileptiform EEG, IEEE Transactions on Biomedical engineering, VOL.49, NO.12, 1557-1566
  • S. L. Himanen, J. Hasan, 2000, Limitations of Rechtschaffen and Kales, Sleep Medicine Reviews, Vol. 4, No. 2, 149-167
  • M. Hirshkowitz, 2000, Standing on the shoulders of giants : the Standardized Sleep Manual after 30 years, Sleep Medicine Reviews, Vol. 4, No. 2, 169-170
  • A.Hossen et al.,2003,A New Simple Algorithm for Heart Rate Variability Analysis in Patients with Obstructive Sleep Apnea and Normal Controls, International Journal of Bioelectromagnetism,Vol. 5, No. 1, pp. 238 - 239
  • E. Huupponen et al., 2002, Sleep Depth Oscillations: An Aspect to Consider in Automatic Sleep Analysis, Journal of Medical Systems, Vol. 27, No. 4, August 2003
  • E. Huupponen et al., 2003, Fuzzy detection of EEG alpha without amplitude thresholding, Artificial Intelligence in Medicine, 24, 133-147
  • E. Huupponen et al., 2003, Sleep Depth Oscillations: An Aspect to Consider in Automatic Sleep Analysis, Journal of Medical Systems, Vol. 27, No. 4, 337-345
  • Jayashree Santhosh, 2002, Brain Computer Interface, Journal of Biomedical Technology
  • F. Jurysta et al., 2003, A study of the dynamic interactions between sleep EEG and heart rate variability in healthy young men, Clinical Neurophysiology, 114, 2146–2155
  • A. Kusiak et al., 2000, Autonomous decision-making: a data mining approach, IEEE Transactions on Biomedical engineering, VOL.4, NO.4, 274-284
  • J-P Lachaux et al., 2003, A simple measure of correlation across time, frequency and space between continuous brain signals, Journal of Neuroscience Methods, 123, 175-188
  • G. Lantza et al., 2003, Epileptic source localization with high density EEG: how many electrodes are needed? , Clinical Neurophysiology, 114, 63–69
  • M.manto et al., 2003, Dynamically Responsive Intervention for Tremor Suppression, IEEE Engineering in medicine and biology magazine, may/june, 120-132
  • P. Meinicke et al., 2002, Improving Transfer Rates in Brain Computer Interfacing: a Case Study, NIPS 2002: Online Preproceedings, IM06
  • M.I. Miga et al., 2002, Source localization using a current-density minimization approach, IEEE Transactions on Biomedical engineering, VOL.49, NO.7, 743-745
  • D.V. Moretti et al., 2003, Computerized processing of EEG–EOG–EMG artifacts for multicentric studies in EEG oscillations and event-related potentials, International Journal of Psychophysiology, Vol.47, Is.3, 199–216
  • F. Nebeker, 2002, Golden accomplishments in biomedical engineering , IEEE Engineering in Medicine and Biology Magazine, 0739-5175
  • B. Obermaier et al., 2001, Hidden Markov Models for online classification of signal trial EEG data, Pattern Recognition Letters, 22, 1299-1309
  • M. Palus et al., 2001, Synchronization and information flow in EEGs of epileptic patients, IEEE Engineering in Medicine and Biology Magazine, 65-71
  • H_J. Park et al., 2002, Automated Detection and Elimination of Periodic ECG Artifacts in EEG Using the Energy Interval Histogram Method, IEEE trans on biomedical engineering, VOL. 49, NO. 12, 1526-1533
  • T. Penzel, R. Conradt, 2000, Computer based sleep recording and analysis, Sleep Medicine Reviews, Vol. 4, No. 2, 131-148
  • W. Philips.,1996, Adaptive noise removal from biomedical signals using warped polynamials, IEEE trans on biomedical engineering,Vol. 43, No. 5, 480 - 492
  • Robi Polikar: The wavelet tutorial :Fourier Transform, Short Therm Fourier Transform, Continuous Wavelet Transform and Discrete Wavelet Transform
  • R. Quian Quiroga, H. Garcia, 2003, Single-trial event-related potentials with wavelet denoising, Clinical Neurophysiology, 114, 376–390
  • O. Rioul and M. Vetterli, 1991,Wavelets and Signal Processing, IEEE Signal Processing Magazine, Oct. 91, 14-38
  • C. Robert et al., 1998, Review of neural network applications in sleep research, Journal of Neuroscience Methods, 79, 187-193
  • P.H. Schimpf et al., 2002, Dipole models for the EEG and MEG, IEEE Transactions on Biomedical engineering, VOL.49, NO.5, 409-418
  • A. Schlögl et al., 1999, Artefact detection in sleep EEG by the use of Kalman filtering, Proceedings EMBEC'99, Part II, pp.1648-1649, 4-7
  • Y. Shena, 2003, Dimensional complexity and spectral properties of the human sleep EEG, Clinical Neurophysiology, 114, 199–209
  • P. Somol, P. Pudil, 2002, Feature selection toolbox, Elsevier Science Pattern Recognition, vol 35, 2749-2759
  • T. Takeuchi, 2003, EEG activities during elicited sleep onset REM and NREM periods reflect different mechanisms of dream generation, Clinical Neurophysiology, 114, 210–220
  • N.G. Tsagarakis, D. Caldwell, 2003, Development and Control of a ‘Soft-Actuated’ Exoskeleton for Use in Physiotherapy and Training, Autonomous Robots vol 15, 21–33
  • H.F. Van der loos et al, 2003, Development of Sensate and Robotic Bed Technologies for Vital Signs Monitoring and Sleep Quality Improvement, Autonomous Robots vol 15, 67-79
  • P. Varady et al., 2002, A novel method for the detection of apnea and hypopnea events in respiration signals, IEEE Transactions on Biomedical Engineering, VOL.49, Is.9, 936-942
  • P. Wahlberg, G. Salomonsson, 2003, Methods for alignment of multi-class signal sets, Signal Processing, Vol.83, Is.5, 911-1144
  • S. Wilson, R. Emerson, 2002, Spike detection: a review and comparison of algorithms, Clinical Neurophysiology, 113, 767-791
  • J. Wolpaw et al, 2002, Brain–computer interfaces for communication and control, Clinical Neurophysiology, 113, 1873-1881
  • Z. Zhang et al., 2001, Electroencephalogram analysis using fast wavelet transform, Computers in Biology and Medicine, 31, 429-440
  • L. Zhukov et al., 2000, Independent component analysis for EEG source localization, IEEE Engineering in Medicine and Biology Magazine, 87-96

Books
  • O. Benoit,F. Goldenberg, ed., 1997, Exploration du sommeil et de la vigilance chez l'adulte *, Editions Médicales Internationales
  • R. Kötter, ed., 2003, Neurosciences Databases *, Kluwer Academic Publishers
  • J. Malmivuo, R. Plonsey, 1995, Bioelectromagnetism : Principles and Applications of Bioelectric and Biomagnetic Fields (zip version), Oxford University Press
  • John Polich, 2003, Detection of change : event-related potential and fMRI findings *, Kluwer Academic Publishers
  • R. M. Rangayyan, 2002, Biomedical Signal Analysis *, John Wiley and Sons
  • E. Stanus, 1985, Contribution à la conception de dispositifs de détection et d'analyse des troubles du sommeil chez l'homme, FPMs PhD thesis
* available from TD

Magazines

Videos
  • Adaptive Brain Interfaces project (ABI) : The objective of the ABI project is to build self-learning individual brain interfaces (by means of neural networks) which interact wih computers by detecting five mental states from on-line spontaneous EEG signals.
    preparation phase / equipment / 4-day training / 5th day performance (full description)

    short documentary on a Finnish channel
    how the user managed to write a sentence in a few minutes during a live demo in Helsinki (May 2000)
  • Le rire.
    présence d'une forme de rire chez les rats

    provocation du rire par stimulation électrique
  • La vision.
    vision
  • Les Ronflements: video issue de matière grise (émission de la RTBF), dont le debut est un article sur le sommeil
    Ronflement et apnées du sommeil
  • Bionique: video issue de matière grise (émission de la RTBF) sur les travaux menés à l'UCL.
    video
  • Chaine TV sur le net: émissions sur le cerveau.
    rechercher "cerveau" sur la chaine BioTV, cliquer sur une référence concernant la semaine du cerveau et afficher le sommaire.
  • Les rêves: Que sont-ils, ont-ils une signification
    rêve
  • Transmision d'informations par voies tactiles.
    Afficheur lingual pour aveugles: les stimulations élèctriques portées sur la langue correspondent à l'image observée

    Gants avec senseurs: l'information des senseurs positionnés sur des gants est reproduite sur le front de la personne
  • Les régions sensibles du corps
    video
  • La méthode Tadoma permettant d'apprendre à parler aux personnes aveugles et sourdes
    La méthode est basée sur la perception de vibrations à différents endroits du visage
  • Le neurone
    Le neurone
  • La narcolepsie: pathologie du someil
    La narcolepsie: effets, conséquences
  • Jambe bionique: prothèse motorisée
    les capteurs situés sur la jambe saine permettent de controler le mouvement de la prothèse.
  • La maladie de Parkinson
    La maladie de Parkinson:

Labs

Conferences