M. Hallett

Publications of M. Hallett

• Localizing brain interactions from rhythmic EEG/MEG data

  Authors: G. Nolte, T. Holroyd, F. Carver, R. Coppola, M. Hallett

  Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE; 10/2004

  The interpretation of MEG/EEG data in terms of brain connectivity is largely obscured by artefacts of volume conduction, i.e. by the fact that a single source is observable in many channels. Here, weThe interpretation of MEG/EEG data in terms of brain connectivity is largely obscured by artefacts of volume conduction, i.e. by the fact that a single source is observable in many channels. Here, we analyze a measure which is insensitive to spurious connectivity arising from volume conducted "self-interaction". For rhythmic data such a measure can be given by the imaginary part of the cross-spectrum between EEG/MEG channels. For the derivation we essentially exploit that a signal is not time-lagged to itself. To localize the sources of this observed interaction we fit a model cross-spectrum consisting of N interacting dipoles to the sample cross-spectrum. The relation to the maximum likelihood estimator will be discussed in detail. The method is illustrated for MEG data of human alpha rhythm in eyes closed condition. The eigenvalues of the imaginary cross-spectrum clearly indicate the presence of at least 4 necessarily interacting sources. Fits of 2 to 6 dipoles in a realistic volume conductor all resulted in locations scattered in the mesial part of the occipital lobe.

• Visualization of spatiotemporal patterns of EEG rhythms during voluntary movements

  Authors: Ou Bai, G. Nolte, M. Zoltan, S. Vorbach, M. Hallett

  Computer-Based Medical Systems, 2004. CBMS 2004. Proceedings. 17th IEEE Symposium on; 07/2004

  The power change of electroencephalographs (EEG) rhythms is an indicator to investigate neuronal activation underlying EEG electrodes. We developed a software routine for analysis and visualizationThe power change of electroencephalographs (EEG) rhythms is an indicator to investigate neuronal activation underlying EEG electrodes. We developed a software routine for analysis and visualization of spatiotemporal patterns of EEG oscillations: event-related desynchronization (ERD) and event-related synchronization (ERS). Further, the spatiotemporal representation of task-related coherence (TRC) was also proposed for investigation of interregional neuronal connectivity. As concurrent artifacts and volume conduction effect may result incorrect or spatially blurred estimation of EEG features, we have embedded data preprocessing functions to reduce these effects including: correcting eye movement-related artifact, changing reference methods and spatial filtering undesirable neuronal activity. These functions were subsequently applied to study EEG time-frequency patterns during self-paced movements in normal volunteers.

• Localizing brain interactions from rhythmic EEG/MEG data.

  Authors: G. Nolte, T Holroyd, F Carver, R Coppola, M. Hallett

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 02/2004; 2:998-1001.

  The interpretation of MEG/EEG data in terms of brain connectivity is largely obscured by artefacts of volume conduction, i.e. by the fact that a single source is observable in many channels. Here, weThe interpretation of MEG/EEG data in terms of brain connectivity is largely obscured by artefacts of volume conduction, i.e. by the fact that a single source is observable in many channels. Here, we analyze a measure which is insensitive to spurious connectivity arising from volume conducted "self-interaction". For rhythmic data such a measure can be given by the imaginary part of the cross-spectrum between EEG/MEG channels. For the derivation we essentially exploit that a signal is not time-lagged to itself. To localize the sources of this observed interaction we fit a model cross-spectrum consisting of N interacting dipoles to the sample cross-spectrum. The relation to the maximum likelihood estimator will be discussed in detail. The method is illustrated for MEG data of human alpha rhythm in eyes closed condition. The eigenvalues of the imaginary cross-spectrum clearly indicate the presence of at least 4 necessarily interacting sources. Fits of 2 to 6 dipoles in a realistic volume conductor all resulted in locations scattered in the mesial part of the occipital lobe.