EEG data
Autoreject: Automated artifact rejection for MEG and EEG data
... hosts data from more than 50,000 subjects is yet another example of this ...of data, thus facilitating ...M/EEG data remains at the preprocessing stage with the annotation and rejection of ...
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Inverse conductivity recovery problem in a spherical geometry from EEG data: uniqueness, reconstruction and stability results
... We examine the inverse skull conductivity estimation problem, which aims at recovering the electrical con- ductivity properties of the skull from measurements given at the surface of the head by EEG measurements. ...
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Inverse skull conductivity estimation problems from EEG data
... We performed numerical analysis of the inverse conductivity estimation problem, using measurements and sources activities expanded on spherical harmonics basis (g km , b km ) simulated by the FindSources3D (FS3D [4]) ...
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MNE: Software for Acquiring, Processing, and Visualizing MEG/EEG Data
... MEG/EEG data acquisition to help detect abnormal brain signals and supplement ...and data scientists may work together to include custom data processing routines at the acquisition ...clean ...
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Removal of pulse artefact from EEG data recorded in MR environment at 3T. Setting of ICA parameters for marking artefactual components: application to resting-state data.
... affects EEG signals recorded in the magnetic resonance (MR) ...resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked ...
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Denoising of ictal EEG data using semi-blind source separation methods based on time-frequency priors
... simulated EEG data were generated using a realistic model developed by our team [8], [9], ...ictal EEG was obtained at the level of 32 scalp electrodes (placed over the scalp according to the ...
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Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data
... However, justifying the CP model of the STF and STWV tensors is not an easy task when two or more extended sources have to be localized as deeply analyzed in [9]. On the other hand, the occurrence of several amplitude ...
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Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches
... simulated data To quantitatively evaluate the performance of the four above-mentioned BSS approaches, we simulated 32- channels EEG data, with a spatiotemporal model devel- oped by our team ...model, ...
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Building Brain Invaders: EEG data of an experimental validation
... Participants 26 subjects participated in the experiment (7 females), with mean (sd) age 24.4 (2.76). The youngest subject was 21 and the oldest 31. One subject was excluded from the study due to material issues during ...
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SVM feature selection for multidimensional EEG Data
... noisy data are available rendering the discrimination task ...(ErrP) data sets, illustrate the efficiency of the sw-SVM from a physiological and a machine learning point of ...
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Decoding intracranial EEG data with multiple kernel learning method
... Filipovych R, Resnick SM, Davatzikos C. Multi-kernel classification for integration of clinical and imaging data: application to prediction of cognitive decline in older adults. Med Image Comput Comput Assist ...
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Decoding perceptual thresholds from MEG/EEG
... . We evaluate the performance of the method with a 10-fold stratified cross-validation (i.e., which preserves the percentage of sample for each class/coherence level in each fold). Fig. 2- a is the accuracy matrix ...
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A sparse EEG-informed fMRI model for hybrid EEG-fMRI neurofeedback prediction
... exploit EEG only, and predict an NF score of quality comparable to the NF score that could be achieved with a simultaneous NF-EEG-fMRI ...both EEG and fMRI are simultaneously acquired and used to ...
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Multivariate Temporal Dictionary Learning for EEG
... analyze EEG signals, this article proposes a data-driven method to obtain an adapted ...real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit ...
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Reproducibility in TMS-EEG studies: A call for data sharing, standard procedures and effective experimental control.
... open data sharing and direct comparisons across labora- tories in order to appraise the current state of the field and propose constructive exchanges to jointly de fine standard ...the EEG channel closest to ...
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Deep learning for continuous EEG analysis
... sleep EEG data can however be an interesting way to objectively evaluate them, bearing in mind that sleep annotations are far from perfect and that might hamper proper ...
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Independent EEG sources are dipolar.
... Brain-generated EEG data are generally considered to index synchronous aspects of local field potentials surrounding radially- arrayed cortical pyramidal cells ...scalp EEG should arise largely from ...
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MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music
... the EEG responses. An External Sync Unit (ESU) receives data from the EEG headset via Bluetooth and passes it over to the acquisition software along with timestamps associated to each EEG ...
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EEG andMEG Data Analysis in SPM8
... Figure 9: Axial, sagittal, and coronal views of the contrast image shown in Figure 8 , projected into MNI voxel space and superimposed on the template structural MRI image. (a) EEG data from the multimodal ...
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EEG-BIDS, an extension to the brain imaging data structure for electroencephalography
... used EEG data analysis tools in order to help EEG practitioners convert their existing data to the EDF or BrainVision Core Data ...Format. Data conversion utilities from many raw ...
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