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software tools for eeg and meg data analysis  (BESA GmbH)

 
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    BESA GmbH software tools for eeg and meg data analysis
    Software Tools For Eeg And Meg Data Analysis, supplied by BESA GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/eeg+data+analysis+tools/software+tools/pmc08960642-361-20-9
    Average 90 stars, based on 1 article reviews
    software tools for eeg and meg data analysis - by Bioz Stars, 2026-11
    90/100 stars

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    other:

    Article Title: Visualizing spikes in source-space: Rapid and efficient evaluation of magnetoencephalography.
    Article Snippet: Objective: reviewing magnetoencephalography (MEG) recordings is time-consuming: signals from the 306 MEG-sensors are typically reviewed divided into six arrays of 51 sensors each, thus browsing each recording six times in order to evaluate all signals.. A novel method of reconstructing the MEG signals in source-space was developed using a source-montage of 29 brain-regions and two spatial components to remove magnetocardiographic (MKG) artefacts.. Our objective was to evaluate the accuracy of reviewing MEG in source-space.

    Article Title: Validating EEG source imaging using intracranial electrical stimulation
    Article Snippet: M.R. is an employee of BESA GmbH, a company, which develops and provides software tools for EEG and MEG data analysis.

    Article Title: DC-EEG recordings of mindfulness.
    Article Snippet: Accepted Manuscript DC-EEG recordings of mindfulness Ernst Rodin, Harald Bornfleth, Michael Johnson PII: S1388-2457(17)30022-6 DOI: http://dx.doi.org/10.1016/j.clinph.2016.12.031 Reference: CLINPH 2008033 To appear in: Clinical Neurophysiology Received Date: 27 September 2016 Revised Date: 25 November 2016 Accepted Date: 29 December 2016 Please cite this article as: Rodin, E., Bornfleth, H., Johnson, M., DC-EEG recordings of mindfulness, Clinical Neurophysiology (2017), doi: http://dx.doi.org/10.1016/j.clinph.2016.12.031 This is a PDF file of an unedited manuscript that has been accepted for publication.. As a service to our customers we are providing this early version of the manuscript.. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form.

    Article Title: LORETA With Cortical Constraint: Choosing an Adequate Surface Laplacian Operator
    Article Snippet: HB is an employee of BESA GmbH, a company which develops and provides software tools for EEG and MEG data analysis.

    Article Title: Acoustic-level and language-specific processing of native and non-native phonological sequence onsets in the low gamma and theta-frequency bands
    Article Snippet: MR is an employee of BESA, GmbH, a company that develops and provides software tools for EEG and MEG data analysis.

    Software:

    Article Title: EEG-fMRI: Ballistocardiogram Artifact Reduction by Surrogate Method for Improved Source Localization
    Article Snippet: .. MR, HB, J-HC, NI, and PB were employees of BESA GmbH, a company which develops and provides software tools for EEG and MEG data analysis. ..

    Article Title: Development of motion speed perception from infancy to early adulthood: a high-density EEG study of simulated forward motion through optic flow
    Article Snippet: .. EEG raw data were segmented using Net Station Tools software version 5.4.1.2 and analysed offline with Brain Electrical Source Analysis (BESA, GmbH) version 6.1 research software. ..

    Article Title: Epileptiform discharge propagation: Analyzing spikes from the onset to the peak.
    Article Snippet: Objective: To investigate how often discharge propagation occurs within the spikes recorded in patients evaluated for epilepsy surgery, and to assess its impact on the accuracy of source imaging.. Methods: Data were analyzed from 50 consecutive patients who had presurgical workup.. Discharge propagation was analyzed using sequential voltage-maps of the averaged spikes, and principal components analysis.



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    Exemplary <t>EEG-BIDS</t> dataset with previews of EEG files. The left side of the figure shows a standard BIDS directory tree with the root containing files describing the dataset in general (“README”, “dataset_description.json”), a file describing the participants (“participants.tsv”), and as several JSON files (“participants.json”, “task-TASKNAME_events.json”), which contain the description necessary to understand the contents of the TSV files. Note that JSON files at high levels get inherited by lower levels unless overridden (the Inheritance Principle). Next to the files at the root, there is a stimuli and a sourcedata directory that can be used to save the respective study data. Most important are the subject directories named “sub- <sub-label> ” for each study participant. Nested in the subject directories are all recorded data split over modalities (eeg and anat, for the EEG and structural MRI data respectively). The right side of the figure shows the contents of the eeg modality directory, including the raw <t>EEG</t> <t>data</t> (1) and associated metadata (2). An events.tsv file (3) specifies all events that were recorded during the session and can reference presented stimuli with the stimuli directory of the dataset (see stim_file column). A “channels.tsv” file (4) provides further information about the raw EEG data and can contain information not present in the raw EEG data file such as filter settings and channel status (good/bad). Finally, an “electrodes.tsv” file (5) and an accompanying “coordsystem.json” file (6) provide electrode locations and specify which coordinate framework to use to interpret the electrode locations (for example with respect to a T1 weighted MRI scan).
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    Exemplary EEG-BIDS dataset with previews of EEG files. The left side of the figure shows a standard BIDS directory tree with the root containing files describing the dataset in general (“README”, “dataset_description.json”), a file describing the participants (“participants.tsv”), and as several JSON files (“participants.json”, “task-TASKNAME_events.json”), which contain the description necessary to understand the contents of the TSV files. Note that JSON files at high levels get inherited by lower levels unless overridden (the Inheritance Principle). Next to the files at the root, there is a stimuli and a sourcedata directory that can be used to save the respective study data. Most important are the subject directories named “sub- <sub-label> ” for each study participant. Nested in the subject directories are all recorded data split over modalities (eeg and anat, for the EEG and structural MRI data respectively). The right side of the figure shows the contents of the eeg modality directory, including the raw EEG data (1) and associated metadata (2). An events.tsv file (3) specifies all events that were recorded during the session and can reference presented stimuli with the stimuli directory of the dataset (see stim_file column). A “channels.tsv” file (4) provides further information about the raw EEG data and can contain information not present in the raw EEG data file such as filter settings and channel status (good/bad). Finally, an “electrodes.tsv” file (5) and an accompanying “coordsystem.json” file (6) provide electrode locations and specify which coordinate framework to use to interpret the electrode locations (for example with respect to a T1 weighted MRI scan).

    Journal: Scientific Data

    Article Title: EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

    doi: 10.1038/s41597-019-0104-8

    Figure Lengend Snippet: Exemplary EEG-BIDS dataset with previews of EEG files. The left side of the figure shows a standard BIDS directory tree with the root containing files describing the dataset in general (“README”, “dataset_description.json”), a file describing the participants (“participants.tsv”), and as several JSON files (“participants.json”, “task-TASKNAME_events.json”), which contain the description necessary to understand the contents of the TSV files. Note that JSON files at high levels get inherited by lower levels unless overridden (the Inheritance Principle). Next to the files at the root, there is a stimuli and a sourcedata directory that can be used to save the respective study data. Most important are the subject directories named “sub- ” for each study participant. Nested in the subject directories are all recorded data split over modalities (eeg and anat, for the EEG and structural MRI data respectively). The right side of the figure shows the contents of the eeg modality directory, including the raw EEG data (1) and associated metadata (2). An events.tsv file (3) specifies all events that were recorded during the session and can reference presented stimuli with the stimuli directory of the dataset (see stim_file column). A “channels.tsv” file (4) provides further information about the raw EEG data and can contain information not present in the raw EEG data file such as filter settings and channel status (good/bad). Finally, an “electrodes.tsv” file (5) and an accompanying “coordsystem.json” file (6) provide electrode locations and specify which coordinate framework to use to interpret the electrode locations (for example with respect to a T1 weighted MRI scan).

    Article Snippet: We are collaborating with the developers of the most widely used EEG data analysis tools in order to help EEG practitioners convert their existing data to the EDF or BrainVision Core Data Format.

    Techniques: