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meg data analysis  (MathWorks Inc)


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    MathWorks Inc meg data analysis
    Each trial began with a fixation-cross at the screen centre, followed by a “starting box” on the left. Fixating on the starting box would trigger the sentence onset. Participants ( n = 42) were instructed to read 188 sentences silently and then gaze at the “ending box” at the screen bottom to trigger the sentence offset. Randomly, 25% of trails included a simple comprehension question requiring a button response. Each sentence contained one or two target words, either of low or high lexical frequency. The target words were unpredictable from the prior context and all sentences were plausible. RIFT was applied by continuously flickering rectangle patches underneath the target and post-target words at frequencies f t and f p respectively (60 and 65 Hz sine waves, balanced across participants). A Gaussian mask was applied over the patch to smooth the sharp luminance changes around the edges to reduce their visibility across saccades. Two discs were positioned at the bottom corners of the screen, with their luminance oscillating at sine waves of f t and f p separately throughout the entire trial. These discs were covered by two photodiodes, and, during sentence presentation, their luminance changes mirrored those of the patches beneath the flickering words. Eye tracker and <t>MEG</t> <t>data</t> were acquired simultaneously. ITI, inter-trial interval.
    Meg Data Analysis, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2335 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "Parallel and dynamic attention allocation during natural reading"

    Article Title: Parallel and dynamic attention allocation during natural reading

    Journal: bioRxiv

    doi: 10.1101/2025.05.27.656336

    Each trial began with a fixation-cross at the screen centre, followed by a “starting box” on the left. Fixating on the starting box would trigger the sentence onset. Participants ( n = 42) were instructed to read 188 sentences silently and then gaze at the “ending box” at the screen bottom to trigger the sentence offset. Randomly, 25% of trails included a simple comprehension question requiring a button response. Each sentence contained one or two target words, either of low or high lexical frequency. The target words were unpredictable from the prior context and all sentences were plausible. RIFT was applied by continuously flickering rectangle patches underneath the target and post-target words at frequencies f t and f p respectively (60 and 65 Hz sine waves, balanced across participants). A Gaussian mask was applied over the patch to smooth the sharp luminance changes around the edges to reduce their visibility across saccades. Two discs were positioned at the bottom corners of the screen, with their luminance oscillating at sine waves of f t and f p separately throughout the entire trial. These discs were covered by two photodiodes, and, during sentence presentation, their luminance changes mirrored those of the patches beneath the flickering words. Eye tracker and MEG data were acquired simultaneously. ITI, inter-trial interval.
    Figure Legend Snippet: Each trial began with a fixation-cross at the screen centre, followed by a “starting box” on the left. Fixating on the starting box would trigger the sentence onset. Participants ( n = 42) were instructed to read 188 sentences silently and then gaze at the “ending box” at the screen bottom to trigger the sentence offset. Randomly, 25% of trails included a simple comprehension question requiring a button response. Each sentence contained one or two target words, either of low or high lexical frequency. The target words were unpredictable from the prior context and all sentences were plausible. RIFT was applied by continuously flickering rectangle patches underneath the target and post-target words at frequencies f t and f p respectively (60 and 65 Hz sine waves, balanced across participants). A Gaussian mask was applied over the patch to smooth the sharp luminance changes around the edges to reduce their visibility across saccades. Two discs were positioned at the bottom corners of the screen, with their luminance oscillating at sine waves of f t and f p separately throughout the entire trial. These discs were covered by two photodiodes, and, during sentence presentation, their luminance changes mirrored those of the patches beneath the flickering words. Eye tracker and MEG data were acquired simultaneously. ITI, inter-trial interval.

    Techniques Used:



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    Image Search Results


    Each trial began with a fixation-cross at the screen centre, followed by a “starting box” on the left. Fixating on the starting box would trigger the sentence onset. Participants ( n = 42) were instructed to read 188 sentences silently and then gaze at the “ending box” at the screen bottom to trigger the sentence offset. Randomly, 25% of trails included a simple comprehension question requiring a button response. Each sentence contained one or two target words, either of low or high lexical frequency. The target words were unpredictable from the prior context and all sentences were plausible. RIFT was applied by continuously flickering rectangle patches underneath the target and post-target words at frequencies f t and f p respectively (60 and 65 Hz sine waves, balanced across participants). A Gaussian mask was applied over the patch to smooth the sharp luminance changes around the edges to reduce their visibility across saccades. Two discs were positioned at the bottom corners of the screen, with their luminance oscillating at sine waves of f t and f p separately throughout the entire trial. These discs were covered by two photodiodes, and, during sentence presentation, their luminance changes mirrored those of the patches beneath the flickering words. Eye tracker and MEG data were acquired simultaneously. ITI, inter-trial interval.

    Journal: bioRxiv

    Article Title: Parallel and dynamic attention allocation during natural reading

    doi: 10.1101/2025.05.27.656336

    Figure Lengend Snippet: Each trial began with a fixation-cross at the screen centre, followed by a “starting box” on the left. Fixating on the starting box would trigger the sentence onset. Participants ( n = 42) were instructed to read 188 sentences silently and then gaze at the “ending box” at the screen bottom to trigger the sentence offset. Randomly, 25% of trails included a simple comprehension question requiring a button response. Each sentence contained one or two target words, either of low or high lexical frequency. The target words were unpredictable from the prior context and all sentences were plausible. RIFT was applied by continuously flickering rectangle patches underneath the target and post-target words at frequencies f t and f p respectively (60 and 65 Hz sine waves, balanced across participants). A Gaussian mask was applied over the patch to smooth the sharp luminance changes around the edges to reduce their visibility across saccades. Two discs were positioned at the bottom corners of the screen, with their luminance oscillating at sine waves of f t and f p separately throughout the entire trial. These discs were covered by two photodiodes, and, during sentence presentation, their luminance changes mirrored those of the patches beneath the flickering words. Eye tracker and MEG data were acquired simultaneously. ITI, inter-trial interval.

    Article Snippet: The MEG data analysis was conducted using MATLAB R2020a (Mathworks Inc, USA), incorporating the FieldTrip toolbox (version 20200220; Oostenveld et al., 2011), the FLUX MEG analysis pipeline , and custom-made scripts.

    Techniques:

    Article part of the special issue by order of publication date.

    Journal: Frontiers in Neuroscience

    Article Title: Editorial: From Raw MEG/EEG to Publication: How to Perform MEG/EEG Group Analysis With Free Academic Software

    doi: 10.3389/fnins.2022.854471

    Figure Lengend Snippet: Article part of the special issue by order of publication date.

    Article Snippet: BrainWave: A MATLAB Toolbox for Beamformer Source Analysis of MEG Data , Jobst et al. , Brainwave site , GNU/ GPL , MEG , Beamformer , MATLAB , , Yes.

    Techniques: Software, Functional Assay, Biomarker Discovery, Activity Assay, Selection, Imaging