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mscohere m function  (MathWorks Inc)


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    MathWorks Inc mscohere m function
    Mscohere M Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 98/100, based on 1082 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/mscohere+function/Signal+Processing+Toolbox/pmc07481812-105-96-98
    Average 98 stars, based on 1082 article reviews
    mscohere m function - by Bioz Stars, 2026-09
    98/100 stars

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    Related Articles

    other:

    Article Title: Improved Myocardial Sodium Quantification at 7 T Using Interleaved 23 Na/ 1 H pTx MRI With Motion and Anatomy-Based B 1 Correction.
    Article Snippet: Within the expected cardiac frequency range (0.5–2 Hz), the dominant peak was identified using MATLAB’s findpeaks function (Signal Processing Toolbox), and its prominence (pcard), center frequency (fcard), and width (wcard) were extracted.

    Article Title: Improved Myocardial Sodium Quantification at 7 T Using Interleaved 23 Na/ 1 H pTx MRI With Motion and Anatomy-Based B 1 Correction.
    Article Snippet: For each 1H receive channel, the frequency power spectrum of the k0 navigator signal was computed and denoised using a Savitzky–Golay filter [25] (sgolayfilt in MATLAB’s Signal Processing Toolbox).

    Article Title: Age-related alterations in trunk extensor force control during isometric and isokinetic contractions.
    Article Snippet: In this study, MSC was computed using the mscohere function of the MATLAB Signal Processing Toolbox.

    Article Title: Reduced central alpha power at rest is associated with the risk of alcohol-induced blackout and frequency of non-REM parasomnia episodes in adults.
    Article Snippet: 2.1.4 | Resting- state EEG data analysis Cleaned EEG datasets were analyzed using built- in MATLAB functions, the Fieldtrip toolbox for MATLAB (Oostenveld et al., 2011), the Signal Processing toolbox for MATLAB, and the fitting oscillations & 1/f (FOOOF) toolbox for python using a MATLAB wrapper (Donoghue et al., 2020).

    Article Title: Variant-to-gene mapping identifies ARHGEF12 as a primary open-angle glaucoma effector gene
    Article Snippet: Fluorescent structures were outlined by hand using the roipoly function (Signal Processing Toolbox; The MathWorks).

    Article Title: Refining nonlinear parameters for evaluating gait stability in subjects with various musculoskeletal disorders: A technical note.
    Article Snippet: The resulting CoP coordinates were filtered with a zero-phase 6th-order Butterworth filter applying MATLAB’s Signal Processing Toolbox with a cut-off frequency of 20 Hz.

    Sampling:

    Article Title: Graph empirical mode decomposition and multiscale feature extraction for EEG-based classification of Alzheimer's disease and frontotemporal dementia.
    Article Snippet: Analyses were performed in MATLAB, using built-in functions from the Signal Processing, Wavelet, and Statistics and Machine Learning Toolboxes. .. The EEG signals were filtered at a 128 Hz sampling rate by MATLAB’s Signal Processing Toolbox with a 4th-order Butterworth band-pass filter (0.5–40 Hz) applied via the functions butter and filtfilt, followed by downsampling by a factor of 5 via decimate with the option ‘fir’. .. The channel graph was formed in MATLAB using corrcoef to calculate the inter-channel Pearson correlation coefficients as elements of a fully weighted adjacency matrix with the diagonal set to zero and without thresholding or sparsification.

    Software:

    Article Title: Htr3a receptors control attenuation of fear responses by modulating the corticolimbic activity and synchronization
    Article Snippet: .. Data were down sampled to 1000Hz, band pass filtered (1-100 Hz) and analyzed using custom-written script based on Matlab (R2017A, MathWorks) signal processing toolbox along with functions in Chronux open-source software package. ..



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    Image Search Results


    a (left) Topographic representation of the 60 Hz RIFT signal (uncombined planar gradiometers), averaged over participants in the 0.1–0.5 s interval ( t = 0 s is the onset of the search display). The RIFT response is confined to the occipital sensors. (right) Source modelling demonstrates that the RIFT response was primarily generated in the early visual cortex. The source grid has been masked to show the 1% most strongly activated grid points (MNI coordinates [0 −92 −4]). b Grand average spectrum, obtained by averaging over the participant-specific sensors of interest, indicating peaks at the 60 and 67 Hz stimulation frequency. c Grand average of the time-frequency representation of coherence between the MEG sensors and the RIFT signal, demonstrating an early, unspecific response in the gamma-band, followed by narrow-band responses at the stimulation frequency. d Set size 16. The responses to the distractor colour are significantly reduced for guided compared to unguided search ( p < 0.05; multiple comparison controlled using a cluster-based permutation test in the 0.1–0.5 s interval). There is no evidence for target boosting for this set size. e Set size 32. The RIFT responses to the guided target colour are significantly enhanced and the responses to the guided distractor colour are significantly reduced compared to the unguided search condition ( p < 0.05; cluster-based permutation test).

    Journal: Communications Biology

    Article Title: Guided visual search is associated with target boosting and distractor suppression in early visual cortex

    doi: 10.1038/s42003-025-08321-3

    Figure Lengend Snippet: a (left) Topographic representation of the 60 Hz RIFT signal (uncombined planar gradiometers), averaged over participants in the 0.1–0.5 s interval ( t = 0 s is the onset of the search display). The RIFT response is confined to the occipital sensors. (right) Source modelling demonstrates that the RIFT response was primarily generated in the early visual cortex. The source grid has been masked to show the 1% most strongly activated grid points (MNI coordinates [0 −92 −4]). b Grand average spectrum, obtained by averaging over the participant-specific sensors of interest, indicating peaks at the 60 and 67 Hz stimulation frequency. c Grand average of the time-frequency representation of coherence between the MEG sensors and the RIFT signal, demonstrating an early, unspecific response in the gamma-band, followed by narrow-band responses at the stimulation frequency. d Set size 16. The responses to the distractor colour are significantly reduced for guided compared to unguided search ( p < 0.05; multiple comparison controlled using a cluster-based permutation test in the 0.1–0.5 s interval). There is no evidence for target boosting for this set size. e Set size 32. The RIFT responses to the guided target colour are significantly enhanced and the responses to the guided distractor colour are significantly reduced compared to the unguided search condition ( p < 0.05; cluster-based permutation test).

    Article Snippet: This interval was chosen to avoid confounds with the broadband gamma response to the onset of the display Fig. . For each window, coherence between the MEG and RIFT signal was estimated based on the Fast Fourier Transform (FFT; zero-padded to 512 samples), and averaged over all windows to obtain one coherence value per trial (as implemented by the mscohere function in MATLAB).

    Techniques: Generated, Comparison

    a (left, top) The coherence between the signal-trial MEG data and RIFT signal was quantified using a sliding-window FFT approach, whereby the coherence was estimated by averaging over the 0.1 s in the 0.2–0.5 s interval (moved in steps of 0.025 s). (left, bottom) The topography of the coherence (combined planar gradiometers) suggests a response in the occipital sensors. (right) The RIFT response to the target and distractor was then concatenated into one vector, and submitted to a GLM with the factors target colour (T), unguided (U), distractor colour (D), and time-on-task (tot). b The contrast between the regressors associated with the target colour and unguided, and between distractor colour and unguided, was compared to 0 using a cluster-based permutation test (5000 permutations). The model fitted to the set size 16 conditions yielded no significant results but suggested reduced responses for distractors compared to unguided stimuli ( p = 0.08). c The model fitted to the set size 32 conditions replicated the magnitude-squared coherence results reported above, with a significantly stronger response to the target colour compared to unguided and a significantly reduced response to the distractor colour.

    Journal: Communications Biology

    Article Title: Guided visual search is associated with target boosting and distractor suppression in early visual cortex

    doi: 10.1038/s42003-025-08321-3

    Figure Lengend Snippet: a (left, top) The coherence between the signal-trial MEG data and RIFT signal was quantified using a sliding-window FFT approach, whereby the coherence was estimated by averaging over the 0.1 s in the 0.2–0.5 s interval (moved in steps of 0.025 s). (left, bottom) The topography of the coherence (combined planar gradiometers) suggests a response in the occipital sensors. (right) The RIFT response to the target and distractor was then concatenated into one vector, and submitted to a GLM with the factors target colour (T), unguided (U), distractor colour (D), and time-on-task (tot). b The contrast between the regressors associated with the target colour and unguided, and between distractor colour and unguided, was compared to 0 using a cluster-based permutation test (5000 permutations). The model fitted to the set size 16 conditions yielded no significant results but suggested reduced responses for distractors compared to unguided stimuli ( p = 0.08). c The model fitted to the set size 32 conditions replicated the magnitude-squared coherence results reported above, with a significantly stronger response to the target colour compared to unguided and a significantly reduced response to the distractor colour.

    Article Snippet: This interval was chosen to avoid confounds with the broadband gamma response to the onset of the display Fig. . For each window, coherence between the MEG and RIFT signal was estimated based on the Fast Fourier Transform (FFT; zero-padded to 512 samples), and averaged over all windows to obtain one coherence value per trial (as implemented by the mscohere function in MATLAB).

    Techniques: Plasmid Preparation