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electromyography (emg) research  (MyoLearn)

 
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    MyoLearn electromyography (emg) research
    Electromyography (Emg) Research, supplied by MyoLearn, used in various techniques. Bioz Stars score: 99/100, based on 84469 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/cross-correlation+coefficient+function/Electromyography+(EMG)+Research/custom%40emg%4036154632
    Average 99 stars, based on 84469 article reviews
    electromyography (emg) research - by Bioz Stars, 2026-09
    99/100 stars

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    Related Articles

    Gait and locomotion:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Musculoskeletal conditions - pain and injury:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Movement and functional task assessment:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Sport and performance:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Exercise and training:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Rehabilitation and treatment:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Muscle activation and recruitment:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Muscle coordination and motor control:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Balance and postural control:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Activity Assay:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f

    Control:

    Article Title: Artificial intelligence models for automated and semiautomated analysis and interpretation of clinical electroencephalography.
    Article Snippet: psia 2023; 64: 754–68. 118 Varatharajah Y, Berry B, Cimbalnik J, et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15: 046035. 119 Yang B, Zhao B, Li C, et al. Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in f



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    (a-c) Example Voltron2 fluorescence images under targeted illumination with confocal slit width set to 4.5, 22.5, and 156 µm. Scale bar 50 µm. (d) Voltron2 fluorescence image over the same FOV but acquired without targeted illumination and with a confocal slit width of 156 µm. TI, targeted illumination. Scale bar 50 µm. (e,h,i) Comparison of spike Δ F / F , spike detection fidelity d ′ , and spike SNR measured with targeted illumination and confocal slit widths of 4.5, 11.3, 22.5, and 156 µm (n = 30 cells from 6 FOVs, 2 mice). Box plots: box, 25th (Q1, bottom line) to 75 th (Q3, top line) percentiles; whiskers, Q 1 − 1.5 × I Q R to Q 3 + 1.5 × I Q R , where I Q R = Q 3 − Q 1 ; middle line, median (m); notch, from m − 1.57 × I Q R / n to m + 1.57 × I Q R / n ; dots, measurement points. p < 0.05, p < 0.01, p < 0.001, no label if p ≥ 0.05, pairwise Wilcoxon signed-rank test, see for statistics. (f,g,j) Comparison of spike Δ F / F , photobleaching rate, and spike SNR measured with and without targeted illumination when using a 14 µm confocal slit. For (f,j), n = 19 cells from 5 FOVs, 2 mice. For (g), n = 92 cells from 5 FOVs, 2 mice. (l,m,n) Example images (scale bar, 20 µm) and corresponding fluorescence traces from two neighboring neurons with targeted illumination and confocal slit widths of 4.5, 22.5, and 156 µm (from top to bottom). Gray line, fluorescence traces; red line, extracted subthreshold Vm traces; r, <t>Pearson</t> cross-correlation coefficient between the subthreshold Vm traces from the 2 neurons.
    Pearson Cross Correlation Coefficients Matlab Function Corrcoef, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    (a-c) Example Voltron2 fluorescence images under targeted illumination with confocal slit width set to 4.5, 22.5, and 156 µm. Scale bar 50 µm. (d) Voltron2 fluorescence image over the same FOV but acquired without targeted illumination and with a confocal slit width of 156 µm. TI, targeted illumination. Scale bar 50 µm. (e,h,i) Comparison of spike Δ F / F , spike detection fidelity d ′ , and spike SNR measured with targeted illumination and confocal slit widths of 4.5, 11.3, 22.5, and 156 µm (n = 30 cells from 6 FOVs, 2 mice). Box plots: box, 25th (Q1, bottom line) to 75 th (Q3, top line) percentiles; whiskers, Q 1 − 1.5 × I Q R to Q 3 + 1.5 × I Q R , where I Q R = Q 3 − Q 1 ; middle line, median (m); notch, from m − 1.57 × I Q R / n to m + 1.57 × I Q R / n ; dots, measurement points. p < 0.05, p < 0.01, p < 0.001, no label if p ≥ 0.05, pairwise Wilcoxon signed-rank test, see for statistics. (f,g,j) Comparison of spike Δ F / F , photobleaching rate, and spike SNR measured with and without targeted illumination when using a 14 µm confocal slit. For (f,j), n = 19 cells from 5 FOVs, 2 mice. For (g), n = 92 cells from 5 FOVs, 2 mice. (l,m,n) Example images (scale bar, 20 µm) and corresponding fluorescence traces from two neighboring neurons with targeted illumination and confocal slit widths of 4.5, 22.5, and 156 µm (from top to bottom). Gray line, fluorescence traces; red line, extracted subthreshold Vm traces; r, <t>Pearson</t> cross-correlation coefficient between the subthreshold Vm traces from the 2 neurons.
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    (a-c) Example Voltron2 fluorescence images under targeted illumination with confocal slit width set to 4.5, 22.5, and 156 µm. Scale bar 50 µm. (d) Voltron2 fluorescence image over the same FOV but acquired without targeted illumination and with a confocal slit width of 156 µm. TI, targeted illumination. Scale bar 50 µm. (e,h,i) Comparison of spike Δ F / F , spike detection fidelity d ′ , and spike SNR measured with targeted illumination and confocal slit widths of 4.5, 11.3, 22.5, and 156 µm (n = 30 cells from 6 FOVs, 2 mice). Box plots: box, 25th (Q1, bottom line) to 75 th (Q3, top line) percentiles; whiskers, Q 1 − 1.5 × I Q R to Q 3 + 1.5 × I Q R , where I Q R = Q 3 − Q 1 ; middle line, median (m); notch, from m − 1.57 × I Q R / n to m + 1.57 × I Q R / n ; dots, measurement points. p < 0.05, p < 0.01, p < 0.001, no label if p ≥ 0.05, pairwise Wilcoxon signed-rank test, see for statistics. (f,g,j) Comparison of spike Δ F / F , photobleaching rate, and spike SNR measured with and without targeted illumination when using a 14 µm confocal slit. For (f,j), n = 19 cells from 5 FOVs, 2 mice. For (g), n = 92 cells from 5 FOVs, 2 mice. (l,m,n) Example images (scale bar, 20 µm) and corresponding fluorescence traces from two neighboring neurons with targeted illumination and confocal slit widths of 4.5, 22.5, and 156 µm (from top to bottom). Gray line, fluorescence traces; red line, extracted subthreshold Vm traces; r, Pearson cross-correlation coefficient between the subthreshold Vm traces from the 2 neurons.

    Journal: bioRxiv

    Article Title: Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy

    doi: 10.1101/2023.07.21.548930

    Figure Lengend Snippet: (a-c) Example Voltron2 fluorescence images under targeted illumination with confocal slit width set to 4.5, 22.5, and 156 µm. Scale bar 50 µm. (d) Voltron2 fluorescence image over the same FOV but acquired without targeted illumination and with a confocal slit width of 156 µm. TI, targeted illumination. Scale bar 50 µm. (e,h,i) Comparison of spike Δ F / F , spike detection fidelity d ′ , and spike SNR measured with targeted illumination and confocal slit widths of 4.5, 11.3, 22.5, and 156 µm (n = 30 cells from 6 FOVs, 2 mice). Box plots: box, 25th (Q1, bottom line) to 75 th (Q3, top line) percentiles; whiskers, Q 1 − 1.5 × I Q R to Q 3 + 1.5 × I Q R , where I Q R = Q 3 − Q 1 ; middle line, median (m); notch, from m − 1.57 × I Q R / n to m + 1.57 × I Q R / n ; dots, measurement points. p < 0.05, p < 0.01, p < 0.001, no label if p ≥ 0.05, pairwise Wilcoxon signed-rank test, see for statistics. (f,g,j) Comparison of spike Δ F / F , photobleaching rate, and spike SNR measured with and without targeted illumination when using a 14 µm confocal slit. For (f,j), n = 19 cells from 5 FOVs, 2 mice. For (g), n = 92 cells from 5 FOVs, 2 mice. (l,m,n) Example images (scale bar, 20 µm) and corresponding fluorescence traces from two neighboring neurons with targeted illumination and confocal slit widths of 4.5, 22.5, and 156 µm (from top to bottom). Gray line, fluorescence traces; red line, extracted subthreshold Vm traces; r, Pearson cross-correlation coefficient between the subthreshold Vm traces from the 2 neurons.

    Article Snippet: To analyze Vm-Vm correlations, we calculated Pearson cross-correlation coefficients (Matlab function corrcoef ) for the extracted subthreshold traces F s u b t from pairs of neurons.

    Techniques: Fluorescence, Comparison, IF-P