ripplelab Search Results


gui ripplelab  (MathWorks Inc)
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MathWorks Inc gui ripplelab
Gui Ripplelab, 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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ripplelab software  (MathWorks Inc)
90
MathWorks Inc ripplelab software
EEG files details
Ripplelab Software, 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
https://www.bioz.com/result/ripplelab software/product/MathWorks Inc
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ripplelab software - by Bioz Stars, 2026-03
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ripplelab toolbox  (MathWorks Inc)
90
MathWorks Inc ripplelab toolbox
EEG files details
Ripplelab Toolbox, 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
https://www.bioz.com/result/ripplelab toolbox/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
ripplelab toolbox - by Bioz Stars, 2026-03
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ripplelab  (MathWorks Inc)
90
MathWorks Inc ripplelab
EEG files details
Ripplelab, 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
https://www.bioz.com/result/ripplelab/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
ripplelab - by Bioz Stars, 2026-03
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short-term energy detector  (MathWorks Inc)
90
MathWorks Inc short-term energy detector
EEG files details
Short Term Energy Detector, 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
https://www.bioz.com/result/short-term energy detector/product/MathWorks Inc
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short-term energy detector - by Bioz Stars, 2026-03
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ripplelab hilbert detection algorithm implemented in  (MathWorks Inc)
90
MathWorks Inc ripplelab hilbert detection algorithm implemented in
Categories of events based on time-frequency analysis classification. Example of biomarkers on iEEG recordings (300 ms) for 7 out of 11 categories of events (S only, R only, FR only, S + R, S + FR, R + FR and S + R + FR). Each scenario shows a biomarker in ( I ) unfiltered iEEG (1st row); (ii) band-pass data in the frequency band 80–250 Hz (2nd row); (iii) time-frequency domain where ripples are seen as an island in the spectral content within the ripple frequency band (80–250 Hz) (3rd row); (iv) band-pass data in the frequency band 250–500 Hz (4th row) and ( V ) t I me-frequency domain where fast ripples are seen as an island in the spectral content within the fast ripple frequency band (250–500 Hz) (5th row). The detected events were visually inspected by two independent reviewers to exclude artefacts. In the filtered time domain (2nd and 4th rows), the black line represents the envelope of the analytic signal obtained using the <t>Hilbert</t> transform. The red dashed line represents the threshold value above which an event is considered a valid HFO. The white line in the time-frequency domain identifies the peak frequency for the HFO. S = spike; R = ripple; FR = fast ripple.
Ripplelab Hilbert Detection Algorithm Implemented In, 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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Average 90 stars, based on 1 article reviews
ripplelab hilbert detection algorithm implemented in - by Bioz Stars, 2026-03
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Image Search Results


EEG files details

Journal: Epilepsy research

Article Title: Harmonization of pipeline for detection of HFOs in a rat model of post-traumatic epilepsy in preclinical multicenter study on post-traumatic epileptogenesis

doi: 10.1016/j.eplepsyres.2019.03.008

Figure Lengend Snippet: EEG files details

Article Snippet: For Melbourne and UCLA, the HFOs detection was performed using RippleLab software (Matlab-based) ( Navarrete et al., 2016 ).

Techniques:

Categories of events based on time-frequency analysis classification. Example of biomarkers on iEEG recordings (300 ms) for 7 out of 11 categories of events (S only, R only, FR only, S + R, S + FR, R + FR and S + R + FR). Each scenario shows a biomarker in ( I ) unfiltered iEEG (1st row); (ii) band-pass data in the frequency band 80–250 Hz (2nd row); (iii) time-frequency domain where ripples are seen as an island in the spectral content within the ripple frequency band (80–250 Hz) (3rd row); (iv) band-pass data in the frequency band 250–500 Hz (4th row) and ( V ) t I me-frequency domain where fast ripples are seen as an island in the spectral content within the fast ripple frequency band (250–500 Hz) (5th row). The detected events were visually inspected by two independent reviewers to exclude artefacts. In the filtered time domain (2nd and 4th rows), the black line represents the envelope of the analytic signal obtained using the Hilbert transform. The red dashed line represents the threshold value above which an event is considered a valid HFO. The white line in the time-frequency domain identifies the peak frequency for the HFO. S = spike; R = ripple; FR = fast ripple.

Journal: Brain Communications

Article Title: Spikes on ripples are better interictal biomarkers of epilepsy than spikes or ripples

doi: 10.1093/braincomms/fcaf056

Figure Lengend Snippet: Categories of events based on time-frequency analysis classification. Example of biomarkers on iEEG recordings (300 ms) for 7 out of 11 categories of events (S only, R only, FR only, S + R, S + FR, R + FR and S + R + FR). Each scenario shows a biomarker in ( I ) unfiltered iEEG (1st row); (ii) band-pass data in the frequency band 80–250 Hz (2nd row); (iii) time-frequency domain where ripples are seen as an island in the spectral content within the ripple frequency band (80–250 Hz) (3rd row); (iv) band-pass data in the frequency band 250–500 Hz (4th row) and ( V ) t I me-frequency domain where fast ripples are seen as an island in the spectral content within the fast ripple frequency band (250–500 Hz) (5th row). The detected events were visually inspected by two independent reviewers to exclude artefacts. In the filtered time domain (2nd and 4th rows), the black line represents the envelope of the analytic signal obtained using the Hilbert transform. The red dashed line represents the threshold value above which an event is considered a valid HFO. The white line in the time-frequency domain identifies the peak frequency for the HFO. S = spike; R = ripple; FR = fast ripple.

Article Snippet: HFOs were automatically detected on each channel using the RippleLab Hilbert detection algorithm implemented in MATLAB (The MathWorks, Inc.).

Techniques: Biomarker Discovery