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25 results for “Empirical Mode Decomposition”
Investigation of the properties of conductivity signals in BK channels by Empirical Mode Decomposition
<p>The idea of the project is the comprehensive time-frequency analysis of ion current data registered from BK channels of the different cell lines and measured under the different experimental conditions. Decomposition of signals into individual frequency modes and application of non-linear measures in the form of Information Entropy or Hurst exponent to individual signal components will allow for a more detailed analysis of the information hidden behind the complex ionic conduction sequences. The sample data contains patch-clamp sequences. </p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4b. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>d et al., 2011). Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue.</p>
Fig. 17 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 17. The number of times each kernel returned the best result in the experiment performed for the BWC, RFC, OC and VDS criteria.
Fig. 10 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 10. Representation in PP for all analyzed series of the sets of vectors formed by the SD, SSC, TTC and EDT criteria.
Fig. 13 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 13. Box plots representing the accuracy of the vectors given by the SD, SSC, TTC and EDT criteria. The cross symbol represents the arithmetic mean.
Fig. 18 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 18. CM for the vectors given by the SD, SSC, TTC and EDT criteria. The parameters of the SVM were considered according to Table 5.
Fig. 7 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 7. Example of IMFs and the residue obtained by EMD with SD criterion applied to an audio signal of one second.
Fig. 6 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 6. SVM structure used in the proposed approach. Layer A is the input layer, with m passive elements; layer B is the hidden layer, with n(Xm) active sc elements; and layer C is the output layer, with one active linear element. In layer B, φ 2 = φn(Xm) 1, φ 1 = φn(Xm), w 2 = wn(Xm) 1 and w 1 = wn(Xm).
Fig. 15 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 15. Accuracy as a function of the number of training samples for the best series using minimum features and best overall series, respectively, both provided by the vectors generated by EMD with the EDT criterion.
Fig. 11 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 11. Representation in PP for all analyzed series of the sets of vectors formed by the BWC, RFC, OC and VDS criteria.
Fig. 5 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 5. Representation of the sets P and Q in the Cartesian plane and in PP, both with two classes. (a) The set P of vectors is linearly separable; (b) The set Q is not linearly separable. (c) PP representing the sets P and Q through PFE, suggesting a simple and a robust classifier for P and Q, respectively. The example is only illustrative and not related to the sets analyzed in this paper.
Fig. 14 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 14. Box plots representing the accuracy of the vectors given by the BWC, RFC, VDS and OC criteria. The cross symbol represents the arithmetic mean.
Fig. 19 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 19. CM for the vectors given by the BWC, RFC, OC and VDS criteria. The parameters of the SVM were considered according to Table 5.
Fig. 8 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 8. Energy distribution, on average, per IMF for the SD, SSC, TTC and EDT criteria.
Fig. 12 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 12. Block diagram illustrating the parameter combinations of the experiment performed.
Fig. 4 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 4. Preprocessing, analysis, SVM training and test steps.
Fig. 2 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 2. Setup for the proposed approach.
Fig. 3 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 3. Representation of the upper and lower envelopes (dashed lines) of a signal (solid line).
Fig. 9 in Empirical mode decomposition applied to acoustic detection of a cicadid pest
Fig. 9. Energy distribution, on average, per IMF for the BWC, RFC, OC and VDS criteria.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.