Signal Data Explorer Screenshots
This page shows some screenshots of SDE in action. If you want to try out SDE for yourself, you can download the free 30 day trial version and have a go at some of the SDE tutorials.
- Data visualisation
This screenshot shows SDE visualising four signals from a large scientific dataset. The variables displayed and scaling factors can all be customised to best meet the users requirements, whilst the control panels allows the user to navigate easily through the data. The box labelled EI-30 at the bottom of the screen allows the user to select an section of data that can be used as a search template.
- Dynamic Filtering
SDE allows the user to apply a wide range of filters to their data and to dynamically adjust the filter parameters to obtain the desired filtering effect. Multiple filters can be chained together to create more complex behaviour, which can then be saved into your filter library. In the screenshot below the user has first smoothed a noisy signal with a Gaussian filter and then simulated both a half and a full-wave rectifier, by chaining together a Binary and a Difference filter.
- Spike/Event Detection
SDE contains a very efficient and flexible spike/event detection algorithm. Simply specify an example spike shape and a set of similarity parameters and SDE will quickly identify the locations of the all the spikes that match your search criteria. You can therefore perform spike sorting by selecting multiple different spike shapes and searching for matches for each. In the screenshot below the user has performed spike detection on two different channels and is comparing the spike locations, shown by the vertical lines, to the original analogue data.
- Complex Event Detection
SDE allows the user to select any data segment and search for similar patterns either in the current dataset or in a pre-compiled database of their files. However, in addition to this, the SDE professional allows the user to define more complex events and search for multiple different patterns in multiple different variables.
Complex event detection is performed using the Task Planner. In the screenshot below, the user has defined an array of tasks, designed to identify coincident spiking activity across multiple channels.
The user then selects one of these tasks and has located two occurences of coincident spiking in activity, across all 3 channels, in the file. By double clicking on the results they can jump directly to these locations and investigate this section of the file more closely.
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