Hi, I'm trying to develop a system for automatically detecting various types of brain activity based on raw EEG data. I have gigabytes of raw data that I want to analyze, and I'm wondering if I could use the UIMA framework in this task.
The high level requirements is that given the raw EEG data the analysis system should produce a set of annotations that indicate which parts of the EEG data indicate certain kinds of brain activity like wake/sleep, REM/non-REM, etc. The typical approach is to use relative strengths of selected frequency bands for the classification, but I'm also experimented with self-organizing maps and other auto-adapting mechanisms in an attempt to increase the accuracy of the annotations. So far I've used custom code (both standalone applications and Matlab plugins) to manage things, but it seems like UIMA would be a nice framework for handling such operations. I guess I could implement both the frequency band and more advanced analyzers as UIMA analysis engines. Do you think UIMA would be a good match for my needs? Are there any (public) examples of doing something similar? Good pointers on where I should start? BR, Jukka Zitting
