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**Tables of Contents**
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[[_TOC_]]
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# Intro
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Our pipeline will be based on the
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[eeg_pipe_asr_amica](https://git.sharcnet.ca/bucanl_pipelines/eeg_pipe_asr_amica)
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pipeline so we can show the vast array of options and features available to batch_context.
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Similar to the
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[eeg_pipe_asr_amica](https://git.sharcnet.ca/bucanl_pipelines/eeg_pipe_asr_amica)
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we will start with initialization, fork to several amica processes, then merge back into one process.
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## Diagram
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![diagram](/uploads/3a9555707c7523271f67076ae154e988/diagram.png)
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## Description
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* s01 will do a band pass on the raw input data. We assume the data has already been converted to EEGLAB data, if you don't have EEGLAB data we will provide some in later steps.
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* s02-s03 will do Amica runs
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* s04 will compare the least likelihoods and record them in the marks data
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*Updated/Verified by Brad Kennedy on 2017-08-10*
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