Standard analysis design steps: Difference between revisions
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(Created page with "UNDER CONSTRUCTION =Create a github page of the project= github.com ==Describe the data acquisition task by task== Auditory M100 task: Tone burst auditory stimuli were delivered bilaterally to the subject using the .... Somatosensory task: Pneumatic stimulation was performed on the index finger at a rate of approximately 2 times per second with a jitter of 20 ms ... ==For each task describe the auxilliary channels:== UADC001 was used for left hand patient response...") |
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UNDER CONSTRUCTION |
UNDER CONSTRUCTION <br> |
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Below are some general guidelines that help you analyze your data<br> |
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Thinking about this early on, is extremely helpful as it can prevent you from acquired a lot of data without the required inputs. Some things can not be corrected after the fact<br> |
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=Create a github page of the project= |
=Create a github page of the project= |
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github.com |
Sign up for a github account: github.com <br> |
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Create a github page of the project - this page can initially be a private repository if you prefer. Try to give it a reasonably focused name and NOT just Data_Analysis. This type of setup is often required for publication and can be very helpful in the understanding of the study for reproducibility. This excercise is very important in thinking about your end goal - otherwise your data analysis can become a wandering mess. Below are some things that are very helpful to put on a data analysis repository. |
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==Describe the data acquisition task by task== |
==Describe the data acquisition task by task== |
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Auditory M100 task: |
Auditory M100 task: |
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==For each task describe the auxilliary channels:== |
==For each task describe the auxilliary channels:== |
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UADC001 was used for left hand patient responses |
UADC001 was used for left hand patient responses |
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UADC002 was used for right hand patient responses |
UADC002 was used for right hand patient responses |
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UADC016 was used for the projector channel to correct for timing delays |
UADC016 was used for the projector channel to correct for timing delays |
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UPPT001 codes the stimuli values |
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===Describe what each PPT value codes=== |
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2: congruent word stimuli |
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4: incongruent word stimuli |
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6: distractor words |
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8: high noise condition |
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.... |
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==If your logfile incorporates special data that is not in your meg dataset - list these entries== |
==If your logfile incorporates special data that is not in your meg dataset - list these entries== |
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The logfile codes out ... |
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==If there is external data that is collected - describe how this will be incorporated with the data== |
==If there is external data that is collected - describe how this will be incorporated with the data== |
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External camera data was used to judge facial expressions. Timing triggers were sent to the |
External camera data was used to judge facial expressions. Timing triggers were sent to the |
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==Describe Your Hypothesis About The Results== |
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We expect to see activation in the left dorsolateral prefrontal area <br> |
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Timing - Prior literature has shown that <br> |
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=Develop code to write the triggers to your MEG dataset= |
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Python Dataframe Based: |
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examples: https://github.com/nih-megcore/hv_proc/tree/main/hv_proc/Process_scripts |
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requires nih2mne: https://github.com/nih-megcore/nih_to_mne.git |
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Commandline Based: |
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requires pyctf: https://github.com/nih-megcore/pyctf OR https://github.com/nih-megcore/pyctf-lite) |
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==Run the |
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Latest revision as of 10:05, 12 December 2024
UNDER CONSTRUCTION
Below are some general guidelines that help you analyze your data
Thinking about this early on, is extremely helpful as it can prevent you from acquired a lot of data without the required inputs. Some things can not be corrected after the fact
Create a github page of the project
Sign up for a github account: github.com
Create a github page of the project - this page can initially be a private repository if you prefer. Try to give it a reasonably focused name and NOT just Data_Analysis. This type of setup is often required for publication and can be very helpful in the understanding of the study for reproducibility. This excercise is very important in thinking about your end goal - otherwise your data analysis can become a wandering mess. Below are some things that are very helpful to put on a data analysis repository.
Describe the data acquisition task by task
Auditory M100 task:
Tone burst auditory stimuli were delivered bilaterally to the subject using the ....
Somatosensory task:
Pneumatic stimulation was performed on the index finger at a rate of approximately 2 times per second with a jitter of 20 ms ...
For each task describe the auxilliary channels:
UADC001 was used for left hand patient responses UADC002 was used for right hand patient responses UADC016 was used for the projector channel to correct for timing delays UPPT001 codes the stimuli values
Describe what each PPT value codes
2: congruent word stimuli 4: incongruent word stimuli 6: distractor words 8: high noise condition ....
If your logfile incorporates special data that is not in your meg dataset - list these entries
The logfile codes out ...
If there is external data that is collected - describe how this will be incorporated with the data
External camera data was used to judge facial expressions. Timing triggers were sent to the
Describe Your Hypothesis About The Results
We expect to see activation in the left dorsolateral prefrontal area
Timing - Prior literature has shown that
Develop code to write the triggers to your MEG dataset
Python Dataframe Based:
examples: https://github.com/nih-megcore/hv_proc/tree/main/hv_proc/Process_scripts requires nih2mne: https://github.com/nih-megcore/nih_to_mne.git
Commandline Based:
requires pyctf: https://github.com/nih-megcore/pyctf OR https://github.com/nih-megcore/pyctf-lite)
==Run the