<commit_before>Project progression presentation(~2 weeks from now):

What we need to do: (step 2 and 3 can be done at the same time)
1. Download the data and separate them into sessions (1 subjects all session ~15G) (DONE)
we will start with subject 4 (Steve downloaded data and they are in the google drive)
2. Preprocessing of data (WEEK 1)
(Depends on how the data look like)
motion correction?
trends correction?
3. Construction of design matrix: movie description analysis (WEEK 1)
- feature extraction: identify objects and action in movie description. 
and labeling it frame by frame; Preferably using label with wordNet to make use of its tree structure
Voxel modeling using ridge regression model (+cross validation using different parameter) (WEEK 2+3)
It is similar to single voxel modeling that is talked about in class(day 15)
In order to model all voxel at once, we just use matrix to represent all voxel
4. prediction and visualize results (WEEK 3)


Things to do now:
-- Data preprocessing 
1. plot the subject 1 data for single run (run 001-run 008); (plot it as activation<commit_msg>add project plans with people assignment<commit_after>Project progression presentation(~2 weeks from now):

What we need to do: (step 2 and 3 can be done at the same time) 
1. Download the data and separate them into sessions (1 subjects all session ~15G) (DONE)
we will start with subject 4 (Steve downloaded data and they are in the google drive)
2. Preprocessing of data (WEEK 1) (Raj, Cindy, Steve)
(Depends on how the data look like)
motion correction?
trends correction?
3. Construction of design matrix: movie description analysis (WEEK 1)(Aria, Rishi)
- feature extraction: identify objects and action in movie description. 
and labeling it frame by frame; Preferably using label with wordNet to make use of its tree structure
4. Voxel modeling using ridge regression model (+cross validation using different parameter) (WEEK 2+3) (Everyone)
It is similar to single voxel modeling that is talked about in class(day 15)
In order to model all voxel at once, we just use matrix to represent all voxel
5. prediction and visualize results (WEEK 3) (Cindy)


Things to do now:
-- Data preprocessing 
1. plot the subject 1 data for single run (run 001-run 008); (plot it as activation
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{'dir': 'git-commits-cleaned', 'id': '946506', 'n_tokens_mistral': 660, 'n_tokens_neox': 580, 'n_words': 369}