[![made-with-datalad](https://www.datalad.org/badges/made_with.svg)](https://datalad.org) # **EEGmanypipelines analysis** **a_eegmp_calculate_erp_tfr** - 50, 100, 150 Hz notch filter - CSD transform implemented via spherical splines using eeg1005 template - time-frequency transform using superlets - TFR: single-trial log10, no baseline - ERP: single-trial baseline -200:0 ms subtraction - condition averaging **b_scenecat_n1_bl** Assess effect of scene novelty on visual N1 peak amplitude.

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Topography around N1 trough:

**c1_taskPLS_novelty_frontal_erp** Assess effect of image novelty on fronto-central voltage.

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**c2_taskPLS_novelty_frontal_theta** Assess effect of image novelty on fronto-central theta power.

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**c3_taskPLS_novelty_posterior_alpha** Assess effect of image novelty on posterior alpha power.

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**d1_taskPLS_recognition_erp** Assess effect of successful 'old' recognition on voltage.

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**d2_taskPLS_recognition_erf** Assess effect of successful 'old' recognition on spectral power.

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**e1_taskPLS_memory_erp** Assess effect of subsequent memory on voltage. **e2_taskPLS_memory_erf** Assess effect of subsequent memory on spectral power.

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--- # **DataLad datasets and how to use them** This repository is a [DataLad](https://www.datalad.org/) dataset. It provides fine-grained data access down to the level of individual files, and allows for tracking future updates. In order to use this repository for data retrieval, [DataLad](https://www.datalad.org/) is required. It is a free and open source command line tool, available for all major operating systems, and builds up on Git and [git-annex](https://git-annex.branchable.com/) to allow sharing, synchronizing, and version controlling collections of large files. You can find information on how to install DataLad at [handbook.datalad.org/en/latest/intro/installation.html](http://handbook.datalad.org/en/latest/intro/installation.html). ### Get the dataset A DataLad dataset can be `cloned` by running ``` datalad clone ``` Once a dataset is cloned, it is a light-weight directory on your local machine. At this point, it contains only small metadata and information on the identity of the files in the dataset, but not actual *content* of the (sometimes large) data files. ### Retrieve dataset content After cloning a dataset, you can retrieve file contents by running ``` datalad get ` ``` This command will trigger a download of the files, directories, or subdatasets you have specified. DataLad datasets can contain other datasets, so called *subdatasets*. If you clone the top-level dataset, subdatasets do not yet contain metadata and information on the identity of files, but appear to be empty directories. In order to retrieve file availability metadata in subdatasets, run ``` datalad get -n ``` Afterwards, you can browse the retrieved metadata to find out about subdataset contents, and retrieve individual files with `datalad get`. If you use `datalad get `, all contents of the subdataset will be downloaded at once. ### Stay up-to-date DataLad datasets can be updated. The command `datalad update` will *fetch* updates and store them on a different branch (by default `remotes/origin/master`). Running ``` datalad update --merge ``` will *pull* available updates and integrate them in one go. ### Find out what has been done DataLad datasets contain their history in the ``git log``. By running ``git log`` (or a tool that displays Git history) in the dataset or on specific files, you can find out what has been done to the dataset or to individual files by whom, and when. ### More information More information on DataLad and how to use it can be found in the DataLad Handbook at [handbook.datalad.org](http://handbook.datalad.org/en/latest/index.html). The chapter "DataLad datasets" can help you to familiarize yourself with the concept of a dataset.