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==Description==
==Description==


The sam_epi routine is a program designed for detecting sub-threshold epileptiform activity in the brain. It evaluates each voxel time series to determine kurtosis. The recommended pipeline is to run sam_cov, sam_wts, and then sam_epi. The utility [[NIFTIPeak|NIFTIPeak]] can then be used to identify voxels with peak or excess kurtosis values. These voxel locations can then be used as targets for a secondary run of sam_cov with a wide bandwidth, an sam_wts. Finally, DataEditor can be used to generate the virtual sensor time series for the targets, and the epileptologist can investigate the time series and mark spikes. Individual spikes can then be modeled with dipole fits.
The sam_epi routine is a program designed for detecting epileptiform activity in the brain in a semi-automated fashion, to prevent the necessity for the epileptologist to view each MEG channel time series individually. sam_epi evaluates the time series of each voxel in the brain to determine kurtosis. The recommended pipeline is to run sam_cov, sam_wts, and then sam_epi, using an initial bandwidth in the range 20-70Hz. The utility [[NIFTIPeak|NIFTIPeak]] can then be used to identify voxels with peak or excess kurtosis values. These voxel locations can then be used as targets for a secondary run of sam_cov and sam_wts with a wide bandwidth (1-150Hz). Finally, DataEditor can be used to generate the virtual sensor time series for the targets, and the epileptologist can investigate the time series to examine individual spikes.


Note that datasets must be continuous, and not epoched into trials.
Note that datasets must be continuous, and not epoched into trials.


==Usage==
==Usage==

Latest revision as of 10:46, 12 March 2019

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Description

The sam_epi routine is a program designed for detecting epileptiform activity in the brain in a semi-automated fashion, to prevent the necessity for the epileptologist to view each MEG channel time series individually. sam_epi evaluates the time series of each voxel in the brain to determine kurtosis. The recommended pipeline is to run sam_cov, sam_wts, and then sam_epi, using an initial bandwidth in the range 20-70Hz. The utility NIFTIPeak can then be used to identify voxels with peak or excess kurtosis values. These voxel locations can then be used as targets for a secondary run of sam_cov and sam_wts with a wide bandwidth (1-150Hz). Finally, DataEditor can be used to generate the virtual sensor time series for the targets, and the epileptologist can investigate the time series to examine individual spikes.

Note that datasets must be continuous, and not epoched into trials.

Usage

 sam_epi -r <dataset_name> -m <parameter_file_name> [options]

The -r flag designates the dataset name (with or without the .ds suffix), and -m designates the parameter file name.

Other options:

 -v  Verbose mode, without this flag sam_wts works silently except for error messages
 -h  Show help

Required Parameters:

 CovBand: Bandpass for the covariance matrices (and directory for weights files)
 ImageBand: Bandpass for the image
 ImageMetric: Must be KURTOSIS
 TimeInt: designates the duration for the boxcar integration
 CovType: Must be GLOBAL

Optional Parameters:

 FilterType: choose the applied filter, IIR vs. FFT 
 ImageDirectory: Directory where the output images are written, default is the SAM directory
 PrefixLength: Number of characters in the MEG dataset name used for naming MRI files

Output Files

Creates a single 3D NIFTI format image of kurtosis (g2). Output file will be named using the PrefixLength parameter.