Computational Sensorimotor Systems Lab:

Jonathan Z. Simon: Old Resources



Nayef Ahmar's MEG code:

sqd2mat.m - To port Squid files into Matlab

mat2sqd.m - To port Matlab files into Squid files.

adapt_noise_supp.m - Frequency domain least mean square method. Suppress environmental noise

    using 3 reference channels. Revised 12/05--do not use older versions.

adapt_noise_supp_mref.m - Frequency domain least mean square method. Suppress environmental

    and or artifact noise using any number of reference channels

FH_sig_tests.m - F and Hotelling's significance test

visual_filt_epoch_2s.m - Contrast raw and filtered data for one stimulus

Nayef Ahmar's adaptive filtering/noise suppression requires Sqdproject.
These functions read in a raw .sqd file and writes out a cleaned .sqd file. It is conceptually similar to MEG160's noise reduction ('CALM') but much more effective. The adaptive filtering method used is 'Block LMS'.

It takes some time to run, e.g. processing an 800 MB sqd file takes:
35 minutes on a 2.8 GHz Xeon with 2 GB RAM
45 minutes on a 1.5 GHz G4 with 2 GB RAM
2.5 hours on a 450 MHz Sun Ultra 10 with 1 GB RAM

Note that one of the input parameters trades memory use for time, so it will work with less memory but take more time (change it to a value smaller than the default of 1), if memory is an issue.

Yadong Wang's code:

This set of codes will read data from sqd file directly (by sqdread)and perform noise reduction like CALM and output the epoch data according to triggers into MATLAB format.

The main file is: read_epochs_noise_reduction.m

Read epochs from SQD file, given trigger, and epoch start and end time and perform multi-channel-noise-reduction.
It will generate two directories:
a. 'matdata' contains a set of matlab .mat file for each trigger. These are original data, without noise reduction
b. 'matdata_nr' contains one .mat file for each trigger, after noise reduction.

Other files:


Huan Luo's code:

Using readingMEG & WritingMEG instructions

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