"load_acq.m" will load BIOPAC's AcqKnowledge (ACQ) data to a structure containing ACQ header field and data matrix field.
It is based on Application Note #156 from BIOPAC web site (see attachment). The note mentioned that: "This document describes file formatting for all Windows versions of AcqKnowledge 3.9.x or below". Thanks to the open Python source code provided by Nathan Vack, this program can also read AcqKnowledge 4.0 & 4.1 data (with no documentation from BIOPAC). Compressed data is not supported by this program.
Usage: acq = load_acq(filename, [force_chan_by_chan_load])
acq - AcqKnowledge file structure containing ACQ header field, and data matrix.
filename - BIOPAC's AcqKnowledge file
force_chan_by_chan_load is an optional argument. By default, this optional flag will be set to 1, which means that when you use acq = load_acq(fname), the data will be loaded one channel after the other. This can avoid memory crash when you load very large ACQ data. If your ACQ data is not huge, I suggest that you set this optional flag to 0, i.e. acq = load_acq(fname, 0). In this case, the program will read data depending on the data type. If the program detects that the data type in ACQ file are different from channel to channel, it will still read data channel by channel. Otherwise, it will read whole data in one block (a lot faster than using traditional way from channel to channel with the same result).
A script called "acq2mat.m" is provided to help you convert all .acq files to .mat files in all specified folders in advance, since to load corresponding .mat files will be much quicker. i.e. In your MATLAB code, instead of using: acq = load_acq('MyGSR.acq'); you can now use: load('MyGSR.mat');
Jimmy Shen (2021). Load BIOPAC ACQ (AcqKnowledge for PC) data (https://www.mathworks.com/matlabcentral/fileexchange/14562-load-biopac-acq-acqknowledge-for-pc-data), MATLAB Central File Exchange. Retrieved .
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