Introduction

BDF.jl is a julia module to read BIOSEMI 24-bit BDF files.

Download and Installation

The source code of BDF.jl is hosted on github: https://github.com/sam81/BDF.jl

It can be installed in Julia using

Pkg.add("BDF")

Usage

Load the module

using BDF

To read an entire BDF recording

dats, evtTab, trigChan, sysCodeChan = readBDF("res1.bdf")

dats is the nChannelXnSamples matrix containing the data. Note that the triggers are not contained in the dats matrix. The triggers can be retrieved either trough the event table (evtTab), or the raw trigger channel (trigChan). The eventTable is a dictionary containing the trigger codes evtTab["code"], the trigger indexes evtTab["idx"] (i.e. the sample numbers at which triggers occurred in the recording), and the trigger durations evtTab["dur"] (in seconds). The raw trigger channel returned in trigChan contains the trigger code for each recording sample. Additional Biosemi status codes (like CM in/out-of range, battery low/OK) are returned in sysCodeChan.

You can also read only part of a recording, the following code will read the first 10 seconds of the recording:

dats, evtTab, trigChan, statChan = readBDF("res1.bdf", from=0, to=10)

The readBDFHeader function can be used to get information on the BDF recording:

bdfInfo = readBDFHeader("res1.bdf")

Get the duration of the recording:

bdfInfo["duration"]

Get the sampling rate of each channel:

bdfInfo["sampRate"]

Get the channel labels:

bdfInfo["chanLabels"]

To read the information stored in the status channel you can use the decodeStatusChannel function

statusChanInfo = decodeStatusChannel(sysCodeChan)

this will return a dictionary with several arrays that indicate for each sample of the recordings whether CMS was in range, whether the battery charge was low, the speedmode of the system, and other information stored in the status channel.

Beware that BDF.jl does not check that you have sufficient RAM to read all the data in a BDF file. If you try to read a file that is too big for your hardware, your system may become slow or unresponsive. Initially try reading only a small amount of data, and check how much RAM that uses.

Bugs

Please, report any bugs on the project issues page