Personal Matlab Toolbox written for EEG processing and classification during the begginning of my thesis @GIPSA-lab (2013-2015). Not well maintained but released for educational purpose.
It is mostly unfinished job. Modules of my work has been released into other repositories in more advanced state. After 2015, the code was maintained in a different (private) repo.
If you found this repo and plan to use it (or part of it), please read below before.
Because I came from signal processing, I wasn't fond of the complex eeglab/fieldtrip structures with never ending attributes and parameters. So I tried to build the "minimum-viable" structure to process and classify EEG on my own and it was probably not better than EEG/fieldtrip structure anyway. While I tried to maintain this code at some point, the effort were dropped because only few engineers and PhD students were using it and I decided to share only piece of cleaner code in different repo. I finally, after many years of this code roting on my computer, decided to upload the ugly monster. Let say it is a monument of "how to not start a thesis".
to install LK TOOLBOX and related functions please, launch the script "installer" (run in matlab) thus you'll have acces to all the functions and script. For some functions, external requirements be needed (see external section below)
Of course the library is far from being well organized because of poor decisions during my thesis so don't expect to find everything perfectly in place. Also, because I had to work on several papers at the same time, sometimes functions have several versions co-existing to not loose compatibility of my code.
This folder contains mostly original work.
A messy folder containing plenty of experiments. Not very worthy if you don't have the data in hand but usefull if you want to see how the functions in lib folder were used.
I give some of the code that is mandatory for running my toolbox. But I'll probably need to add the two following folders if some functions doesn't work:
While I tried that most of the funtion can work directly with matrix (and not complicated structures), it may be easier to use to encapsulate your data into a structure, e.g. for using preprocessingEEG or plotEEG.
| FIELDNAME | SIZE/TYPE | DESCRIPTION | fieldtrip | eeglab | autre |
|---|---|---|---|---|---|
| Fs | scalar | sample rate in Hz | fsample | ‘srate’ | fs |
| Trigger | [nb samples x1 ] | Trigger channel of '0' with '1' at the start of each sweep. There are [nb epochs] '1' | sampleinfo | ||
| EpochClass | [nb epochs x1] | class of the sweeps (0 for Non-TARGET, 1 for TARGET). | trialinfo | ||
| Channels | [nb samples x nb channels] | continuous eeg recording | |||
| NoiseTrigger* | [nb samples x1 ] | the equivalent of Trigger but for the sweep of the noise. | |||
| By default, it takes the same. | |||||
| ElectrodesName* | {1 x nb channels} | the names of the electrodes | label |
ACSTPoptions is a structure with
Epoch_size: scalar, the length of the epoch window (in samples)
LatencyCorr_max: scalar, the maximum of samples for the latency
correction. Set 0 to disable the Latency correction.
Mask_Electrodes: vector of the selectionned electrodes. Usefull for the
automatic subspace selection (BestPz) and latency
correction (Latency).
Mask_Time: vector of the selectionned sample. Usefull for the
automatic subspace selection (BestPz) and latency
correction (Latency).
MaxIterLatency*: scalar, the maximum iteration allowed to compute the
latency correction. Default: 10.
SubspaceDim*: vector, containing all the subspace dimension (nb electrodes)
to test in descent order.
By default, it is equal to (nb_channels:-1:(nb_channels/2))
computeClassLat*: vector, containing all the class tag in which you want
to compute the latency correction. By default, it
computes it for all classes but it could be long (for
instance you can skip the non-target).
Weights*: Default: true.
option1(given) [nb epochs x1] vector, containing the weights for each
epoch if it is computed from an external function.
option2 (true/false) boolean. If true (default) the ACSTP compute the
weight for each epoch. If false, the weights are
desactivated (i.e. set to 1).
DISPLAY*: Boolean, should the comparative result between the arithmetic ensemble
average and the ACSTP should be display at the end. Default: true.ACSTPstruct is a structure with
EA: the ensemble average before ACSTP
EAcstp: the ensemble average corrected with latencies, weighted
and filtered + with the effect of overlapping
correction
As Bs Bt At: such as Xhat(:,:,k)=As{indClass}*Bs{indClass}'*W(k)*X(:,:,k)*Bt{indClass}*At{indClass}'
Each filter is a cell containing a matrix filter for
each class
Class: The tag and the order in which the filter are sorted
BestPz: Orders of the best subspace for the ACSTP for the given
Class
Weights: [nb epochs x1] the weights of each epoch
Latency: [nb epochs x1] the corrected offset of each epoch
Epoch_size: scalar, the length of the epoch window (in samples)