From eee70249bc5888a1d9777c642f8db219b605d0d6 Mon Sep 17 00:00:00 2001 From: "Steven.le-cam" <steven.le-cam@univ-lorraine.fr> Date: Sun, 10 Sep 2023 12:02:06 +0200 Subject: [PATCH] some comments added to readme file and code --- README.md | 61 +++++++++---------------------------------------------- VBDS.m | 18 ++++++++++++---- main.m | 8 ++++---- 3 files changed, 28 insertions(+), 59 deletions(-) diff --git a/README.md b/README.md index 78a76c6..4a96df0 100644 --- a/README.md +++ b/README.md @@ -1,64 +1,23 @@ # LFP Despiking and Spike Sorting - - -## Getting started - -To make it easy for you to get started with GitLab, here's a list of recommended next steps. - -Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)! - -## Add your files - -- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files -- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command: - -``` -cd existing_repo -git remote add origin https://gitlab.univ-lorraine.fr/lecam5/lfp-despiking-and-spike-sorting.git -git branch -M main -git push -uf origin main -``` - -## Integrate with your tools - -- [ ] [Set up project integrations](https://gitlab.univ-lorraine.fr/lecam5/lfp-despiking-and-spike-sorting/-/settings/integrations) - -## Collaborate with your team - -- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) -- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) -- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) -- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) -- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html) - -## Test and Deploy - -Use the built-in continuous integration in GitLab. - -- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html) -- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/) -- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html) -- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/) -- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html) - -*** - - -## Name -LFP Despiking and spike Sorting - ## Description -Le Cam et al. 2023, A Bayesian approach for simultaneous spike/LFP separation and spike sorting, Journal of Neural Engineering +Implement the method described in "Le Cam et al. 2023, A Bayesian approach for simultaneous spike/LFP separation and spike sorting, Journal of Neural Engineering, 2023" +Simultaneous spike sorting and spike/lfp separation based on an iterative variational bayesian approach. +Modify the main.m file to load your own data. The current version can only process one channel at a time. The code comes with two examples: +- 20 seconds of simulated signals from "Le Cam et. al., 2023" +- 10 minutes of simulated signals from "Camunas-Mesa et. al., 2013" + +The main parameters of the method can be changed on top of the main file, in particular the spike detection threshold factor 'coef' and the number of wavelet features 'nbfeat' to extract for classification. ## Configuration Matlab Wavelet toolbox required ## Support -Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc. +This is a first sharable version of the code, probably need some further amelioration to be more user friendly and robust to any kind of data. +Please send me an email (steven.le-cam@univ-lorraine.fr) for questions or if you need any support, I will be happy to help and bug corrections will benefit to future users of the code ## License -For open source projects, say how it is licensed. +If you use this code to process your data, please cite "Le Cam et al. 2023, A Bayesian approach for simultaneous spike/LFP separation and spike sorting, Journal of Neural Engineering, 2023" (DOI 10.1088/1741-2552/acc210) diff --git a/VBDS.m b/VBDS.m index b42b0ba..c1354e6 100644 --- a/VBDS.m +++ b/VBDS.m @@ -1,6 +1,16 @@ function [paramVBDS,Spike,w]=VBDS(xb,Fs,paramVBDS,display,verbose) - - +% Pipeline for spike detection, feature extraction and spike/LFP +% classification/separation +% input: +% xb: monochannel data +% Fs: data sampling rate +% paramVBDS: main parameters of the method +% display: display mode +% verbose: verbose mode +% output: +% paramVDBS: updated method parameters +% Spike: matrix of estimated spike waveforms +% w: despiked LFP %% %Band pass filtering to highlight the spikes and thresholding @@ -24,7 +34,7 @@ else gB = fitLFPpowerSpectrum(xb',.01,2*10e3,Fs,display); gB=fftshift(gB); -%% Initial guess on the number of classes (using GMM-EM) +%% If mode set to 1: automatic initial guess on the number of classes (using GMM-EM) if paramVBDS.mode @@ -40,7 +50,7 @@ end %% -% Classif and despiking (with iterations if mode=1) +% Classif and despiking % [paramVBDS,Spike,w]=LFPSpikeDemixing(xbr,xb,Fs,ideltasr,gB,wfeat,paramVBDS,indsupp,verbose); diff --git a/main.m b/main.m index 6825569..34f63d8 100644 --- a/main.m +++ b/main.m @@ -10,11 +10,11 @@ paramVBDS.spike_band=[300 3000]; % set the frequency band for spikes detection paramVBDS.coef=4; %coef for robust threholding (spike detection) paramVBDS.coefH=5; % coef for outlier rejection (rejection thr = coefH*detection thr) paramVBDS.ds=3; % set spike duration in ms (3ms or above) -paramVBDS.nbfeat=6; %number of wav features for classif -paramVBDS.mode=1; % 0: number of class given by user, 1: automatic mode (with nbiter iterations) +paramVBDS.nbfeat=6; %number of wavelet features for classification +paramVBDS.mode=1; % 0: number of class given by user, 1: automatic mode (GMM initialization) paramVBDS.K=2; % considered if mode=0 -paramVBDS.nbiter=10; -paramVBDS.rangeK=1; +paramVBDS.rangeK=1; % Explore K values in the range [K-rangeK K+rangeK] +paramVBDS.nbiter=10; % number of iteration for each explored value of K display=true; % display some figures during processing verbose=true; % verbose mode -- GitLab