From 82993ecd2a8cd4b5e3890214b22815e695aff2e8 Mon Sep 17 00:00:00 2001
From: Konstantinos Tsilimparis <105793148+contsili@users.noreply.github.com>
Date: Thu, 31 Oct 2024 06:53:17 +0100
Subject: [PATCH] OPM tutorial for CuttingEEG X (5) (#818)

* fixed figures

* fixed internal links

* Update squids_vs_opms.md

* removed files

* fixed figures

* Update squids_vs_opms.md

* Update squids_vs_opms.md

---------

Co-authored-by: Jan-Mathijs Schoffelen <1517611+schoffelen@users.noreply.github.com>
---
 workshop/cuttingeegx/squids_vs_opms.md | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/workshop/cuttingeegx/squids_vs_opms.md b/workshop/cuttingeegx/squids_vs_opms.md
index a39e9f55..b06ae97e 100644
--- a/workshop/cuttingeegx/squids_vs_opms.md
+++ b/workshop/cuttingeegx/squids_vs_opms.md
@@ -456,14 +456,23 @@ Now, we'll plot the 3D sensor topography for the time window [0.035, 0.050] seco
 ```
 %% Plotting the 3D sensor topography
 
+load mesh_brain mesh_brain
+load mesh_scalp mesh_scalp
+load avg_squid avg_squid
+load ctf275 ctf275
+
+% do not select the reference sensors
+index  = startsWith(ctf275.label,'M');
+pos275 = ctf275.chanpos(index,:);
+
 % average the activity of interest in the time window [0.035 0.050] sec
 sampling_rate = 1200; % in Hz
 prestim = 0.2;
 
-I1 = (prestim+0.035)*sampling_rate; 
-I2 = (prestim+0.050)*sampling_rate;
+I1 = (prestim + 0.035) * sampling_rate; 
+I2 = (prestim + 0.050) * sampling_rate;
 
-selected_avg = mean(avg_stim.avg(:, I1:I2), 2);
+selected_avg = mean(avg_squid.avg(:, I1:I2), 2);
 
 % Plot
 figure;
@@ -471,7 +480,7 @@ ft_plot_mesh(mesh_scalp, 'facealpha', 0.5, 'facecolor', 'skin', 'edgecolor', 'no
 hold on
 ft_plot_mesh(mesh_brain, 'facecolor', 'brain', 'edgecolor', 'none');
 hold on
-ft_plot_topo3d(pos275,selected_avg, 'facealpha', 0.9)
+ft_plot_topo3d(pos275, selected_avg, 'facealpha', 0.9)
 camlight
 view([360 0])
 
@@ -569,6 +578,7 @@ Next, we isolate the helmet region from the 3D scan using a bounding box. We wil
 
 ```
 %% Isolate helmet
+
 cfg           = [];
 cfg.method    = 'box';
 cfg.selection = 'outside';
@@ -666,11 +676,10 @@ transform_helmet2face = transform_scan2face/transform_scan2helmet;
 The transformation matrix ```'transform_helmet2face'``` can now be used to coregister the the OPM sensors with the MRI, which aligns the OPM sensors with head-based coordinate system. Before applying the transformation, we need to read the OPM sensors from the six fif files and combine them into a single sensor file. In the FieldLine system the OPM sensors slide into the smart helmet; the fif file contains the actual position of the sensors relative to the helmet. 
 
 ```
-%% Read the sensors
+%% Read and combine the sensors from the six recordings
 
 f = dir('*.fif'); % add the whole path to your .fif files
 f = struct2cell(f);
-sens = struct();
 
 for i=1:6
     sensfile = fullfile(f{2,i},f{1,i});