--- a
+++ b/getData.m
@@ -0,0 +1,108 @@
+% An example to get the BCI competition IV datasets 2a, 2b is the same
+% Data from: http://www.bbci.de/competition/iv/
+% using open-source toolbox Biosig on MATLAB: http://biosig.sourceforge.net/
+% Just an example, you should change as you need.
+
+function data = getData(subject_index)
+
+subject_index = 6; % 1-9
+
+%% T data
+session_type = 'T'; % T and E
+dir_1 = ['D:\MI\BCICIV_2a_gdf\A0',num2str(subject_index),session_type,'.gdf']; % set your path of the downloaded data
+[s, HDR] = sload(dir_1);
+
+% Label 
+% label = HDR.Classlabel;
+labeldir_1 = ['D:\MI\true_labels\A0',num2str(subject_index),session_type,'.mat'];
+load(labeldir_1);
+label_1 = classlabel;
+
+% construct sample - data Section 1000*22*288
+Pos = HDR.EVENT.POS; % use POS to get trials
+% Dur = HDR.EVENT.DUR;
+Typ = HDR.EVENT.TYP;
+
+k = 0;
+data_1 = zeros(1000,22,288);
+for j = 1:length(Typ)
+    if  Typ(j) == 768
+        k = k+1;
+        data_1(:,:,k) = s((Pos(j)+500):(Pos(j)+1499),1:22);
+    end
+end
+
+% wipe off NaN
+data_1(isnan(data_1)) = 0;
+
+
+% E data
+session_type = 'E';
+dir_2 = ['D:\Lab\MI\BCICIV_2a_gdf\A0',num2str(subject_index),session_type,'.gdf'];
+% dir = 'D:\Lab\MI\BCICIV_2a_gdf\A01E.gdf';
+[s, HDR] = sload(dir_2);
+
+% Label 
+% label = HDR.Classlabel;
+labeldir_2 = ['D:\Lab\MI\true_labels\A0',num2str(subject_index),session_type,'.mat'];
+load(labeldir_2);
+label_2 = classlabel;
+
+% construct sample - data Section 1000*22*288
+Pos = HDR.EVENT.POS;
+% Dur = HDR.EVENT.DUR;
+Typ = HDR.EVENT.TYP;
+
+k = 0;
+data_2 = zeros(1000,22,288);
+for j = 1:length(Typ)
+    if  Typ(j) == 768
+        k = k+1;
+        data_2(:,:,k) = s((Pos(j)+500):(Pos(j)+1499),1:22);
+    end
+end
+
+% wipe off NaN
+data_2(isnan(data_2)) = 0;
+
+%% preprocessing
+% option - band-pass filter
+fc = 250; % sampling rate
+Wl = 4; Wh = 40; % pass band
+Wn = [Wl*2 Wh*2]/fc;
+[b,a]=cheby2(6,60,Wn);
+
+% a better filter for 4-40 Hz band-pass
+% fc = 250;
+% Wl = 4; Wh = 40; 
+% Wn = [Wl*2 Wh*2]/fc;
+% [b,a]=cheby2(8,20,Wn);
+
+for j = 1:288
+    data_1(:,:,j) = filtfilt(b,a,data_1(:,:,j));
+    data_2(:,:,j) = filtfilt(b,a,data_2(:,:,j));
+end
+
+% option - a simple standardization
+%{
+eeg_mean = mean(data,3);
+eeg_std = std(data,1,3); 
+fb_data = (data-eeg_mean)./eeg_std;
+%}
+
+%% Save the data to a mat file 
+data = data_1;
+label = label_1;
+% label = t_label + 1;
+saveDir = ['D:\MI\standard_2a_data\A0',num2str(subject_index),'T.mat'];
+save(saveDir,'data','label');
+
+data = data_2;
+label = label_2;
+saveDir = ['D:\MI\standard_2a_data\A0',num2str(subject_index),'E.mat'];
+save(saveDir,'data','label');
+
+end
+
+
+