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PreProcess.m

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+function [AllData,y_AllData]=PreProcess(channels,sample_length,sample_interval,subban_no,totalsubject,totalblock,totalcharacter,sampling_rate,dataset)
+% Preprocessing 
+% This code is directly taken from the original repository (https://github.com/osmanberke/Deep-SSVEP-BCI) of
+% "A Deep Neural Network for SSVEP-based Brain Computer Interfaces" 
+
+
+%% Initialization
+total_channels=length(channels); % Determine total number of channel
+AllData=zeros(total_channels,sample_length,subban_no,totalcharacter,totalblock,totalsubject); %initializing
+y_AllData=zeros(1,totalcharacter,totalblock,totalsubject); %initializing
+
+%% Forming bandpass filters
+%High cut off frequencies for the bandpass filters (90 Hz for all)
+high_cutoff = ones(1,subban_no)*90;
+%Low cut off frequencies for the bandpass filters (ith bandpass filter low cutoff frequency 8*i)
+low_cutoff =8:8:8*subban_no;
+filter_order=2; % Filter Order of bandpass filters
+PassBandRipple_val=1; 
+bpFilters=cell(subban_no,1); % Form and store bandpass filters
+for i=1:subban_no
+    bpFilt1 = designfilt('bandpassiir','FilterOrder',filter_order, ...
+        'PassBandFrequency1',low_cutoff(i),'PassBandFrequency2',high_cutoff(i),...
+		'PassBandRipple',PassBandRipple_val,...
+		'DesignMethod','cheby1','SampleRate',sampling_rate);        
+    bpFilters{i}=bpFilt1;
+end
+%% Filtering
+for subject=1:totalsubject
+    nameofdata=['s',num2str(subject),'.mat'];
+    data=load(nameofdata); % Loading the subject data
+    data=data.data;
+    if strcmp(dataset,'BETA')	
+		data=data.EEG;	
+	end
+	% Taking data from spesified channels, and signal interval
+    sub_data= data(channels,sample_interval,:,:); 
+
+    for chr=1:1:totalcharacter        
+        for blk=1:totalblock            
+            if strcmp(dataset,'Bench')
+                tmp_raw=sub_data(:,:,chr,blk);
+            elseif strcmp(dataset,'BETA')
+                tmp_raw=sub_data(:,:,blk,chr);
+                %else
+            end         
+            for i=1:subban_no
+                processed_signal=zeros(total_channels,sample_length); % Initialization
+                for j=1:total_channels            
+                    processed_signal(j,:)=filtfilt(bpFilters{i},tmp_raw(j,:));
+
+                end                 
+                AllData(:,:,i,chr,blk,subject)=processed_signal;
+				y_AllData(1,chr,blk,subject)=chr;
+            end
+        end
+    end    
+end
+end