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demo2a_ssvep_decomposition_reconstruction.m
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demo2a_ssvep_decomposition_reconstruction.m
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%% Demo 2a of Decomposition and Reconstruction of SSVEP %%%
% In this code, we provide an example about how to decompose and
% reconstruct an SSVEP based on the superposition model as mentioned in some previous studies, such as
% A. Capilla, P. Pazo-Alvarez, A. Darriba, P. Campo, and J. Gross,
% ''Steady-state visual evoked potentials can be explained by temporal
% superposition of transient event-related responses,'' PLoS ONE, vol. 6,
% no. 1, Jan. 2011, Art. no. e14543.
% J. Thielen, P. van den Broek, J. Farquhar, and P. Desain, ''Broad-
% Band visually evoked potentials: Re (Con) volution in brain-computer
% interfacing,'' PLoS ONE, vol. 10, no. 7, 2015, Art. no. e0133797.
% S. Nagel and M. Sp?ler, ''Modelling the brain response to arbitrary visual
% stimulation patterns for a flexible high-speed brain-computer interface,''
% PLoS ONE, vol. 13, no. 10, Oct. 2018, Art. no. e0206107.
% In this demo, we decompose an SSVEP corresponding to the i-th stimulus and then reconstruct a new SSVEP corresponding to the (i+1)-th stimulus. We measure
% the difference between the real SSVEP and the reconstructed SSVEP (both corresponding to the (i+1)-th stimulus) by using the correlation and MSE
%
% Demo 1a is similar to Demo 1. The difference is that here we use the
% canonical correlation analysis (rather than ALS) to find the spatial
% filter and impulse response when we decompose the SSVEP.
% This code is prepared by Chi Man Wong (chiman465@gmail.com)
% Date: 23 March 2021
% if you use this code for a publication, please cite the following paper:
% @article{wong2021transferring,
% title={Transferring Subject-Specific Knowledge Across Stimulus Frequencies in SSVEP-Based BCIs},
% author={Wong, Chi Man and Wang, Ze and Rosa, Agostinho C and Chen, CL Philip and Jung, Tzyy-Ping and Hu, Yong and Wan, Feng},
% journal={IEEE Transactions on Automation Science and Engineering},
% year={2021},
% publisher={IEEE}
% }
clear all;
close all;
% Please download the SSVEP benchmark dataset for this code
% Wang, Y., et al. (2016). A benchmark dataset for SSVEP-based brain¡Vcomputer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1746-1752.
% Then indicate where the directory of the dataset is :
% str_dir=cd; % Directory of the SSVEP Dataset (Change it if necessary)
str_dir='..\Tsinghua dataset 2016\';
addpath('..\mytoolbox\');
num_of_subj=35; % Number of subjects (35 if you have the benchmark dataset)
Fs=250; % sample rate
ch_used=[48 54 55 56 57 58 61 62 63]; % Pz, PO5, PO3, POz, PO4, PO6, O1,Oz, O2 (in SSVEP benchmark dataset)
oz_ch=8;
num_of_trials=5; % Number of training trials (1<=num_of_trials<=2)
num_of_harmonics=5; % for all cca-based methods
num_of_subbands=1; % for filter bank analysis
latencyDelay = round(0.12*Fs); % for excluding latency response
tw=2; % time window length
sig_len=tw*Fs; % data length
%bandpass filter
for k=1:num_of_subbands
Wp = [(8*k)/(Fs/2) 90/(Fs/2)];
Ws = [(8*k-2)/(Fs/2) 100/(Fs/2)];
[N,Wn] = cheb1ord(Wp,Ws,3,40);
[subband_signal(k).bpB,subband_signal(k).bpA] = cheby1(N,0.5,Wn);
end
%notch
Fo = 50;
Q = 35;
BW = (Fo/(Fs/2))/Q;
[notchB,notchA] = iircomb(Fs/Fo,BW,'notch');
seed = RandStream('mt19937ar','Seed','shuffle');
for sn=1:num_of_subj
tic
load(strcat(str_dir,'\s',num2str(sn),'.mat'));
% pre-stimulus period: 0.5 sec
% latency period: 0.14 sec
eeg=data(ch_used,floor(0.5*Fs)+1:floor(0.5*Fs+latencyDelay)+sig_len,:,:);
[d1_,d2_,d3_,d4_]=size(eeg);
d1=d3_;d2=d4_;d3=d1_;d4=d2_;
no_of_class=d1;
% d1: num of stimuli
% d2: num of trials
% d3: num of channels % Pz, PO5, PO3, POz, PO4, PO6, O1, Oz, O2
% d4: num of sampling points
for i=1:1:d1
for j=1:1:d2
y0=reshape(eeg(:,:,i,j),d3,d4);
y = filtfilt(notchB, notchA, y0.'); %notch
y = y.';
for sub_band=1:num_of_subbands
for ch_no=1:d3
tmp2=filtfilt(subband_signal(sub_band).bpB,subband_signal(sub_band).bpA,y(ch_no,:));
y_sb(ch_no,:) = tmp2(latencyDelay+1:latencyDelay+2*Fs);
end
subband_signal(sub_band).SSVEPdata(:,:,j,i)=reshape(y_sb,d3,length(y_sb),1,1);
end
end
end
clear eeg
pha_val=[0 0.5 1 1.5 0 0.5 1 1.5 0 0.5 1 1.5 0 0.5 1 1.5 0 0.5 1 1.5 ...
0 0.5 1 1.5 0 0.5 1 1.5 0 0.5 1 1.5 0 0.5 1 1.5 0 0.5 1 1.5]*pi;
sti_f=[8:0.2:15.8];
n_sti=length(sti_f); % number of stimulus frequencies
temp=reshape([1:40],8,5);
temp=temp';
target_order=temp(:)';
for k=1:num_of_subbands
subband_signal(k).SSVEPdata=subband_signal(k).SSVEPdata(:,:,:,target_order); % To sort the orders of the data as 8.0, 8.2, 8.4, ..., 15.8 Hz
for i=1:no_of_class
subband_signal(k).signal_template(i,:,:)=mean(subband_signal(k).SSVEPdata(:,:,:,i),3); % Average the SSVEP across trials
end
end
for k=1:num_of_subbands
sub_fig=1;
for i=1:2:no_of_class
% Decompose an SSVEP corresponding to the i-th stimulus
fs=sti_f(i);
ph=pha_val(i);
ssvep=squeeze(subband_signal(k).signal_template(i,:,:)); % this is the SSVEP we want to decompose
y0=ssvep;
y0_oz=ssvep(oz_ch,:);
freq_period=1.05*(1./sti_f(i)); % the length of impulse response
t=[0:sig_len-1]/Fs;
[H0,h0]=my_conv_H(fs,ph,Fs,tw,60,freq_period); % Create the matrix for convolution calculation
x_hat_oz=y0_oz*H0'*inv(H0*H0'); % impulse response (Oz)
[A,B,r]=canoncorr(y0',H0');
x_hat=B(:,1)';
w0_new=A(:,1)';
% Alternating Least Square (Find the impulse response and the
% spatial filter simultaneously
% w0_old=randn(1,length(ch_used));
% x_hat_old=w0_old*y0*H0'*inv(H0*H0');
% e_old=norm(w0_old*y0-x_hat_old*H0);
% my_err=100;
% iter=1;
% while (my_err(iter)>0.0001 && iter<200)
% w0_new=x_hat_old*H0*y0'*inv(y0*y0');
% x_hat_new=w0_new*y0*H0'*inv(H0*H0');
% e_new=norm(w0_new*y0-x_hat_new*H0);
%
% iter=iter+1;
% my_err(iter)=abs(e_old-e_new);
% w0_old=w0_new;
% w0_old=w0_old/std(w0_old);
% x_hat_old=x_hat_new;
% x_hat_old=x_hat_old/std(x_hat_old);
% e_old=e_new;
% end
% x_hat=x_hat_new;
cr=corrcoef(x_hat,x_hat_oz);
if cr(1,2)<0
x_hat=-x_hat;
w0_new=-w0_new;
end
% Reconstruct a new SSVEP corresponding to the (i+1)-th
% stimulus
fs1=sti_f(i+1);
ph1=pha_val(i+1);
ssvep1=squeeze(subband_signal(k).signal_template(i+1,:,:)); % this is the SSVEP we want to reconstruct
y1=ssvep1;
y1_oz=ssvep1(oz_ch,:);
freq_period1=1.05*(1./sti_f(i)); % the length of impulse response
[H1,h1]=my_conv_H(fs1,ph1,Fs,tw,60,freq_period1); % Create the matrix for convolution calculation
y1_hat=x_hat*H1;
y1_hat(:,1:length(find(y1_hat==0)))=0.8*y1_hat(:,Fs+1:Fs+length(find(y1_hat==0)));
y1_sf=w0_new*ssvep1;
y1_sf=y1_sf-mean(y1_sf);
y1_sf=y1_sf/std(y1_sf);
y1_hat=y1_hat-mean(y1_hat);
y1_hat=y1_hat/std(y1_hat);
if sn==1
figure(1);
subplot(4,5,sub_fig);
plot(x_hat,'r');
hold on;
plot(x_hat_oz,'b');
hold off;
xlim([0 length(x_hat)]);
title([num2str(sti_f(i),'%.1f') '(Hz)']);
figure(2);
subplot(4,5,sub_fig);
plot(y1_sf(1:floor(Fs*0.5)),'r');
hold on;
plot(y1_hat(1:floor(Fs*0.5)),'b');
hold off;
xlim([0 length(y1_hat(1:floor(Fs*0.5)))]);
title([num2str(sti_f(i+1),'%.1f') '(Hz)']);
end
ymse(sn,sub_fig)=norm(y1_sf-y1_hat)/length(y1_sf);
r=corrcoef(y1_sf,y1_hat);
ycor(sn,sub_fig)=r(1,2);
sub_fig=sub_fig+1;
end
end
toc
disp(sn)
end