The data you provided only has 256 data points. It does not have much noise, and no significant baseline drift, so I used a lowpass FIR filter here.
This works:
[d,s,r] = xlsread('Athira Surendran data.xls');
Ts = 0.5; % Sampling Interval (s)
Fs = 1/Ts; % Sampling Frequency (Hz)
Fn = Fs/2; % Nyquist Frequency (Hz)
sig = d(1,:); % Signal
t = d(3,:); % Time
L = length(t);
figure(1)
plot(t, sig)
grid
xlabel('Time')
ylabel('Amplitude')
FTsig = fft(sig)/L;
Fv = linspace(0, 1, fix(L/2)+1)*Fn;
Iv = 1:length(Fv);
figure(2)
plot(Fv, abs(FTsig(Iv))*2)
grid
xlabel('Frequency (Hz)')
ylabel('Amplitude')
fcuts = [0.08 0.19]; % Frequency Vector
mags = [1 0]; % Magnitude (Defines Passbands & Stopbands)
devs = [0.01 0.05]; % Allowable Deviations
[n,Wn,beta,ftype] = kaiserord(fcuts,mags,devs,Fs); % Kaiser Window
n = n + rem(n,2);
hh = fir1(n,Wn,ftype,kaiser(n+1,beta),'scale'); % Desing FIR Lowpass Filter
figure(3)
freqz(hh, 1, 2^14, Fs) % Plot Filter Characteristic
filt_sig = filtfilt(hh, 1, sig); % Filter Signal
figure(4)
plot(t, sig, ':b', 'LineWidth',1.5)
hold on
plot(t, filt_sig, '-r')
hold off
grid
legend('Original', 'Low-Pass Filtered')
xlabel('Time')
ylabel('Amplitude')
