diff --git a/FFT.m b/FFT.m
new file mode 100644
index 0000000..05be1df
--- /dev/null
+++ b/FFT.m
@@ -0,0 +1,47 @@
+%%%%%%%%%%%%%%%%%%%%%
+% Script task: Normalize RGB data and plot FFT using the power spectra
+%
+% Input : RGB_data.csv -> average RGB values of each image
+%
+% Output : Fast Fourier Transform of X(t): a graph representing the Single-Sided Amplitude Spectrum of X(t)
+%
+% Author: Maryne DEY (maryne.dey@ecam.fr)
+% Date: 07/02/2023
+%%%%%%%%%%%%%%%%%%%%%
+
+clear all
+close all
+clc
+
+pkg load io %% to be able to extract from external format (excel)
+
+data = csvread('RGB_database/RGB_data.csv');
+standard_deviation = std(data);
+mean_value = mean(data);
+
+for i = 1:size(data,1)
+  normalized_data_G(i,1) = (data(i,2)-mean_value(2))/standard_deviation(2); %%2 and not 1 because green is the 2nd color
+endfor
+
+Fs = 970/32;           % Sampling frequency = 970 images in 32 seconds                  
+T = 1/Fs;              % Sampling period       
+L = 970;               % Length of signal = 32 seconds
+t = (0:L-1)*T;         % Time vector
+
+X = normalized_data_G;
+
+plot(t(1:970),X(1:970))
+title("Signal")
+xlabel("t (milliseconds)")
+ylabel("X(t)")
+
+Y = fft(X);
+P2 = abs(Y/L);
+P1 = P2(1:L/2+1);
+P1(2:end-1) = 2*P1(2:end-1)
+f = Fs*(0:(L/2))/L
+
+plot(f(25:end),P1(25:end))
+title("Single-Sided Amplitude Spectrum of X(t)")
+xlabel("f (Hz)")
+ylabel("|P1(f)|")
\ No newline at end of file