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+# EEG-Emotion-classification
+
+# PROBLEM S TATEMENT
+It is difficult to look at the EEG signal and identify the state of Human mind. In this assign-
+ment, the SVM classifier is trained with Deap dataset to predict the state of mind. the state of
+mind is predicted in terms of valence, arousal. which can further be used to predict the state
+of mind in terms of expression.
+# PROCEDURE TO SOLVE THE ABOVE PROBLEM
+In this assignment, the preprocessed data is used for training the classifier.
+Steps involve in training the dataset:-
+1. Extracting the dataset
+2. Finding the features
+3. Reducing the dimension
+4. traning the vector
+5. checking the classifier efficiency
+## EXTRACTING THE DATASET
+The DEAP dataset consists of two parts:
+1. The ratings from an online self-assessment where 120 one-minute extracts of music
+videos were each rated by 14-16 volunteers based on arousal, valence and dominance.
+2. The participant ratings, physiological recordings and face video of an experiment where
+32 volunteers watched a subset of 40 of the above music videos. EEG and physiological
+signals were recorded and each participant also rated the videos as above.
+In this assignment, labels are extracted into separate file and data of each channel is extracted
+into separate file. data from each channel is stored in row wise versus time in column for each
+trail,per person
+## FINDING THE FEATURES
+In this assignment, Wavelet transform is used to decompose the each channel data into the
+five feature i.e
+• Delta (< 4 Hz)
+• Theta (4-7 Hz)
+• Alpha (8-15 Hz)
+• Beta (16-31 Hz)
+• Gamma (> 32 Hz)
+In this assignment, obtained the 7 decomposed values but we negalted the frequency whose
+range is in 0-0.5 Hz so that the artifcats are removed. The frequency whose range is near 50
+Hz are removed to reduce the effect of power line on signals. finally, EEG band are obtained
+for each channel.
+## REDUCING THE DIMENSION
+The dimension can be reduced using one of the below mention method:-
+1. Standard Deviation
+2. Mean
+3. Variance
+4. Median
+But in this assignment Standard Deviation is used because it describe the devaition of each
+EEG Band power density properly given by the equation below.
+## TRANING THE VECTOR
+In this assignment, the classifier used is Support vector machine (SVM). we can also use other
+classifier or neural network to predict the values but the training efficiency is found to be
+nearly 98 percentage with SVM.