# Human-Emotion-Analysis-using-EEG-from-DEAP-dataset
Processed the DEAP dataset on basis of
1) PSD (power spectral density) and
2)DWT(discrete wavelet transform) features .
Classifies the EEG ratings based on Arousal and Valence(high /Low)
Find the DEAP dataset here: (you need to seek permission by sending a mail to the authorized personell to download it)
http://www.eecs.qmul.ac.uk/mmv/datasets/deap/
The matlab files are there to process the data from EEG. (EEG dataset for matlab version)
Keep the matlab files in the DEAP dataset folder directly , where the data is.
Run the process.m to get the Power Spectral Density text files
Each testFile generated contains the 4 features- alpha, beta ,delta and theta wave power spectral density ratio(normalized by total psd), the output for valence , arousal and combined(valence+arousal) respectively. 1->low, 2->high
You can read about brain waves here (overview):
https://mentalhealthdaily.com/2014/04/15/5-types-of-brain-waves-frequencies-gamma-beta-alpha-theta-delta/
You can see the chart for arousal and valence states and their meanings here:
https://www.researchgate.net/figure/Arousal-valence-based-emotional-states_fig3_307587787
You will not get a good accuracy using psd features and KNN/SVM as they are mainly just a measure of human presence of mind.
DWT analysis helps us to get the time based features apart from frequency based psd.
Run the dwt_feature_extraction.m to generate testfiles for dwt analysed wave.
It consists of 3 features : wavelet energy, wavelet entropy and standard deviation. along with the arousla and valce ratings.
Note that, here each wave has been divided into segments of 6 sec (thus 10 total segments)
the folders "psd analysis knn and svm" and "dwt analysis" already contain the processed textfiles and the python code to take in the training data from those testfiles and classify.
Run the ipynb files for classification with KNN and SVM.
References:
1.Kairui Guo, Henry Candra, Hairong Yu,Huiqi Li, Hung T. Nguyen and Steven W. Su,EEG-based Emotion Classification Using Innovative Features and Combined SVM and HMM Classifier. IEEE conference 2017
https://ieeexplore.ieee.org/document/8036868/
2.DEAP: A Database for Emotion Analysis using Physiological Signals -Sander Koelstra, Student Member, IEEE, Christian M¨uhl, Mohammad Soleymani, Student Member, IEEE,
Jong-Seok Lee, Member, IEEE, Ashkan Yazdani, Touradj Ebrahimi, Member, IEEE,
Thierry Pun, Member, IEEE, Anton Nijholt, Member, IEEE, Ioannis Patras, Member, IEEE
https://ieeexplore.ieee.org/document/5871728/
3.https://www.sciencedirect.com/science/article/pii/S0165027000003563
（matlab）Human-Emotion-Analysis-EEG-DEAP.zip （79个子文件） 
（matlab）Human-Emotion-Analysis-EEG-DEAP 
beta_band_filter.m 764B delta_band_filter.m 556B dwt analysis 
testFile_17.txt 416KB testFile_13.txt 417KB testFile_03.txt 416KB testFile_20.txt 416KB testFile_15.txt 417KB testFile_04.txt 416KB testFile_23.txt 417KB testFile_31.txt 417KB testFile_29.txt 417KB testFile_01.txt 416KB testFile_30.txt 417KB testFile_16.txt 417KB testFile_26.txt 416KB svm_dwt_analysis_of_human_emotion.ipynb 37KB testFile_28.txt 417KB testFile_09.txt 416KB testFile_07.txt 416KB testFile_06.txt 417KB testFile_19.txt 417KB testFile_21.txt 416KB testFile_22.txt 416KB testFile_18.txt 417KB knn_dwt_emotion_recognition.ipynb 2KB testFile_14.txt 417KB testFile_05.txt 416KB testFile_10.txt 416KB testFile_24.txt 417KB testFile_27.txt 417KB testFile_08.txt 416KB testFile_32.txt 417KB testFile_02.txt 416KB testFile_12.txt 417KB testFile_11.txt 417KB testFile_25.txt 416KB lowpass_filter.m 527B dwt_feature_extraxtion.m 3KB theta_band_filter.m 766B forGraphGeneration.m 3KB psd analysis knn and svm testFile_17.txt 46KB testFile_13.txt 46KB testFile_03.txt 46KB knn.ipynb 2KB testFile_20.txt 46KB testFile_15.txt 46KB testFile_04.txt 46KB testFile_23.txt 46KB testFile_31.txt 46KB testFile_29.txt 46KB testFile_01.txt 46KB testFile_30.txt 46KB testFile_16.txt 46KB testFile_26.txt 46KB testFile_28.txt 46KB testFile_09.txt 46KB testFile_07.txt 46KB testFile_06.txt 46KB testFile_19.txt 46KB testFile_21.txt 46KB testFile_22.txt 46KB testFile_18.txt 46KB testFile_14.txt 46KB testFile_05.txt 46KB testFile_10.txt 46KB testFile_24.txt 46KB testFile_27.txt 46KB testFile_08.txt 46KB testFile_32.txt 46KB testFile_02.txt 46KB testFile_12.txt 46KB testFile_11.txt 46KB testFile_25.txt 46KB alpha_band_filter.m 741B process_dwt_final.m 3KB README.md 3KB dwt_graphs.m 2KB readme.txt 341B process.m 4KB
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