A KNN classifier to predict human emotions from EEG data
- Due to an EULA, dataset is not included
- The average accuracy results are 82.33% (valence) and 87.32% (arousal).
The preprocessed data is used for training the classifier. Steps involved in training the dataset:
- Extracting the dataset
- Finding features
- Reducing the dimension
- Training
- checking classifier efficiency
The DEAP dataset consists of two parts:
- The participant ratings, physiological recordings and face video of an experiment where 32 volunteers watched 40 music videos.
- EEG and physiological signals were recorded and each participant also rated the videos as above.
- For a single participant the data is subdivided into label array and eeg_data array
| data | dim | contents |
|---|---|---|
| eeg_data | 40 x 40 x 8064 | video/trial x channel x data |
| labels | 40 x 4 | video/trial x label (valence(1-9), arousal(1-9), dominance(1-9), liking(1-9) |
- EEG data of ~10 participants is extracted then converted into vectors which is used as training data.
- The train_std.csv contains standard deviation of data of all 32 electrodes from each participant.
- Additional csv files are respective labels for the train.csv data.
- I have created KNN(k=3) classifier from scratch using scipy.spatial.distance.canberra as distance metric.
- Now create feature vector from our operational data
- I have used valence-arousal model for classification.
- Based on that model, 5 class of emotions can be detected using my approach.
Install the dependencies and devDependencies and start running knn_predict.py.
$ cd Emotion-detection-from-EEG
$ python knn.predict.py- Perform same task with an SVM
- Training an DNN for increasing accuracy
Want to contribute? Great! You can contact me for any suggestion or feedback!
MIT