Emotion recognition is a complex as it is data heavy and requires learning of all relevant details from a large pool of noisy inputs. A single layer as well as a double hidden layer was implemented in this project.
- Due to an EULA, dataset is not included
- The accuracy for single layer was found to be ~75% and for double layer is ~83%
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) |
This describes the major steps performed in both of the approaches-
- Extracting the data in such a manner thus reducing the eeg_data dimension to (320,32).
- Various preprocessing techniques like calculation standard deviation and mean for reducing feature space.
- For labels, a seperate preprocessing technique is applied which reduces its dim to (32,50).
- Then it passes through both neural network architecture and final output is used in cost calculation.
- The final output is flatened for obtaining single cost value.
- Both then outputs the most probable class of emotion.
- EEG data of 10 participants is extracted then converted into feature vectors which is used as training data.
- Forward and backward propogation are created from sratch using tanh and sigmoid as activation functions
- Number of iteration were 50,000 with learning rate of 0.3
- 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 DL-based-Emotion-recognition-from-EEG
$ python nn_eeg_1l.py
$ python nn_eeg_2l.py- Add more hidden layers
- Applying similar approach in performing neural reconstruction
Want to contribute? Great! You can contact me for any suggestion or feedback!
MIT