TorchEEG provides plugins related to graph algorithms for converting EEGin datasets into graph structures and analyzing them using graph neuralnetworks. In this tutorial, you learned how to train your first Convolutional Neural Network (CNN) using the PyTorch deep learning library. Each image in the KMNIST dataset is a single channel grayscale image; however, we want to use OpenCV’s cv2.putText function to draw the predicted class label and ground-truth label on the image. Get used to seeing both methods as some deep learning practitioners (almost arbitrarily) prefer one over the other. Lines set our initial learning rate, batch size, and number of epochs to train for, while Lines 34 and 35 define our training and validation split size (75% of training, 25% for validation).
EEG data from the Persyst system can be read withmne.io.read_raw_persyst(). EEG data from the Nexstim eXimia system can be read withmne.io.read_raw_eximia(). GDF (General Data Format) is a flexibleformat for biomedical signals that overcomes some of the limitations of theEDF format. The original specification (GDF v1) includes a binary headerand uses an event table. An updated specification (GDF v2) was released in2011 and adds fields for additional subject-specific information (gender,age, etc.) and allows storing several physical units and other properties.Both specifications are supported by MNE. Since 3-byte raw data buffers are not presentlysupported in the FIF format, these data will be changed to 4-byteintegers in the conversion.
Both methods are preceded by extensive pre-processing, with frequency filtering, an approach to extract features, as the most important step. CSP and RG are explained in more detail here, and frequency filtering is explained in more detail in this post. Due to the difficulty of our dataset, all network architectures we tried had a strong tendency to begin overfitting.
Inthis tutorial, we use k-fold cross-validation on the entire dataset(KFold) as an example of dataset splitting. The torcheeg.datasets module contains dataset classes for manyreal-world EEG datasets. In this tutorial, we use the DEAP dataset.We first go to the official website to apply for data downloadpermission according to the introduction of DEAPdataset, anddownload the dataset. Next, we need to specify the download location ofthe dataset in the root_path parameter. For the DEAP dataset, wespecify the path to the data_preprocessed_python folder,e.g. This sequence of saving a model after training, and then loading it and using the model to make predictions, is a process you should become comfortable with — you’ll be doing it often as a PyTorch deep learning practitioner.
If you are looking to train a Convolutional Neural Network (CNN) using Electroencephalography (EEG) data in Python, one of the first steps is to load the EEG data into your program.
I strongly believe that if you had the right teacher you could master computer vision and deep learning. We then load the testing data from the KMNIST dataset on Lines 26 and 27. We randomly sample a total of 10 images from this dataset on Lines 28 and 29 using the Subset class (which creates a smaller “view” of the full testing data).
This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning algorithms such as Support vector machine and K – Nearest Neighbor. As a refresher, the full connection step within a convolutional neural network is simply a standalone layer of an artificial neural network where every neuron is connected to each neuron in the previous layer. It is very simple to add another convolutional layer and max pooling layer to our convolutional neural network. As you might image, we need to specify the characteristics of our convolutional layer for the first layer of this neural network. TensorFlow contains a built-in object designed specifically for building convolutional layer. Overfitting is a common problem in machine learning and deep learning and is characterized by having very high accuracy on the training data and much lower accuracy on the test data.
The training_set variable that we created earlier in this tutorial contains an attribute called class_indices that is a dictionary with keys and values showing which number corresponds to each animal. The output of our specific full connection step will be a binary cat/dog classification determined by a Sigmoid function. As mentioned, we will not be applying image augmentation techniques to our test data. We want the test data to remain unchanged (which would be the case if our machine learning model was deployed in production). The reason for this is that to avoid overfitting, we will add an extra transformation to the training data images.
Step 1: Preprocessing the EEG data
EEG data is often stored in various formats such as .edf or .mat files. To load this data into Python, you can use libraries like MNE-Python or NeuroKit which provide functions to read and process EEG data efficiently.
Step 2: Formatting the data for CNN training
Once you have loaded the EEG data, it is essential to format it in the correct shape for training a CNN. Typically, EEG data is multidimensional and requires reshaping into a suitable input shape for the neural network.
Step 3: Building and training the CNN model
After preprocessing the EEG data, you can start building your CNN model using deep learning libraries such as TensorFlow or PyTorch. Define the architecture of the CNN, including the number of layers, filters, and activation functions.
Step 4: Training and evaluating the CNN model
Once the model is built, you can train it using the preprocessed EEG data. Monitor the training process by evaluating metrics such as accuracy and loss on a separate validation dataset to prevent overfitting.
Conclusion
By following these steps, you can successfully load EEG data in Python and train a CNN model for classification or regression tasks. Experiment with different architectures and hyperparameters to optimize the performance of your neural network.
