CNN for EEG 2-class pattern classification
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I am new to using the deep learning for classifcation so i have some basic questions, i will highly appreciate if anyone can help through.
I have EEG data collected from 16 channels,at 1200 sampling frequency of two classes. After pre-processing i have extracted the epochs of two classes (for N=100 for each class) for 1second which are in this format: 1200x16x100.I need to train the CNN to classify the class 1 and 2 with 70% training data and 30% for testing.
1: How to prepare the data for training and testing/target.?
2: How to assign the labels to each class for training/testing in CNN.?
Mahesh Taparia 2019년 12월 9일
You have an EEG dataset of two classes of dimensions 1200X16X100. Initially, put the dataset of both the classes into two separate folders with their folder name as their labels. Convert this dataset into datastore using ‘datastore’ function. You can split the training and testing dataset using ‘splitEachLabel’ function.
You can refer to this link for image classification. In your case instead of images as input, some matrix is there which can be loaded using datastore function.
To create a custom model, you can refer to this documentation.