Why does eeg data have a lots of rows while the class denoting rows is only less number

i have graz data set 2b and i am working on eeg signal classification. i have a array of 120 rows and 1 column denoting it as 1 or 2 based on which class is this trial and i have a dataset of 6 columns( corresponding to 3 bipolar electrodes and 3 eog electrodes ) and 604803 rows . I am confused how i should relate this large number of rows with that of 120 rows to train the classifier.

Answer 1

[Yash]

Hi Fathima.

To relate the large number of rows in your dataset (604803) with the 120 rows for training the classifier, you can use feature extraction techniques to reduce the dimensionality of your data. Here's a general approach you can follow:

1. Split your dataset into training and testing sets. Since you have only 120 rows for training, you may consider using a larger portion for testing to ensure a reliable evaluation of your classifier.
2. Apply feature extraction techniques to reduce the dimensionality of your dataset. Some common techniques for EEG signal classification include time-domain features (e.g., mean, variance, skewness), frequency-domain features (e.g., power spectral density, spectral entropy), and time-frequency features (e.g., wavelet transform, spectrogram).
3. Extract the features from your training dataset and create a feature matrix with 120 rows (corresponding to the 120 trials) and a reduced number of columns (based on the extracted features).
4. Train your classifier using the feature matrix and the corresponding class labels (1 or 2). Extract the features from your testing dataset and create a feature matrix with the remaining rows (604803 - 120) and the same number of columns as the training feature matrix.
5. Use the trained classifier to predict the class labels for the testing feature matrix. Evaluate the performance of your classifier using appropriate metrics such as accuracy, precision, recall, or F1 score.

You can refer to the following documentation to know more about feature extraction in MATLAB: https://in.mathworks.com/discovery/feature-extraction.html?s_tid=srchtitle_support_results_1_feature%20extraction

Hope this helps!