Feature extraction from spectrum to aid categorisation

1. What is feature extraction and classification?
2. What are common methods of feature extraction?
3. What features can be extracted from EEG?
4. Why feature extraction is important for pattern recognition?

What is feature extraction and classification?

Feature Extraction uses an object-based approach to classify imagery, where an object (also called segment) is a group of pixels with similar spectral, spatial, and/or texture attributes. Traditional classification methods are pixel-based, meaning that spectral information in each pixel is used to classify imagery.

What are common methods of feature extraction?

Autoencoders, wavelet scattering, and deep neural networks are commonly used to extract features and reduce dimensionality of the data.

What features can be extracted from EEG?

The simplest features of the EEG signal are statistical features, like mean, median, variance, standard deviation, skewness, kurtosis, and similar [50].

Why feature extraction is important for pattern recognition?

Feature extraction is the process of determining the features to be used for learning. The description and properties of the patterns are known. However, for the classification task at hand, it is necessary to extract the features to be used.