Fractal based feature extraction technique for classifying EEG signal for color visualization tasks

Abstract

Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy (SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features.