Feature Extraction of Time-Amplitude-Frequency Analysis for Classifying Single EEG (Journal of Fiber Bioengineering and Informatics, 2014)
Feature extraction and feature classification are two important stages in most EEG based Brain-computerInterfaces (BCI). The features extracted by DiscreteWavelet Transform (DWT) have a great relationshipwith sampling frequency. On the other hand, the features extracted by Amplitude-frequency Analysis(AFA) always ignore time information. In this paper, we proposed a feature extraction scheme basedon Time-Amplitude-Frequency analysis (TAF) for classifying left/right hand imagery movement tasks.The time and frequency information are included in the proposed features. The Graz datasets used inBCI Competition 2003 and the datasets collected in the lab of Electromagnetic Theory and ArtificialIntelligence of Chongqing University are used to show the effectiveness of the proposed features. Thesimulation showed that the proposed features improved the classifying accuracy and the MutualInformation (MI) for both datasets. The mutual information of TAF for Graz2003 dataset is 0.58which is better than that of AFA and DWT.
Keywords: Brain-computer Interfaces (BCI); Electroencephalogram (EEG); Motor Imagery (MI); Discrete Wavelet Transform (DWT); Amplitude-frequency Analysis (AFA)