Volume 7, Issue 2
Feature Extraction of Time-Amplitude-Frequency Analysis for Classifying Single EEG

Yonghui Fang & Xufei Zheng

Journal of Fiber Bioengineering & Informatics, 7 (2014), pp. 261-271.

Published online: 2014-07

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  • Abstract

Feature extraction and feature classification are two important stages in most EEG based Brain-computer Interfaces (BCI). The features extracted by DiscreteWavelet Transform (DWT) have a great relationship with 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 based on 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 in BCI Competition 2003 and the datasets collected in the lab of Electromagnetic Theory and Artificial Intelligence of Chongqing University are used to show the effectiveness of the proposed features. The simulation showed that the proposed features improved the classifying accuracy and the Mutual Information (MI) for both datasets. The mutual information of TAF for Graz2003 dataset is 0.58 which 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)

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COPYRIGHT: © Global Science Press

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@Article{JFBI-7-261, author = {}, title = {Feature Extraction of Time-Amplitude-Frequency Analysis for Classifying Single EEG}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2014}, volume = {7}, number = {2}, pages = {261--271}, abstract = {Feature extraction and feature classification are two important stages in most EEG based Brain-computer Interfaces (BCI). The features extracted by DiscreteWavelet Transform (DWT) have a great relationship with 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 based on 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 in BCI Competition 2003 and the datasets collected in the lab of Electromagnetic Theory and Artificial Intelligence of Chongqing University are used to show the effectiveness of the proposed features. The simulation showed that the proposed features improved the classifying accuracy and the Mutual Information (MI) for both datasets. The mutual information of TAF for Graz2003 dataset is 0.58 which is better than that of AFA and DWT.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi06201412}, url = {http://global-sci.org/intro/article_detail/jfbi/4783.html} }
TY - JOUR T1 - Feature Extraction of Time-Amplitude-Frequency Analysis for Classifying Single EEG JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 261 EP - 271 PY - 2014 DA - 2014/07 SN - 7 DO - http://doi.org/10.3993/jfbi06201412 UR - https://global-sci.org/intro/article_detail/jfbi/4783.html KW - Brain-computer Interfaces (BCI) KW - Electroencephalogram (EEG) KW - Motor Imagery (MI) KW - Discrete Wavelet Transform (DWT) KW - Amplitude-frequency Analysis (AFA) AB - Feature extraction and feature classification are two important stages in most EEG based Brain-computer Interfaces (BCI). The features extracted by DiscreteWavelet Transform (DWT) have a great relationship with 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 based on 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 in BCI Competition 2003 and the datasets collected in the lab of Electromagnetic Theory and Artificial Intelligence of Chongqing University are used to show the effectiveness of the proposed features. The simulation showed that the proposed features improved the classifying accuracy and the Mutual Information (MI) for both datasets. The mutual information of TAF for Graz2003 dataset is 0.58 which is better than that of AFA and DWT.
Yonghui Fang & Xufei Zheng. (2019). Feature Extraction of Time-Amplitude-Frequency Analysis for Classifying Single EEG. Journal of Fiber Bioengineering and Informatics. 7 (2). 261-271. doi:10.3993/jfbi06201412
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