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Simultaneous EEG Analysis and Feature Extraction Selection Based on Unsupervised Learning

  • Badar Almarri
  • Chun-Hsi Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11309)

Abstract

Time-series EEG signals in a raw form are challenging to analyze, train, and compute. Several feature extraction methods, such as fast Fourier transform, wavelet transform, and time-frequency distributions, are commonly employed for this purpose. However, when applied to different datasets, the alignment between the method and machine learning algorithms varies significantly. Through an EEG experiment, we test a simultaneous analysis and unsupervised learning application that can effectively determine what feature extraction method will potentially lead to a higher prediction precision when the ground truth is provided by the participants at a later stage.

Keywords

EEG Feature extraction Clustering Real time analysis 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of ConnecticutStorrsUSA
  2. 2.King Faisal UniversityAl HofufSaudi Arabia

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