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Investigation of New Statistical Features for BCI Based Sleep Stages Recognition through EEG Bio-signals

  • Conference paper
Intelligent Computing in Bioinformatics (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8590))

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Abstract

Electroencephalogram (EEG) is one of the oldest techniques available to read brain data. It is a methodology to measure and record the electrical activity of brain using sensitive sensors attached to the scalp. Brain’s electrical activity is visualized on computers in form of signals through BCI tools. It is also possible to convert these signals into digital commands to provide human-computer interaction (HCI) through adaptive user interfaces. In this study, a set of statistical features: mean entropy, skew-ness, kurtosis and mean power of wavelets are proposed to enhance human sleep stages recognition through EEG signal. Additionally, an adaptive user interface for vigilance level recognition is introduced. One-way ANOVA test is employed for feature selection. EEG signals are decomposed into frequency sub-bands using discrete wavelet transform and selected statistical features are employed in SVM for recognition of human sleep stages: stage 1, stage 3, stage REM and stage AWAKE. According to experimental results, proposed statistical features have a significant discrimination rate for true classification of sleep stages with linear SVM. The accuracy of linear SVM reaches to 93% in stage 1, 82% in stage 3, 73% in stage REM and 96% in stage AWAKE with proposed statistical features.

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Author information

Authors and Affiliations

  1. Energy Institute, TÜBİTAK MAM, Baris Mah. Dr. Zeki Acar Cad. No:1 P.K. 21, 41470, Gebze, Kocaeli, Turkey

    Ibrahim Sefik

  2. National Research Institute of Electronics and Cryptology, TÜBİTAK BİLGEM, Baris Mah. Dr. Zeki Acar Cad. No:1 P.K. 21, 41470, Gebze, Kocaeli, Turkey

    Furkan Elibol

  3. Hanwul Multimedia Communication Co. Ltd., 1012-1015, Ace High Tech21, U-dong, Haeundae-gu, 1470, Busan, Republic of Korea

    Ibrahim Furkan Ince

  4. Institute of High Performance Computing, A*STAR, 1 Fusionopolis Way ,#16-16 Connexis North, 138632, Singapore

    Ilker Yengin

Authors
  1. Ibrahim Sefik
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  2. Furkan Elibol
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  3. Ibrahim Furkan Ince
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  4. Ilker Yengin
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer Science and Engineering Inha University, Incheon, South Korea

    Kyungsook Han

  3. Department of Biotechnology, Indian Institute of Technology Madras, 600 036, Chennai, Tamilnadu, India

    Michael Gromiha

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Sefik, I., Elibol, F., Ince, I.F., Yengin, I. (2014). Investigation of New Statistical Features for BCI Based Sleep Stages Recognition through EEG Bio-signals. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-09330-7_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09329-1

  • Online ISBN: 978-3-319-09330-7

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