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Prosthetic Motor Imaginary Task Classification Based on EEG Quality Assessment Features

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9492))

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Abstract

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant’s imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4 % of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it’s high speed and accuracy.

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Authors and Affiliations

  1. Centre for Intelligent Systems Research, Deakin University, Victoria, Australia

    Sherif Haggag, Shady Mohamed, Omar Haggag & Saeid Nahavandi

Authors
  1. Sherif Haggag
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  2. Shady Mohamed
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  3. Omar Haggag
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  4. Saeid Nahavandi
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Corresponding author

Correspondence to Sherif Haggag .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Haggag, S., Mohamed, S., Haggag, O., Nahavandi, S. (2015). Prosthetic Motor Imaginary Task Classification Based on EEG Quality Assessment Features. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-26561-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26560-5

  • Online ISBN: 978-3-319-26561-2

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