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Designing a robot controller by using a simple brain-wave sensor and a machine learning technique

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Abstract

We designed a robot controller that can use unstable data, such as brain waves. The controller analyzes brain waves from a simple electroencephalograph. A user can concentrate to make the robot move faster, and relax to make it move slower. In order to judge the user’s state by his brain-wave data, we adopt a machine learning technique called support vector machine. We investigated improving the classification accuracy by increasing the number of data sets used to make the user concentration model. We increased the data sets from 30 to 180; consequently, the accuracy increased, with a maximum of about 80 % with 150 data sets. This indicates that our controller is able to accurately classify unstable data and can control a robot using brain waves from a simple electroencephalograph.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National Institute of Technology, Akita College, Akita, Akita, Japan

    Hironori Hiraishi

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  1. Hironori Hiraishi
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Correspondence to Hironori Hiraishi.

Additional information

This work was presented in part at the 20th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 21–23, 2015.

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Hiraishi, H. Designing a robot controller by using a simple brain-wave sensor and a machine learning technique. Artif Life Robotics 20, 217–221 (2015). https://doi.org/10.1007/s10015-015-0224-y

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  • DOI: https://doi.org/10.1007/s10015-015-0224-y

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