Identification and Discrimination of the Limb Motions using Brain Waves from Motor Imagery

DOI

10.2991/caai-17.2017.20How to use a DOI?

Keywords

Electro-encephalogram (EEG); Brain Computer Interface (BCI); motor imagery; limbs motion; Fast Fourier Transform (FFT); Neural Network (NN); Support Vector Machine (SVM)

Abstract

This research identified and distinguished limb motions by the use of Neural Networks (NN), Support Vector Machines (SVM) and the Electro-encephalogram (EEG). EEG enabled motor imagery of the limbs. By comparing the EEG power at rest with that of the limb's motor imagery, measurements from the electrodes and EEG frequency bands were examined. In this study, EEG with a limited frequency band (from 25Hz to 30Hz) obtained from the electrodes C3, C4, P3 and P4 was used. The features were extracted by the use of Fast Fourier Transform (FFT). The results showed that three out of four subjects were able to identify and distinguish their limb motions at a success rate of over 70%. Furthermore, the rates of identification and discrimination of the limb motions were slightly higher for the Support Vector Machines than for the Neural Networks.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Minoru Sasaki
AU  - Ryo Nakamura
AU  - Waweru Njeri
AU  - Kojiro Matsushita
AU  - Satoshi Ito
PY  - 2017/06
DA  - 2017/06
TI  - Identification and Discrimination of the Limb Motions using Brain Waves from Motor Imagery
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 96
EP  - 101
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.20
DO  - 10.2991/caai-17.2017.20
ID  - Sasaki2017/06
ER  -