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Estimation of the Shoulder Joint Angle Using Brainwaves
A B S T R A C T
This paper presents the angle of the shoulder joint as basic research for developing a machine
interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage
value were used as the learning feature. Hebbian learning was used on a multilayer perceptron
network for pattern classification for the estimation of joint angles 0
o
, 90o
and 180o of the
shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3%
of motion using voltage values of the raw EEG signal with the neural network. Further, with
selected electrodes and power density spectrum features, accuracy rose to 93.3% with more
stable motion estimation.
KEYWORDS
Shoulder Joint Angle; EEG; Neural Network
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Detail Information
- Series Title
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- Call Number
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621.381
- Publisher
- : Universitas Andalas., 2021
- Collation
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- Language
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English
- ISBN/ISSN
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2777-0079
- Classification
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621.381
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- Media Type
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- Edition
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- Subject(s)
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- Statement of Responsibility
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