Abstract
Neuronal activity, the fundamental source for bio-electric signals expresses the variability of brainwaves in humans. Brainwave and specific EEG spectral analysis are important in bio-electric signal variability identification. Recent researches in neuro-robotics rely on the use of brain computer interface (BCI) technology in developing robotic commands. Brainwave variability identification provides different levels of robot control signal development and optimization.
This paper presents the development of robotic arm control strategy using brainwave signal variability. The bio-electric signal identification was derived from physiological expressions. The physiological expressions are identified using spectral analysis and the paper presents possible future research options and applications towards using physiological and facial parameters in controlling robotic arm.
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Onunka, C., Bright, G., Stopforth, R. (2014). Brainwave Variability Identification in Robotic Arm Control Strategy. In: Kim, JH., Matson, E., Myung, H., Xu, P., Karray, F. (eds) Robot Intelligence Technology and Applications 2. Advances in Intelligent Systems and Computing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-05582-4_16
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DOI: https://doi.org/10.1007/978-3-319-05582-4_16
Publisher Name: Springer, Cham
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