What is it about?
This research introduce a self-adjusting method based on brain EEG signals to improve control over robotic arms. This paper proposes an optimized data sampling model to identify the status of the human brain and further discover brain activity patterns. The sampling methods used in the proposed model include the segmented EEG graph using piecewise linear approximation (SEGPA) method, which incorporates optimized data sampling methods; and the EEG-based weighted network for EEG data analysis, which can be used for machinery control. The data sampling and segmentation techniques combine normal distribution approximation (NDA), Poisson distribution approximation (PDA), and related sampling methods. This research also proposes an efficient method for recognizing human thinking and brain signals with entropy-based frequent patterns (FPs). The obtained recognition system provides a foundation that could to be useful in machinery or robot control.
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Why is it important?
It's an important topic for brain control robotic arms. Much of research aims to realize this technology. This research aims to provide a novel method.
Perspectives
Future work will focus on delivering a more ecient algorithm for EEG pattern generation and on improving the EEG experimental data variety
Henry Zane
NIT, Zhejiang University
Read the Original
This page is a summary of: EEG Self-Adjusting Data Analysis Based on Optimized Sampling for Robot Control, Electronics, June 2020, MDPI AG,
DOI: 10.3390/electronics9060925.
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