Abstract
An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.
Original language | English |
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Pages (from-to) | e0150667 |
Journal | PLoS ONE |
Volume | 11 |
Issue number | 3 |
DOIs | |
Publication status | Published - 16 Mar 2016 |
ASJC Scopus subject areas
- General Biochemistry,Genetics and Molecular Biology
- General Agricultural and Biological Sciences
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Dingguo Zhang
- Department of Electronic & Electrical Engineering - Reader in Robotics Engineering
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Bioengineering & Biomedical Technologies (CBio)
- Bath Institute for the Augmented Human
- IAAPS: Propulsion and Mobility
Person: Research & Teaching, Core staff, Affiliate staff