Author
Zhenghua Wu
Bio: Zhenghua Wu is an academic researcher from University of Electronic Science and Technology of China. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.
Papers
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TL;DR: The results show that the RE method can be adopted in a real-time SSVEP-based brain–computer interface (BCI) system, and the detection accuracy is similar to the CCA method, although it is higher than that of the PS method in situations where theSSVEP has low strength.
3 citations
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TL;DR: A novel method based on underdamped second-order stochastic resonance (USSR) is proposed, which effectively improves the information transmission rate (ITR) of SSVEP-based BCI and is compared with the widely-used canonical coefficient analysis and multivariate synchronization index.
Abstract: Objective As one of the commonly used control signals of brain-computer interface (BCI), steady-state visual evoked potential (SSVEP) exhibits advantages of stability, periodicity and minimal training requirements. However, SSVEP retains the non-linear, non-stationary and low signal-to-noise ratio (SNR) characteristics of EEG. The traditional SSVEP extraction methods regard noise as harmful information and highlight the useful signal by suppressing the noise. In the collected EEG, noise and SSVEP are usually coupled together, the useful signal is inevitably attenuated while the noise is suppressed. Also, an additional band-pass filter is needed to eliminate the multi-scale noise, which causes the edge effect. Approach To address this issue, a novel method based on underdamped second-order stochastic resonance (USSR) is proposed in this paper for SSVEP extraction. Main results A synergistic effect produced by noise, useful signal and the nonlinear system can force the energy of noise to be transferred into SSVEP, and hence amplifying the useful signal while suppressing multi-scale noise. The recognition performances of detection are compared with the widely-used canonical coefficient analysis (CCA) and multivariate synchronization index (MSI). Significance The comparison results indicate that USSR exhibits increased accuracy and faster processing speed, which effectively improves the information transmission rate (ITR) of SSVEP-based BCI.
11 citations
TL;DR: A novel method for the detection of SSVEP that combines the empirical mode decomposition (EMD) and a power spectral peak analysis (PSPA) is proposed that was evaluated with two EEG datasets, and was compared with the widely used FB and CCA.
3 citations
31 Aug 2018
TL;DR: A design scheme that the brain controls UAVs system based on cloud platform using brainwaves collected by visual evoked and a fixed encoding format is used to control the flight of the aircraft.
Abstract: With the development of science and technology, unmanned aerial vehicles(UAVs) are widely used in various industries, and the research for the Minitype UAVs have been widely studied. This paper introduces a design scheme that the brain controls UAVs system based on cloud platform. Brainwaves of the subject's occipital lobe were collected by visual evoked, which transmits to the mobile phone via Bluetooth acquisition, data processing, and a fixed encoding format is used to control the flight of the aircraft, Environmental monitoring platform is loaded simultaneously, Users can remotely observe the flight status, environmental conditions and so on by the browser. The scheme has a good prospect of application.