Recognition of SSMVEP signals based on multi-channel integrated GT<sup>2</sup>&lt;inf&gt;circ&lt;/inf&gt; statistic method

Xie, Jun; Han, Xingliang; Xu, Guanghua; Zhang, Xiaodong; Li, Min; Luo, A-Li; Mu, Xiaoqi

doi:10.1109/urai.2017.7992703

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…For each trial data, the GT

statistic method (Xie et al, 2017 ) was used to determine the presence of SSMVEP at each stimulating frequency and its sub-harmonic. Three rectangular windows containing three cycles of three stimulating frequencies (840 sampling numbers of 8.6-Hz stimulation, 600 sampling numbers of 12-Hz stimulation, and 480 sampling numbers of 15-Hz stimulation) were separately slid over each trial with one-cycle overlap (280 sampling numbers of 8.6-Hz stimulation, 200 sampling numbers of 12-Hz stimulation, and 160 sampling numbers of 15-Hz stimulation), according to the sampling rate of 1,200 Hz.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface

Xie

Zhao

et al. 2018

Front. Neurorobot.

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Neuroplasticity, also known as brain plasticity, is an inclusive term that covers the permanent changes in the brain during the course of an individual's life, and neuroplasticity can be broadly defined as the changes in function or structure of the brain in response to the external and/or internal influences. Long-term potentiation (LTP), a well-characterized form of functional synaptic plasticity, could be influenced by rapid-frequency stimulation (or “tetanus”) within in vivo human sensory pathways. Also, stochastic resonance (SR) has brought new insight into the field of visual processing for the study of neuroplasticity. In the present study, a brain-computer interface (BCI) intervention based on rapid and repetitive motion-reversal visual stimulation (i.e., a “tetanizing” stimulation) associated with spatiotemporal visual noise was implemented. The goal was to explore the possibility that the induction of LTP-like plasticity in the visual cortex may be enhanced by the SR formalism via changes in the amplitude of visual evoked potentials (VEPs) measured non-invasively from the scalp of healthy subjects. Changes in the absolute amplitude of P1 and N1 components of the transient VEPs during the initial presentation of the steady-state stimulation were used to evaluate the LTP-like plasticity between the non-noise and noise-tagged BCI interventions. We have shown that after adding a moderate visual noise to the rapid-frequency visual stimulation, the degree of the N1 negativity was potentiated following an ~40-min noise-tagged visual tetani. This finding demonstrated that the SR mechanism could enhance the plasticity-like changes in the human visual cortex.

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“…For each trial data, the GT

Section: Methodsmentioning

confidence: 99%

Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface

Xie

Zhao

et al. 2018

Front. Neurorobot.

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“…Moreover, the researcher has found that it's crucial to set up algorithms that can adapt to the variation in the subjects' cognitive behaviors and enhance the strength and feasibility of BCIs. EEG contingent BCIs, event-related synchronization and desynchronization (ERS/ERD), mu and beta rhythms, P300 visual evoked potentials, and steady-state visual evoked potentials (SSVEP; Xie et al, 2017 ) can all be considered rehabilitation techniques. Thus, in Zhang L. et al ( 2017 ) and Zhang X. et al ( 2017 ), a novel object-oriented SSVEP-BCI model has been tested.…”

Section: Traditional Cooperative Controlmentioning

confidence: 99%

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

et al. 2023

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Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed.

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“…In recent years, there has been growing interest in steady state motion visual evoked potentials (SSMVEPs), especially based on the motion-reversal patterns. One of the proposed solutions for this specific (motion-reversal) behavior is the use of oscillating Newton’s rings [ 14 , 15 , 16 ], an oscillation (contraction-expansion) of a circled checkerboard pattern [ 17 , 18 ] or a swing motion, a radial rotation and a spiral rotation [ 19 ]. Xie et al [ 14 ] tested six participants in an offline experiment with oscillating Newton’s rings-based stimulation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain–Computer Interfaces

Stawicki

Volosyak

2020

Brain Sciences

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Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials—SSMVEP), without employing the typical flickering. The tested movement patterns for the visual stimuli included a pendulum-like movement, a flipping illusion, a checkerboard pulsation, checkerboard inverse arc pulsations, and reverse arc rotations, all with a spelling task consisting of 18 trials. In an online experiment with nine participants, the movement-based BCI systems were evaluated with an online four-target BCI-speller, in which each letter may be selected in three steps (three trials). For classification, the minimum energy combination and a filter bank approach were used. The following frequencies were utilized: 7.06 Hz, 7.50 Hz, 8.00 Hz, and 8.57 Hz, reaching an average accuracy between 97.22% and 100% and an average information transfer rate (ITR) between 15.42 bits/min and 33.92 bits/min. All participants successfully used the SSMVEP-based speller with all types of stimulation pattern. The most successful SSMVEP stimulus was the SSMVEP1 (pendulum-like movement), with the average results reaching 100% accuracy and 33.92 bits/min for the ITR.

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Recognition of SSMVEP signals based on multi-channel integrated GT²<inf>circ</inf> statistic method

Cited by 3 publications

References 15 publications

Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface

Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain–Computer Interfaces

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Recognition of SSMVEP signals based on multi-channel integrated GT2<inf>circ</inf> statistic method

Cited by 3 publications

References 15 publications

Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface

Enhanced Plasticity of Human Evoked Potentials by Visual Noise During the Intervention of Steady-State Stimulation Based Brain-Computer Interface

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Comparison of Modern Highly Interactive Flicker-Free Steady State Motion Visual Evoked Potentials for Practical Brain–Computer Interfaces

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Recognition of SSMVEP signals based on multi-channel integrated GT²<inf>circ</inf> statistic method