Optimization of Stimulus Color for SSVEP-Based Brain-Computer Interfaces in Mixed Reality

He, Feng; Wu, Jieyu; Xiao, Xiaolin; Gao, Runyuan; Yi, Weibo; Chen, Yuanfang; Xu, Minpeng; Jung, Tzyy-Ping; Ming, Dong

doi:10.1007/978-981-19-8222-4_16

Cited by 1 publication

(2 citation statements)

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“…The nature of the OST headset meant that the stimuli appeared bright while the background was relatively dull. Reducing the brightness of the stimuli could make it easier to see the environment but may lead to reduced contrast, which in turn reduces SSVEP decoding accuracy [40]. However, contrast can be improved by using red stimuli, which provide a high contrast relative to the typically white/grey home, office or hospital environments [10], [40].…”

Section: Scene Visibilitymentioning

confidence: 99%

“…Reducing the brightness of the stimuli could make it easier to see the environment but may lead to reduced contrast, which in turn reduces SSVEP decoding accuracy [40]. However, contrast can be improved by using red stimuli, which provide a high contrast relative to the typically white/grey home, office or hospital environments [10], [40]. Alternatively, although VST-AR systems have not been used much in SSVEP-BCIs, it would be interesting to evaluate the ability of the latest VST-AR systems to clearly display stimuli and the environment background (e.g., by dynamically maintaining contrast).…”

Section: Scene Visibilitymentioning

confidence: 99%

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Semi-Autonomous Continuous Robotic Arm Control Using an Augmented Reality Brain-Computer Interface

Kokorin,

Zehra,

et al. 2024

Preprint

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Noninvasive augmented-reality (AR) brain-computer interfaces (BCIs) that use steady-state visually evoked potentials (SSVEPs) typically adopt a fully autonomous goal-selection framework to control a robot, where automation is used to compensate for the low information transfer rate of the BCI. This scheme improves task performance but users may prefer direct control (DC) of robot motion. To provide users with a balance of autonomous assistance and manual control, we developed a shared control (SC) system for continuous control of robot translation using an SSVEP AR-BCI, which we tested in a 3D reaching task. The SC system used the BCI input and robot sensor data to continuously predict which object the user wanted to reach, generated an assistance signal, and regulated the level of assistance based on prediction confidence. Eighteen healthy participants took part in our study and each completed 24 reaching trials using DC and SC. Compared to DC, SC significantly improved (paired two-tailed t-test, Holm-corrected α<0.05) mean task success rate (p<0.0001, µ=36.1%, 95% CI [25.3%, 46.9%]), normalised reaching trajectory length (p<0.0001, µ=-26.8%, 95% CI [-36.0%,-17.7%]), and participant workload (p<0.02, µ=-11.6, 95% CI [-21.1,-2.0]) measured with the NASA Task Load Index. Therefore, users of SC can control the robot effectively, while experiencing increased agency. Our system can personalise assistive technology by providing users with the ability to select their preferred level of autonomous assistance.

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Section: Scene Visibilitymentioning

confidence: 99%