Remote Steering of a Mobile Robotic Car by Means of VR-Based SSVEP BCI

Stawicki, Piotr; Gembler, Felix; Grichnik, Roland; Volosyak, Ivan

doi:10.1007/978-3-030-20521-8_34

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Wang et al designed BCI commands at four subharmonic frequencies (6.4, 7.5, 9.0, and 11.25 Hz on a 90Hz HMD) to control a flying drone [15]. Stawicki et al implemented a three-command SSVEP-based BCI using three subharmonic frequencies (7.2, 9.0 and 12.0 Hz) on a 72 Hz HMD to control a robot car [14]. In the aforementioned studies, the available command number and the stimulation frequencies were low which led to lower ITRs and poor visualization.…”

Section: Discussionmentioning

confidence: 99%

“…Volosyak et al even indicated that only five subharmonic frequencies (6.67, 7.50, 8.57, 10, and 12 Hz) of a 60 Hz display system, were suitable for designing BCI commands [13]. Due to the development of display technologies, some studies have adopted display devices with higher refreshing rate, such as 72 Hz [14], 90 Hz [15], and 120 Hz [16], to increase the available number of BCI commands. However, since the majority of popular consumer products (standalone HMD) is based on 60 Hz refreshing rate, a frequency approximation method, which enables the synthesis of user-specified stimulation frequency on a 60 Hz display device, is worthy to be developed.…”

Section: Introduction Brain Computer Interface (Bci) Is a Promising Techniquementioning

confidence: 99%

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Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD

Hsu

Shyu

Hsu

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Steady-state visual evoked potential (SSVEP) has been used to implement brain-computer interface (BCI) due to its advantages of high information transfer rate (ITR) and high accuracy. In recent years, owing to the developments of head-mounted device (HMD), the HMD has become a popular device to implement SSVEP-based BCI. However, an HMD with fixed frame rate only can flash at its subharmonic frequencies which limits the available number of stimulation frequencies for SSVEP-based BCI. In order to increase the number of available commands for SSVEP-based BCI, we proposed a phase-approaching (PA) method to generate visual stimulation sequences at user-specified frequency on an HMD. The flickering sequence generated by our PA method (PAS sequence) tries to approximate user-specified stimulation frequency by means of minimizing the difference of accumulated phases between our PAS sequence and the ideal wave of user-specified frequency. The generated sequence of PA method determines the brightness state for each frame to approach the accumulated phase of the ideal wave. The SSVEPs evoked from stimulators, driven by PAS sequences, were analyzed using canonical correlation analysis (CCA) to identify user's gazed target. In this study, a six-command SSVEP-based BCI was designed to operate a flying drone. The ITR and detection accuracy are 36.84 bits/min and 93.30%, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Introduction Brain Computer Interface (Bci) Is a Promising Techniquementioning

confidence: 99%

Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD

Hsu

Shyu

Hsu

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…The second is the stereoscopic stimulation target (SST), which induces the SSVEP through three-dimensional stimulation flicker. The PST is commonly used as the visual stimulus in the SSVEP-BCI-VR [22,23], while the SST is underutilized. Although the SST parameters (shape, color, and frequency) have a significant impact on the performance of the SSVEP-BCI, they have not been extensively researched.…”

Section: Introductionmentioning

confidence: 99%

Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph

et al. 2023

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The steady-state visually evoked potential (SSVEP) is an important type of BCI that has various potential applications, including in virtual environments using virtual reality (VR). However, compared to VR research, the majority of visual stimuli used in the SSVEP-BCI are plane stimulation targets (PSTs), with only a few studies using stereo stimulation targets (SSTs). To explore the parameter optimization of the SSVEP-BCI virtual SSTs, this paper presents a parameter knowledge graph. First, an online VR stereoscopic stimulation SSVEP-BCI system is built, and a parameter dictionary for VR stereoscopic stimulation parameters (shape, color, and frequency) is established. The online experimental results of 10 subjects under different parameter combinations were collected, and a knowledge graph was constructed to optimize the SST parameters. The best classification performances of the shape, color, and frequency parameters were sphere (91.85%), blue (94.26%), and 13Hz (95.93%). With various combinations of virtual reality stereo stimulation parameters, the performance of the SSVEP-BCI varies. Using the knowledge graph of the stimulus parameters can help intuitively and effectively select appropriate SST parameters. The knowledge graph of the stereo target stimulation parameters presented in this work is expected to offer a way to convert the application of the SSVEP-BCI and VR.

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The Current Research of Combining Multi-Modal Brain-Computer Interfaces With Virtual Reality

Dong

Liang

Zhou

et al. 2021

IEEE J. Biomed. Health Inform.

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Remote Steering of a Mobile Robotic Car by Means of VR-Based SSVEP BCI

Cited by 4 publications

References 15 publications

Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD

Phase-Approaching Stimulation Sequence for SSVEP-Based BCI: A Practical Use in VR/AR HMD

Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph

The Current Research of Combining Multi-Modal Brain-Computer Interfaces With Virtual Reality

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