Indirect Control of an Autonomous Wheelchair Using SSVEP BCI

Ng, Danny Wee-Kiat; Goh, Sing-Yau

doi:10.20965/jrm.2020.p0761

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…This study combined brain–computer interface with robot intelligence, and verified the feasibility of BCI-based robot control in performing complex tasks. BCI has been shown to enable excellent control of robotic devices by using non-SSVEP methods ( Meng et al, 2016 ; Edelman et al, 2019 ; Pulferer et al, 2022 ), while SSVEP-BCI method is considered to have a better potential for controlling robotics due to its high information transfer rate, good signal-to-noise ratio, easy quantification, and less user training ( Ng and Goh, 2020 ; Li and Kesavadas, 2021 ). In this study, we designed an online experiment in which the SSVEP-BCI system and humanoid robot were combined for the robot walking task in the maze.…”

Section: Discussionmentioning

confidence: 99%

Humanoid Robot Walking in Maze Controlled by SSVEP-BCI Based on Augmented Reality Stimulus

Zhang

Gao

Chen

2022

Front. Hum. Neurosci.

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The application study of robot control based brain-computer interface (BCI) not only helps to promote the practicality of BCI but also helps to promote the advancement of robot technology, which is of great significance. Among the many obstacles, the importability of the stimulator brings much inconvenience to the robot control task. In this study, augmented reality (AR) technology was employed as the visual stimulator of steady-state visual evoked potential (SSVEP)-BCI and the robot walking experiment in the maze was designed to testify the applicability of the AR-BCI system. The online experiment was designed to complete the robot maze walking task and the robot walking commands were sent out by BCI system, in which human intentions were decoded by Filter Bank Canonical Correlation Analysis (FBCCA) algorithm. The results showed that all the 12 subjects could complete the robot walking task in the maze, which verified the feasibility of the AR-SSVEP-NAO system. This study provided an application demonstration for the robot control base on brain–computer interface, and further provided a new method for the future portable BCI system.

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Section: Discussionmentioning

confidence: 99%

Humanoid Robot Walking in Maze Controlled by SSVEP-BCI Based on Augmented Reality Stimulus

Zhang

Gao

Chen

2022

Front. Hum. Neurosci.

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“…The other study of SSVEP is [49]. The model created for this work can be used in low, high, and hybrid formats.…”

Section: Comparison and Detailed Descriptionmentioning

confidence: 99%

Towards Practical BCI-Driven Wheelchairs: A Systematic Review Study

Naser

Bhattacharya

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The use of brain signals in controlling wheelchairs is a promising solution for many disabled individuals, specifically those who are suffering from motor neuron disease affecting the proper functioning of their motor units. Almost two decades since the first work, the applicability of EEG-driven wheelchairs is still limited to laboratory environments. In this work, a systematic review study has been conducted to identify the state-of-the-art and the different models adopted in the literature. Furthermore, a strong emphasis is devoted to introducing the challenges impeding a broad use of the technology as well as the latest research trends in each of those areas.

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“…First, ADC resolution is a crucial factor affecting the EEG signal acquisition quality [52]. A higher resolution allows the ADC to convert smaller voltage variations, thus improving signal accuracy.…”

Section: Analog-to-digital Conversion Circuitmentioning

confidence: 99%

Application and Development of EEG Acquisition and Feedback Technology: A Review

Qin,

Zhang,

Zhang

et al. 2023

Biosensors

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This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain–computer interface technology utilizes EEG signals for human–computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain–computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.

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Indirect Control of an Autonomous Wheelchair Using SSVEP BCI

Cited by 8 publications

References 18 publications

Humanoid Robot Walking in Maze Controlled by SSVEP-BCI Based on Augmented Reality Stimulus

Humanoid Robot Walking in Maze Controlled by SSVEP-BCI Based on Augmented Reality Stimulus

Towards Practical BCI-Driven Wheelchairs: A Systematic Review Study

Application and Development of EEG Acquisition and Feedback Technology: A Review

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