A Single-Channel SSVEP-Based BCI with a Fuzzy Feature Threshold Algorithm in a Maze Game

Chen, Shih-Chung; Chen, Yeou-Jiunn; Zaeni, Ilham Ari Elbaith; Wu, Chung-Min

doi:10.1007/s40815-016-0289-3

Cited by 30 publications

(22 citation statements)

References 30 publications

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“…Similar research has been carried out using SSVEP stimuli to control robot-like behaviour in [11] and [16] in which these authors again used fixed size and position stimuli symbols with differing frequencies that indicate different directions for the robot to move toward. In [11] the authors controlled a mobile robot by using 3 different SSVEP frequencies by moving forward or turning to the left or right in order to avoid the obstacles.…”

Section: Related Workmentioning

confidence: 95%

“…This occurs in the real-time as the humanoid robot navigates a natural indoor environment. In contrast to earlier work [3], [10], [11], [16], [20], our stimuli vary both in terms of pixel pattern, size and on-screen position in-conjunction with the changing nature of the environment the robot is navigating through.…”

Section: Related Workmentioning

confidence: 98%

“…In [11] the authors controlled a mobile robot by using 3 different SSVEP frequencies by moving forward or turning to the left or right in order to avoid the obstacles. The stimuli in [16] consisted of four fixed flickering boxes where each frequency was used to command a mobile robot (forward, backward, turn counter-clockwise/clockwise) to navigate the robot through a maze path.…”

Section: Related Workmentioning

confidence: 99%

“…Our key idea is to make the SSVEP stimuli more natural to the user, as the stimuli (or in our case, objects) will be presented in the context of the real-world scene the robot is currently navigating. Unlike previous stimuli [6], [11], [16], in this work the size of each SSVEP flicker region depends on the physical dimensions of the object detected. The detected object pixel regions are flickered at differing on screen frequencies (10,12,15 Hz) and the decoded EEG signals are used to navigate the robot to walk toward objects based on the objects selected by the subject (user) via the SSVEP interface.…”

Section: Introductionmentioning

confidence: 98%

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Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

Aznan

Connolly

Moubayed

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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2019) 'Using variable natural environment brain-computer interface stimuli for real-time humanoid robot navigation.any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.Abstract-This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-time object detection and dry-Electroencephalography (EEG) based human cortical brain bio-signals decoding. We employ recent advances in dry-EEG technology to stream and collect the cortical waveforms from subjects while they fixate on variable Steady State Visual Evoked Potential (SSVEP) stimuli generated directly from the environment the robot is navigating. To these ends, we propose the use of novel variable BCI stimuli by utilising the real-time video streamed via the on-board robot camera as visual input for SSVEP, where the CNN detected natural scene objects are altered and flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are not akin to traditional stimulias both the dimensions of the flicker regions and their on-screen position changes depending on the scene objects detected. Onscreen object selection via such a dry-EEG enabled SSVEP methodology, facilitates the on-line decoding of human cortical brain signals, via a specialised secondary CNN, directly into teleoperation robot commands (approach object, move in a specific direction: right, left or back). This SSVEP decoding model is trained via a priori offline experimental data in which very similar visual input is present for all subjects. The resulting classification demonstrates high performance with mean accuracy of 85% for the real-time robot navigation experiment across multiple test subjects.

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Section: Related Workmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

Aznan

Connolly

Moubayed

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…By transforming the time domain EEG signals to frequency domain, amplitudes and phases information of each stimulation frequency are obtained for further target identification procedure [86]. Many works [48], [49] about SSVEP-based BCIs employ Fourier transform due to its small computation time and simplicity. Estimating the phase of EEG signals is another fundamental issue of SSVEP-based BCI systems.…”

Section: B Ssvep Recognition and Classificationmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

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Using a novel modular continuous restricted Boltzmann machine to SSVEP-based BCIs for amyotrophic lateral sclerosis

et al. 2019

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A Single-Channel SSVEP-Based BCI with a Fuzzy Feature Threshold Algorithm in a Maze Game

Cited by 30 publications

References 30 publications

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

Using a novel modular continuous restricted Boltzmann machine to SSVEP-based BCIs for amyotrophic lateral sclerosis

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