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

Zhang, Shangen; Gao, Xiaorong; Chen, Xiaogang

doi:10.3389/fnhum.2022.908050

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…Subjects had a gaze distance of approximately 80 cm to the screen. SSVEP stimulations were encoded using the sampled sinusoidal stimulation method [22,23]. The nine targets were encoded with an initial phase of 0 and frequencies of 9 Hz, 9.75 Hz, 10.5 Hz, 9.25 Hz, 10 Hz, 10.75 Hz, 9.5 Hz, 10.25 Hz and 11 Hz, respectively.…”

Section: B Ssvep Stimulation Settingsmentioning

confidence: 99%

Transcranial Direct Current Stimulation-Based Neuromodulation Improves the Performance of Brain–Computer Interfaces Based on Steady-State Visual Evoked Potential

Zhang

Gao

Cui

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The study of brain state estimation and intervention methods is of great significance for the utility of brain-computer interfaces (BCIs). In this paper, a neuromodulation technology using transcranial direct current stimulation (tDCS) is explored to improve the performance of steady-state visual evoked potential (SSVEP)-based BCIs. The effects of pre-stimulation, sham-tDCS and anodal-tDCS are analyzed through a comparison of the EEG oscillations and fractal component characteristics. In addition, in this study, a novel brain state estimation method is introduced to assess neuromodulation-induced changes in brain arousal for SSVEP-BCIs. The results suggest that tDCS, and anodal-tDCS in particular, can be used to increase SSVEP amplitude and further improve the performance of SSVEP-BCIs. Furthermore, evidence from fractal features further validates that tDCS-based neuromodulation induces an increased level of brain state arousal. The findings of this study provide insights into the improvement of BCI performance based on personal state interventions and provide an objective method for quantitative brain state monitoring that may be used for EEG modeling of SSVEP-BCIs.

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Section: B Ssvep Stimulation Settingsmentioning

confidence: 99%

Transcranial Direct Current Stimulation-Based Neuromodulation Improves the Performance of Brain–Computer Interfaces Based on Steady-State Visual Evoked Potential

Zhang

Gao

Cui

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Currently, various methods are presented, such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and near infrared spectroscopy (NIRS), to monitor the brain activity and build BCIs [8]. Since EEG has the advantages of high time resolution, non-invasiveness, and cost effectiveness, it is widely adopted for building non-invasive BCIs [9], [10], [11], [12]. Currently, EEG has been used for developing multiple BCI paradigms [9], [13], [14], such as steady-state visual evoked potential (SSVEP)-based BCI, P300-based BCI, and sensorimotor rhythms (SMRs)-based BCI.…”

Section: Introductionmentioning

confidence: 99%

“…Among these existing EEG-based BCIs, SSVEP-BCI has been extensively investigated due to its high communication speed and less calibration time [15], [16], [17], [18], [19], [20], [21]. Currently, SSVEP-BCI has been applied in multiple fields, including robot control [12], [18], [22], [23], cognitive evaluation [24], [25], and text speller [15], [17], [20], etc. SSVEPs are periodic brain responses elicited by repeated visual stimuli, manifested by increased brain activities at stimulus frequencies and their harmonics [26].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Stimulus Frequency Ranges for Building a High-Rate High Frequency SSVEP-BCI

Chen

Liu

Wang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have been extensively explored due to their advantages in terms of high communication speed and smaller calibration time. The visual stimuli in the low-and medium-frequency ranges are adopted in most of the existing studies for eliciting SSVEPs. However, there is a need to further improve the comfort of these systems. The high-frequency visual stimuli have been used to build BCI systems and are generally considered to significantly improve the visual comfort, but their performance is relatively low. The distinguishability of 16-class SSVEPs encoded by the three frequency ranges, i.e., 31-34.75 Hz with an interval of 0.25 Hz, 31-38.5 Hz with an interval of 0.5 Hz, 31-46 Hz with an interval of 1 Hz, is explored in this study. We compare classification accuracy and information transfer rate (ITR) of the corresponding BCI system. According to the optimized frequency range, this study builds an online 16-target high frequency SSVEP-BCI and verifies the feasibility of the proposed system based on 21 healthy subjects. The BCI based on visual stimuli with the narrowest frequency range, i.e., 31-34.5 Hz, have the highest ITR. Therefore, the narrowest frequency range is adopted to build an online BCI system.

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“…As a matter of fact, the classification of SSVEPs can be performed with good results even with simple, trainingless algorithms, such as Power Spectral Density Analysis (PSDA) or Canonical Correlation Analysis (CCA) [17]. Nevertheless, there is much room for improvements aimed at employing SSVEP-based BCIs in challenging contexts, where the requirements are very demanding [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

et al. 2023

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This work addresses the employment of Machine Learning (ML) and Domain Adaptation (DA) in the framework of Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs). Currently, all the state-of-the-art classification strategies do not consider the high non-stationarity typical of brain signals. This can lead to poor performance, expecially when short-time signals have to be considered to allow real-time human-environment interaction. In this regard, ML and DA techniques can represent a suitable strategy to enhance the performance of SSVEPs classification pipelines. In particular, the employment of a two-step DA technique is proposed: first, the standardization of the data per subject is performed by exploiting a part of unlabeled test data during the training stage; second, a similarity measure between subjects is considered in the selection of the validation sets. The proposal was applied to three classifiers to verify the statistical significance of the improvements over the standard approaches. These classifiers were validated and comparatively tested on a well-known public benchmark dataset. An appropriate validation method was used in order to simulate real-world usage. The experimental results show that the proposed approach significantly improves the classification accuracy of SSVEPs. In fact, up to 62.27 % accuracy was achieved also in the case of short-time signals (i.e., 1.0 s). This represents a further confirmation of the suitability of advanced ML to improve the performance of BCIs for daily-life applications.

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

Cited by 10 publications

References 30 publications

Transcranial Direct Current Stimulation-Based Neuromodulation Improves the Performance of Brain–Computer Interfaces Based on Steady-State Visual Evoked Potential

Transcranial Direct Current Stimulation-Based Neuromodulation Improves the Performance of Brain–Computer Interfaces Based on Steady-State Visual Evoked Potential

Optimizing Stimulus Frequency Ranges for Building a High-Rate High Frequency SSVEP-BCI

Employment of Domain Adaptation Techniques in SSVEP-Based Brain–Computer Interfaces

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