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

Chen, Xiaogang; Liu, Bingchuan; Wang, Yijun; Cui, Hongyan; Dong, Jianwei; Ma, Ruijuan; Li, Ning; Gao, Xiaorong

doi:10.1109/tnsre.2023.3243786

Cited by 16 publications

(5 citation statements)

References 51 publications

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“…Then, calibrated data in different groups were used to classify the EEG collected during the test stage containing 120 trials. The accuracy for different tested frequencies is obtained by calculating the confusion matrix [33]. As illustrated in figure 8, the accuracy corresponding to the test frequency decreased as the distance to the calibrated frequency increased.…”

Section: Online Experimental Resultsmentioning

confidence: 99%

Almost free of calibration for SSVEP-based brain-computer interfaces

Luo,

Xiao,

Chen

et al. 2023

J. Neural Eng.

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Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations. Approach. This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment. Main results. The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 seconds to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits/min with a peak of 242.6 bits/min. Significance. This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.

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Section: Online Experimental Resultsmentioning

confidence: 99%

Almost free of calibration for SSVEP-based brain-computer interfaces

Luo,

Xiao,

Chen

et al. 2023

J. Neural Eng.

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“…Chen et al . built an online high frequency (31–34.5 Hz, with an interval of 0.25 Hz) SSVEP-BCI system with 16 targets, which reached an average ITR of 153.79 bits/min 16 . Jiang et al .…”

Section: Background and Summarymentioning

confidence: 99%

An open dataset for human SSVEPs in the frequency range of 1-60 Hz

Gu,

Pei,

Gao

et al. 2024

Sci Data

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A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system relies on the photic driving response to effectively elicit characteristic electroencephalogram (EEG) signals. However, traditional visual stimuli mainly adopt high-contrast black-and-white flickering stimulations, which are easy to cause visual fatigue. This paper presents an SSVEP dataset acquired at a wide frequency range from 1 to 60 Hz with an interval of 1 Hz using flickering stimuli under two different modulation depths. This dataset contains 64-channel EEG data from 30 healthy subjects when they fixated on a single flickering stimulus. The stimulus was rendered on an LCD display with a refresh rate of 240 Hz. Initially, the dataset was rigorously validated through comprehensive data analysis to investigate SSVEP responses and user experiences. Subsequently, BCI performance was evaluated through offline simulations of frequency-coded and phase-coded BCI paradigms. This dataset provides comprehensive and high-quality data for studying and developing SSVEP-based BCI systems.

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“…Flexible tactile sensors have attracted significant attention recently owing to their comfortable interface and long-term continuous signal acquisition capabilities ( Guo et al, 2017 ; Li G. et al, 2020 ; Bae et al, 2022 ; Chen et al, 2023 ). Flexible tactile sensors can be integrated onto complex surfaces such as human skin or soft robots due to their low modulus, stretchability, and light weight ( Pang et al, 2020 ; Ainsworth et al, 2022 ; Liu et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Progress and prospects in flexible tactile sensors

Liu,

Wang,

Chen

2023

Front. Bioeng. Biotechnol.

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Flexible tactile sensors have the advantages of large deformation detection, high fault tolerance, and excellent conformability, which enable conformal integration onto the complex surface of human skin for long-term bio-signal monitoring. The breakthrough of flexible tactile sensors rather than conventional tactile sensors greatly expanded application scenarios. Flexible tactile sensors are applied in fields including not only intelligent wearable devices for gaming but also electronic skins, disease diagnosis devices, health monitoring devices, intelligent neck pillows, and intelligent massage devices in the medical field; intelligent bracelets and metaverse gloves in the consumer field; as well as even brain–computer interfaces. Therefore, it is necessary to provide an overview of the current technological level and future development of flexible tactile sensors to ease and expedite their deployment and to make the critical transition from the laboratory to the market. This paper discusses the materials and preparation technologies of flexible tactile sensors, summarizing various applications in human signal monitoring, robotic tactile sensing, and human–machine interaction. Finally, the current challenges on flexible tactile sensors are also briefly discussed, providing some prospects for future directions.

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

Cited by 16 publications

References 51 publications

Almost free of calibration for SSVEP-based brain-computer interfaces

Almost free of calibration for SSVEP-based brain-computer interfaces

An open dataset for human SSVEPs in the frequency range of 1-60 Hz

Progress and prospects in flexible tactile sensors

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