Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

Lee, Hyeon Kyu; Choi, Young-Seok

doi:10.3390/s21041315

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…Due to the development of BCI detection algorithm, the short-time detection performance of SSVEP-BCIs has almost reached its limit. It is significant to design a more user-friendly BCI paradigm based on the characteristics of existing EEG signal algorithms [25], [35], [36]. good BCI stimulation paradigm can be defined with the following features: (1) Visual fatigue caused by stimulation can be accepted by most users; (2) it can be easily transplanted to various control scenes; (3) it can well adapt to existing EEG detection algorithms; (4) it supports encoding a large number of operation instructions.…”

Section: Discussionmentioning

confidence: 99%

An SSPVEP Brain-Computer Interface Using a Small Amount of Flicker Stimuli

Zhao

Wang

et al. 2022

IEEE Access

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Brain-computer interface based on steady-state visual evoked potential (SSVEP-BCI) has been widely concerned because of its highest SNR of EEG signals. This kind of BCIs needs to modulate the control instructions with visual stimuli. However, a large number of visual stimuli will induce strong visual fatigue, and occupy a large area of screen space. As a result of the above disadvantages, the further application of this kind of BCIs can not be accepted by most users. It has been verified that 40 control instructions can be encoded with 20 flicker stimuli. The visual fatigue caused by SSVEP-BCIs can be alleviated by reducing the number of flicker stimuli. However, whether it is possible to encode the same number of control instructions with fewer flicker stimuli or not still remains unknown. This study will show the performance of the SSPVEP-BCIs after further removing some redundant flicker stimuli. We divide the proposed BCI system into four subsystems, and the performance of each subsystem will be shown in this paper. 12 flickering disks were used as visual stimuli to stimulate the brain to produce SSVEP responses. 12 targets were set on the flickering disks, and 28 targets were set alternately between the flicker stimuli, ensuring that always exist 1 to 2 flicker stimuli near each target. Manhattan distance based task-related component analysis (MTRCA) was used to process and decode the EEG signals in this paper. The performance within each of the subsystems was investigated and compared. When the sampling time was set to 4.6 s, the average accuracy of all subjects reaches 77.12%. This result shows that the 40-target BCI system based on proposed stimulation paradigm is available to the vast majority of users. Meanwhile, the accuracy of three subsystems is greater than 80% when the sampling time is 4.6 s. This result shows that the performance of SSPVEP stimulation paradigm is accepted in the application scenarios which only require a small number of instructions. In this paper, the research of SSPVEP paradigm is improved. It is revealed that using less flicker stimuli can still stimulate the brain to produce classifiable SSVEP responses. The conclusion of this study can guide the following study and application of SSPVEP-BCIs.

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Section: Discussionmentioning

confidence: 99%

An SSPVEP Brain-Computer Interface Using a Small Amount of Flicker Stimuli

Zhao

Wang

et al. 2022

IEEE Access

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“…This constrained optimization problem can be transformed into a Rayleigh-Ritz eigenvalue problem by Covariance maximization [35]. Similar to filter bank canonical correlation analysis, applying filter bank analysis to TRCA can significantly improve performance [36]. The filter bank analysis is applied to the EEG data for decomposing it into 𝑁 𝑘 sub-bands.…”

Section: E Trca-based Methodsmentioning

confidence: 99%

Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy

Han,

Ke,

Wang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Steady-state visual evoked potential (SSVEP)based brain-computer interfaces (BCIs) have gained significant attention in recent years due to their advantages, including high communication rates, short calibration times, and high signal-to-noise ratios. However, one of the major challenges in practical application is performance degradation due to fatigue during continuous and prolonged use. This study aims to mitigate the negative effects of fatigue on SSVEP-BCI performance by adjusting data lengths and updating detection models using dynamic stopping strategy. Two 16-target SSVEP-BCIs were used for data collection, one using low-frequency stimulation and the other using high-frequency stimulation. A self-recorded fatigue dataset from twenty-four subjects was used for extensive evaluation studies. The simulated online experiment demonstrated that the proposed methods outperformed the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, regardless of stimulus frequency. In conclusion, the proposed methods can improve the performance of the SSVEP-BCI in fatigue state and provide superior performance over extended periods of sustained use.

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“…Many SSVEP decoding algorithms exist for this purpose and have been widely studied [6]–[9]. It is often reported that including historical ‘training’ or calibration data - whether subject specific or across multiple subjects - offers significantly superior decoding performance compared to inference on instantaneous test signals (as with standard CCA) [7]–[9].…”

Section: Introductionmentioning

confidence: 99%

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding

Teversham

Wong

Hsieh

et al. 2022

Preprint

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This study details the development of a novel, approx. 20 pound (GBP) electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 ± 3.74% with an ITR of 102 bits/min was achieved with modest calibration.

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Enhancing SSVEP-Based Brain-Computer Interface with Two-Step Task-Related Component Analysis

Cited by 6 publications

References 30 publications

An SSPVEP Brain-Computer Interface Using a Small Amount of Flicker Stimuli

An SSPVEP Brain-Computer Interface Using a Small Amount of Flicker Stimuli

Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding

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