Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework

Ke, Yufeng; Du, Jiale; Liu, Shuang; Ming, Dong

doi:10.1109/tnsre.2023.3246359

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…Table 7 shows that the majority of the 20 SSVEP decoding algorithm papers analyzed used more than one dataset, with only three papers [ 18 , 20 , 25 ] using self-collected data verification methods. Among the 20 papers, 40% (8/20) used only one dataset, while 80% (16/20) used no more than two datasets, and the remaining four papers used only three datasets for verification.…”

Section: Discussionmentioning

confidence: 99%

“…However, the majority of current algorithmic research utilizes one or two datasets to verify their performance [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], which did not make full use of public data resources, and the results were limited by the distribution of data samples in individual datasets, so it was not conducive to judge the application effect of the algorithm in the actual scene through the result. This issue has two underlying causes.…”

Section: Introductionmentioning

confidence: 99%

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Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

Liang

Zhang

Zhou

et al. 2023

Sensors

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Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are differences in sample distribution and collection equipment across different datasets, and there is a lack of a unified evaluation method. Most new SSVEP decoding algorithms are tested based on self-collected data or offline performance verification using one or two previous datasets, which can lead to performance differences when used in actual application scenarios. To address these issues, this paper proposed a SSVEP dataset evaluation method and analyzed six datasets with frequency and phase modulation paradigms to form an SSVEP algorithm evaluation dataset system. Finally, based on the above datasets, performance tests were carried out on the four existing SSVEP decoding algorithms. The findings reveal that the performance of the same algorithm varies significantly when tested on diverse datasets. Substantial performance variations were observed among subjects, ranging from the best-performing to the worst-performing. The above results demonstrate that the SSVEP dataset evaluation method can integrate six datasets to form a SSVEP algorithm performance testing dataset system. This system can test and verify the SSVEP decoding algorithm from different perspectives such as different subjects, different environments, and different equipment, which is helpful for the research of new SSVEP decoding algorithms and has significant reference value for other BCI application fields.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

Liang

Zhang

Zhou

et al. 2023

Sensors

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“…BCI can be classified into two modes: synchronous and asynchronous [6], [7], [8], [9], [10]. The synchronous BCI system is characterized by the use of predefined time windows with a specific cue or trigger that indicates the onset of mental activity.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI

Ding,

Liu,

Guan

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the limited amount of electroencephalography (EEG) data, leading to overfitting. This study proposes an effective data augmentation approach called EEG mask encoding (EEG-ME) to mitigate overfitting. EEG-ME forces models to learn more robust features by masking partial EEG data, leading to enhanced generalization capabilities of models. Three different network architectures, including an architecture integrating convolutional neural networks (CNN) with Transformer (CNN-Former), time domain-based CNN (tCNN), and a lightweight architecture (EEGNet) are utilized to validate the effectiveness of EEG-ME on publicly available benchmark and BETA datasets. The results demonstrate that EEG-ME significantly enhances the average classification accuracy of various DL-based methods with different data lengths of time windows on two public datasets. Specifically, CNN-Former, tCNN, and EEGNet achieve respective improvements of 3.18%, 1.42%, and 3.06% on the benchmark dataset as well as 11.09%, 3.12%, and 2.81% on the BETA dataset, with the 1-second time window as an example. The enhanced performance of SSVEP classification with EEG-ME promotes the implementation of the asynchronous SSVEP-BCI system, leading to improved robustness and flexibility in human-machine interaction.

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Discriminating brainwave patterns of different control and non-control states for enhancing asynchronous brain-computer interfaces

Zhao,

Li,

Wang

et al. 2024

Expert Systems with Applications

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Enhancing Detection of Control State for High-Speed Asynchronous SSVEP-BCIs Using Frequency-Specific Framework

Cited by 7 publications

References 48 publications

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm

A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI

Discriminating brainwave patterns of different control and non-control states for enhancing asynchronous brain-computer interfaces

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