Incorporating Neighboring Stimuli Data for Enhanced SSVEP-Based BCIs

Huang, Jiayang; Yang, Pengfei; Xiong, Bang; Wang, Quan; Wan, Bo; Ruan, Ziling; Yang, Keyi; Zhang, Zhiqiang

doi:10.1109/tim.2022.3219497

Cited by 9 publications

(6 citation statements)

References 36 publications

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“…The accuracy of the target subjects is provided here. The number range of training blocks is [3,5] for Dataset I and [2,4] for Dataset II. The heat maps visualize the highest classification accuracy and lowest accuracy using colors on a scale from light to dark.…”

Section: Accuracy (%)mentioning

confidence: 99%

“…Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provide humans a direct communication path between brain activities and external equipment without the need to move peripheral nerves or muscles [1]- [3]. Steadystate visual evoked potential (SSVEP) is one of the most popular paradigms in the research area of BCI due to its high signal-to-noise ratio (SNR), reliability, and minimal set up requirement [4]- [7]. SSVEP-based BCI has been broadly employed in various applications, such as communication [5], robot [8], [9], and smart home [10].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs

Zhang

Xie

Shi

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been substantially studied in recent years due to their fast communication rate and high signal-to-noise ratio. The transfer learning is typically utilized to improve the performance of SSVEP-based BCIs with auxiliary data from the source domain. This study proposed an intersubject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. In our method, the spatial filter was trained via multiple covariance maximization to extract SSVEP-related information. The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. The contribution scores of different source subjects can be calculated based on the distance between the source subject and the target subject. Finally, a fourdimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. The extensive experimental results validated the feasibility of the proposed method for improving SSVEP detection.

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Section: Accuracy (%)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs

Zhang

Xie

Shi

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…With the development of BCI technology, scholars have proposed Bacomics as a new cross-discipline [2]. The brain-machine interaction systems are mainly used to control external devices by resolving different types of electroencephalogram (EEG) signals, among which the common manners are motor imagery (MI) [3,4], P300 [5], and steady-state visual evoked potential (SSVEP) [6].…”

Section: Introductionmentioning

confidence: 99%

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding

Yang,

Zhao,

et al. 2024

Biomed. Phys. Eng. Express

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Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user's intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.

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“…Electroencephalography (EEG) is broadly employed in BCI research due to many advantages, such as non-invasiveness, high temporal resolution, and simple operation [9]. Steadystate visual evoked potential (SSVEP) as one of EEG paradigms has attracted significant attention because of its minimal training requirements and high signal-to-noise ratio (SNR) [10]- [12]. The SSVEP-based BCI system maps the brain signals to robot commands and then transmits them to the corresponding manipulation [13].…”

Section: Introductionmentioning

confidence: 99%

SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation

Zhang,

Qian,

Xie

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single-and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.

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Incorporating Neighboring Stimuli Data for Enhanced SSVEP-Based BCIs

Cited by 9 publications

References 36 publications

Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs

Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding

SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation

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