FB-EEGNet: A fusion neural network across multi-stimulus for SSVEP target detection

Yao, Huiming; Liu, Ke; Deng, Xin; Tang, Xianlun; Yu, Hong

doi:10.1016/j.jneumeth.2022.109674

Cited by 19 publications

(9 citation statements)

References 25 publications

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“…Since CCNN, EEGNet and SSVEPformer have been tested and compared on Dataset 2 in previous studies [ 36 , 42 , 43 ], only atten-CCNN and CCNN were used for comparison in this part to emphasize the changes in the performance of the proposed model relative to its original model. Figure 5 illustrates the performance of atten-CCNN and CCNN on Dataset 2 with a three-channel EEG.…”

Section: Resultsmentioning

confidence: 99%

A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG

Li,

Yang,

Fei

et al. 2024

Bioengineering

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The application of wearable electroencephalogram (EEG) devices is growing in brain–computer interfaces (BCI) owing to their good wearability and portability. Compared with conventional devices, wearable devices typically support fewer EEG channels. Devices with few-channel EEGs have been proven to be available for steady-state visual evoked potential (SSVEP)-based BCI. However, fewer-channel EEGs can cause the BCI performance to decrease. To address this issue, an attention-based complex spectrum–convolutional neural network (atten-CCNN) is proposed in this study, which combines a CNN with a squeeze-and-excitation block and uses the spectrum of the EEG signal as the input. The proposed model was assessed on a wearable 40-class dataset and a public 12-class dataset under subject-independent and subject-dependent conditions. The results show that whether using a three-channel EEG or single-channel EEG for SSVEP identification, atten-CCNN outperformed the baseline models, indicating that the new model can effectively enhance the performance of SSVEP-BCI with few-channel EEGs. Therefore, this SSVEP identification algorithm based on a few-channel EEG is particularly suitable for use with wearable EEG devices.

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Section: Resultsmentioning

confidence: 99%

A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG

Li,

Yang,

Fei

et al. 2024

Bioengineering

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“…EEGNet, a compact convolutional neural network initially designed for classifying multiple BCI paradigms including P300 visual-evoked potential, error-related negativity responses (ERN), movement-related cortical potential (MRCP), and sensor motor rhythms (SMRs), has also been widely utilized as a basic module in CNN models. In Yao’s research, three EEGNets were used as sub-networks in his CNN model [ 73 ]. Likewise, Li modified EEGNet and applied transfer learning to initially train the model parameters [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

“…Chen also found that, compared to using two or four filter banks, using three filter banks provided the best performance [ 64 ]. Yao built three filter banks and then fed the input to three EEGNets used as sub-networks separately before merging the features together [ 73 ]. These studies showed that a filter bank is an effective tool to process the SSVEP input and make frequency features easier to extract by the deep learning models.…”

Section: Model Inputmentioning

confidence: 99%

An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey

Tang

et al. 2023

Brain Sciences

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The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011–2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.

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“…Its enhanced effect of harmonic correlation on the classification performance was verified while achieving higher accuracy. Yao et al [18] designed a classification method that combined the use of filter banks and EEGNet. The method combined features from multiple sub-networks, where every sub-network extracted features of the specified SSVEP sub-band data.…”

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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FB-EEGNet: A fusion neural network across multi-stimulus for SSVEP target detection

Cited by 19 publications

References 25 publications

A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG

A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG

An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey

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

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