Evaluation of an online SSVEP-BCI with fast system setup

Li, Xiaodong; Wang, Junlin; Cao, Xiang; Huang, Yong; Huang, Wei; Wan, Feng; Kai-Tsun To, Michael; Xie, Sheng Quan

doi:10.1016/j.jnrt.2024.100122

Cited by 1 publication

(1 citation statement)

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“…A trial contained a 1 s cue, a 3 s visual stimulation and a 1 s rest. It is noted that a strategy of simplifying the system setup was adopted in this experiment to reduce the experimental preparation time and enhance the practicality of the BCI in real-life applications [ 33 ]. As a result, the signal quality of this dataset might be degraded.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%