A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG.
Bioengineering (Basel)
; 11(6)2024 Jun 15.
Article
en En
| MEDLINE
| ID: mdl-38927850
ABSTRACT
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Bioengineering (Basel)
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Suiza