EEGNet With Ensemble Learning to Improve the Cross-Session Classification of SSVEP Based BCI From Ear-EEG

Ear-electroencephalography (ear-EEG) using electrodes placed above hairless areas around ears is a convenient and comfortable method for signal recording in practical applications of steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI). However, due to the constraint of electrode distribution behind the ear, the amplitude of SSVEP in ear-EEG signals is relatively low, which hinders the application of ear-EEG in SSVEP-based BCI. This study was aimed to improve the performance of ear-EEG in SSVEP-based BCI through re-implementing a compact convolutional neural network (EEGNet) with ensemble learning. We first evaluated the feasibility of applying widely used EEGNet models with different kernel numbers to decode SSVEP in ear-EEG signals. Then we applied an ensemble learning strategy to combine EEGNet models with different kernel numbers to improve the classification of ear-EEG signals. The ear-EEG data was from an open dataset, which acquired three sessions of SSVEP data induced by three flicker stimuli from eleven subjects. The average accuracy of EEGNet with ensemble learning for ear-EEG signals in cross-session validations at 1 s window length was 81.12% (from session 1 to session 2) and 81.74% (from session 1 to session 3), which significantly outperformed canonical correlation analysis (CCA). In addition, the network visualization indicated that EEGNet extracted features related to stimulation frequencies. The results showed promise for accurate classification of SSVEP in ear-EEG signals using deep learning models with strategies, helping to promote the SSVEP based BCI from laboratory to practical application.