Multi-layer ear-scalp distillation framework for ear-EEG classification enhancement

Background. Ear-electroencephalography (ear-EEG) holds significant promise as a practical tool in brain-computer interfaces (BCIs) due to its enhanced unobtrusiveness, comfort, and mobility compared to traditional steady-state visual evoked potential (SSVEP)-based BCI systems. However, achieving accurate SSVEP classification with ear-EEG remains a major challenge due to the significant attenuation and distortion of the signal amplitude. Objective . Our aim is to enhance the classification performance of SSVEP using ear-EEG and to increase its practical application value. Approach . To address this challenge, we focus on enhancing ear-EEG feature representations by training the model to learn features similar to those of scalp-EEG. We introduce a novel framework, termed multi-layer ear-scalp distillation (MESD), designed to optimize SSVEP target classification in ear-EEG data. This framework combines signals from the scalp to obtain multi-layer distilled knowledge through the cooperation of mid-layer feature distillation and output layer response distillation. Main results . We improve the classification of the initial 1 s data and achieved a maximum classification accuracy of 81.1%. We evaluate the proposed MESD framework through single-session, cross-session, and cross-subject transfer decoding, comparing it with baseline methods. The results demonstrate that the proposed framework achieves the best classification results in all experiments. Significance . Our study enhances the classification accuracy of SSVEP based on ear-EEG within a short time window. These results offer insights for the application of ear-EEG brain-computer interfaces in tasks such as auxiliary control and rehabilitation training in future endeavors.