B a c k g r o u n d . 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. O b j e c t i v e . Our aim is to enhance the classification performance of SSVEP using ear-EEG and to increase its practical application value. A p p r o a c h . 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. M a i n r e s u l t s . 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. S i g n i f i c a n c e . 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.

Non‐invasive brain‐computer interfaces (BCIs) that monitor brain activity via hemodynamic signals enable motor and language rehabilitation for individuals with disabilities and provide multiple communication capabilities, such as sleep monitoring and fatigue alerts, for healthy individuals. Here, we propose a wearable prefrontal headband with an embedded array of 4 photodetectors and 4 micro‐LEDs to form a fiberless functional near‐infrared spectroscopy (fNIRS) system measuring the change in oxyhemoglobin and deoxyhemoglobin concentrations, thus indirectly reflecting the cognitive functions of the brain and constructing BCI. The utilization of dual short‐channel measurements and long‐short channel multiplexing in the fNIRS system enables all 10 optodes to contribute to both long‐ and short‐channel acquisitions, achieving a long‐short‐channel optode proportion of 100% with improvement in removal of superficial physiological noise. The average signal‐to‐noise ratio of long channels reaches 64 dB, and the minimum dynamic optical range of short channels is 122 dB due to the time‐division measurement scheme for two types of channels, demonstrating the feasibility of the proposed channel configuration. In simulated driving tasks, the headband captured discriminable physiological features to distinguish mental‐fatigue states, indicating its potential for real‐world BCI applications.

Background/Objectives: Mental fatigue during driving can arise under different task conditions and typically progresses from mild to severe states. Active fatigue is usually linked to cognitively demanding driving, whereas passive fatigue is associated with prolonged monotonous driving. However, studies on multilevel mental fatigue remain scarce, and direct comparisons of prefrontal multimodal physiological responses to active and passive fatigue are still limited. The objective of this study is to characterize and compare the prefrontal multimodal physiological signatures across three fatigue levels under two simulated driving paradigms designed to induce active and passive fatigue. Methods: Eleven healthy participants completed two simulated driving tasks designed to induce active and passive fatigue. Physiological data were recorded using a self-developed prefrontal EEG-fNIRS system, and pulse-related signals were derived from the hemodynamic measurements. Based on subjective and objective indicators, fatigue was classified into non-fatigue (NonF), moderate fatigue (ModF), and severe fatigue (SevF). Results: In the active-fatigue-inducing paradigm, significant changes in prefrontal EEG and hemodynamic already emerged from NonF to ModF; for example, the EEG β/(θ + α) power ratio increased from 0.973 to 1.157 (p < 0.001) and the normalized mean deoxyhemoglobin feature increased from −0.06 to 0.09 (p < 0.001). In the passive-fatigue-inducing paradigm, EEG changes became prominent mainly from ModF to SevF, with β/(θ + α) power ratio decreasing from 0.806 to 0.761 (p < 0.05). Pulse rate variability showed increasing trends in both paradigms. Conclusions: These findings suggest that the two simulated driving paradigms were associated with distinct prefrontal electrophysiological, hemodynamic, and autonomic evolution patterns across three fatigue levels, supporting graded fatigue assessment and multimodal fatigue monitoring in driving.