Acute Effects of Portable Dry‐EEG Neurofeedback on Classical Chinese Learning: A Three‐Arm Repeated‐Measures Study

OBJECTIVE: Dry-electrode electroencephalography (dry-EEG) systems offer promising opportunities for real-time neurofeedback in naturalistic educational settings, yet their effectiveness in supporting complex language learning remains underexplored. This study investigated the acute effects of portable dry-EEG neurofeedback on students' cognitive performance and attentional states during classical Chinese learning, using a repeated-measures design to compare neurofeedback, sham feedback, and device control conditions. METHODS: A total of 20 undergraduate participants completed three sessions involving a customized semantic disambiguation task after passive reading. EEG signals were acquired using a dry-sensor OpenBCI system from four frontal sites (Fp1, Fp2, F3, F4). Real-time attention indices were computed based on the beta/(alpha+theta) ratio and fed back visually in the neurofeedback condition. Cognitive outcomes included comprehension test scores and semantic conflict resolution performance (RT, accuracy, cognitive load). RESULTS: Compared to sham and control conditions, neurofeedback significantly improved comprehension accuracy (p < 0.001), reduced reaction times in the interference task (p < 0.05), and lowered subjective cognitive load (p = 0.002). EEG indices of attention were significantly elevated during neurofeedback (p < 0.001) and positively correlated with behavioral gains (r = 0.63, p < 0.05). CONCLUSIONS: Portable dry-electrode EEG systems can reliably support real-time neurofeedback to enhance attention and cognitive control in complex language learning contexts. This study provides empirical validation for deploying dry-EEG sensors in adaptive educational technologies and contributes to the broader integration of wearable brain-computer interfaces in cognitive augmentation applications.