【摘要】 癫痫是常见的慢性神经系统疾病。经充分、合理且可耐受的两种抗癫痫发作药物（anti-seizure medications,ASMs）方案治疗（单药或联合）仍未实现持续无发作者，即为药物难治性癫痫（drug-resistant epilepsy,DRE）。对于该人群，外科切除与神经调控等治疗方案可有效降低发作负担，但疗效受制于致痫灶定位不确定、刺激参数与靶点选择的个体差异以及手术风险。脑机接口（brain–computer interface, BCI）作为一种新兴技术，能够提供一套实时监测—状态解码—决策—干预执行的闭环框架，使治疗从固定参数的开环方案转向基于生物标志物的动态个体化调控，为DRE的精准个体化治疗提供了新思路。本文将综述癫痫BCI的信号获取与解码策略、闭环干预模式及其临床转化挑战。更多还原

【Abstract】 Epilepsy is a common chronic neurological disorder. Drug-resistant epilepsy(DRE) is defined as failure to achieve sustained seizure freedom after adequate trials of two appropriately chosen and tolerated anti-seizure medications(ASMs) regimens(as monotherapies or in combination). In this population, surgical resection and neuromodulation can effectively reduce seizure burden; however, their effectiveness is limited by uncertainty in epileptogenic zone localization, interindividual variability in stimulation targets and parameters, and procedure-related risks. As an emerging technology, brain–computer interface(BCI) offer a closed-loop framework of real-time monitoring,state decoding, decision-making, and intervention delivery, shifting therapy from fixed-parameter open-loop approaches to biomarker-driven, dynamically personalized modulation and providing new avenues for precision, individualized treatment of DRE. This review summarizes signal acquisition and decoding strategies for epilepsy BCI, closed-loop intervention paradigms, and key challenges in clinical translation.更多还原