侵入式脑机接口应用：记忆的解码与调控
Invasive brain-computer interface applications: Decoding and modulation of memory
以记忆障碍为典型症状的阿尔兹海默症和创伤后应激障碍等疾病的治疗是脑机接口研究的关键方向。本文聚焦侵入式脑机接口在情景记忆的空间与情绪信息处理中的应用，重点阐述如何基于人脑深部脑区局部场电位信号，结合机器学习算法，实现对运动状态、环境边界、空间位置及情绪效价等多维度记忆信息的精准解析。基于上述神经特征的调控技术可以实现记忆和情绪的靶向干预。当前技术瓶颈包括个体差异、电极稳定性不足及自适应算法局限。未来需融合动态网络模型与柔性电极技术，推动临床个性化闭环干预范式的发展。
神经病学、精神病学生物科学研究方法、生物科学研究技术计算技术、计算机技术
脑机接口侵入式电极神经调控闭环刺激
田柳青,陈彦霖,林美玲,陈栋,王亮.侵入式脑机接口应用：记忆的解码与调控[EB/OL].(2025-09-24)[2025-09-29].https://chinaxiv.org/abs/202509.00181.点此复制
Alzheimers disease and post-traumatic stress disorder (PTSD), characterized by severe memory deficits, constitute major therapeutic targets for braincomputer interface (BCI) research. We explores the application of invasive BCIs in contextual memory modulation, with a focus on the encoding and decoding of spatial and affective components. By leveraging local field potentials (LFPs) from deep brain structures in conjunction with machine learning algorithms, this approach enables high-resolution decoding of locomotor dynamics, environmental boundaries, spatial localization, and emotional valence. These neural signatures serve as targets for precise neuromodulation strategies aimed at restoring memory functions and regulating affective states. Nonetheless, significant challenges remain, including inter-subject variability, long-term electrode instability, and the limited adaptability of decoding algorithms. Future progress will require the integration of flexible neurointerfaces and dynamic network modeling to advance personalized closed-loop intervention strategies in clinical neuroscience.
brain-computer interfaces (BCIs)invasive neural electrodesneuromodulationclosed-loop stimulation
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