Intracortical brain-computer interfaces (BCIs) can decode speech from neural activity with high accuracy when trained on data pooled across recording sessions. In realistic deployment, however, models must generalize to new sessions without labeled data, and performance often degrades due to cross-session nonstationarities (e.g., electrode shifts, neural turnover, and changes in user strategy). In this paper, we propose ALIGN, a session-invariant learning framework based on multi-domain adversarial neural networks for semi-supervised cross-session adaptation. ALIGN trains a feature encoder jointly with a phoneme classifier and a domain classifier operating on the latent representation. Through adversarial optimization, the encoder is encouraged to preserve task-relevant information while suppressing session-specific cues. We evaluate ALIGN on intracortical speech decoding and find that it generalizes consistently better to previously unseen sessions, improving both phoneme error rate and word error rate relative to baselines. These results indicate that adversarial domain alignment is an effective approach for mitigating session-level distribution shift and enabling robust longitudinal BCI decoding.

翻译：皮层内脑机接口在跨记录会话的联合训练数据上，能以高精度从神经活动解码语音信号。然而在实际部署中，模型必须在无标注数据的情况下泛化至新会话，而跨会话非平稳性（如电极漂移、神经元更替及用户策略变化）常导致性能下降。本文提出ALIGN——基于多域对抗神经网络的会话不变学习框架，用于半监督跨会话自适应。该框架联合训练特征编码器、音素分类器及作用于隐层表征的域分类器。通过对抗优化，编码器被引导保留任务相关信息的同时抑制会话特异性线索。我们在皮层内语音解码任务上评估ALIGN，发现其对未见过会话的泛化能力持续优于基线方法，音素错误率和词错误率均得到改善。结果表明，对抗性域对齐是缓解会话级分布偏移、实现稳健纵向脑机接口解码的有效途径。