Abstract
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.
Abstract (translated)
皮质内脑机接口（BCIs）在利用跨记录时段的聚合数据训练时，能够以高精度从神经活动中解码语音。然而，在实际部署中，模型必须在无标签数据的新时段中实现泛化，且由于跨时段非平稳性（如电极移位、神经元更替以及用户策略变化），性能常会下降。本文提出ALIGN，一种基于多领域对抗神经网络、用于半监督跨时段自适应的会话不变学习框架。ALIGN联合训练一个特征编码器、一个音素分类器以及一个在潜在表征上操作的领域分类器。通过对抗优化，该编码器被鼓励保留任务相关信息，同时抑制时段特异性线索。我们在皮质内语音解码任务上评估ALIGN，发现其对未见过时段的泛化能力持续优于基线方法，在音素错误率和词错误率两方面均有提升。这些结果表明，对抗性领域对齐是缓解时段级分布偏移、实现稳健纵向BCI解码的有效方法。
URL
https://arxiv.org/abs/2603.18299
