Brain–computer interfaces (BCIs) enable control of assistive devices in individuals with severe motor impairments. A limitation of BCIs that has hindered real-world adoption is poor long-term reliability and lengthy daily recalibration times. To develop methods that allow stable performance without recalibration, we used a 128-channel chronic electrocorticography (ECoG) implant in a paralyzed individual, which allowed stable monitoring of signals. We show that long-term closed-loop decoder adaptation, in which decoder weights are carried across sessions over multiple days, results in consolidation of a neural map and ‘plug-and-play’ control. In contrast, daily reinitialization led to degradation of performance with variable relearning. Consolidation also allowed the addition of control features over days, that is, long-term stacking of dimensions. Our results offer an approach for reliable, stable BCI control by leveraging the stability of ECoG interfaces and neural plasticity.

脑机接口（BCI）可以控制严重运动障碍患者的辅助设备。BCI的局限性在于长期可靠性差和每天重新校准时间长，这阻碍了现实世界的采用。为了开发无需重新校准即可实现稳定性能的方法，我们在瘫痪的个体中使用了128通道的慢性皮质电图（ECoG）植入物，从而可以稳定地监视信号。我们展示了长期的闭环解码器适应性，其中解码器权重在几天内跨会话进行，导致了神经图和“即插即用”控制的合并。相比之下，每天重新初始化会导致性能随着可重新学习而降低。整合还允许在几天之内添加控制功能，即长期堆叠尺寸。