神经可塑性和环境噪声干扰导致脑神经信号具有时变性、非稳性等特点，当前脑机接口系统中大多采用静态解码模型，对脑信号的时变性无法准确建模，导致系统不能长时间稳定工作，极大限制了脑机接口系统的应用。通过对脑信号时变性成因的深刻剖析，本项目提出多模型动态自适应融合的解决思路，通过多模型间的协同工作实现对脑信号变化的自适应，使不同模型在不同脑电模式下动态融合，获得联合最优的结果；同时针对脑信号本身特点，将多模型融合框架与演化计算、类脑计算模型相结合，研究解码模型随脑信号变化的在线可塑性学习和演化方法，以保证模型长期有效。本项目研究的时变脑信号自适应建模与学习方法，能够动态地适应大脑的变化，从而有望建立高可靠性、长期稳定可用的脑机接口系统，具有重要意义。

Brain signals are usually nonstationary due to neuroplasticity and signal recording noises. Most existing brain-machine interface (BMI) systems use static decoders and can not model the nonstationary changes accurately, thus face difficulties in maintaining a stable performance in a long-term, which has become a serious problem in BMI applications. After deeply analyzing the nonstationary properties in brain signals, we propose to use a novel dynamic multiple-model combination approach for robust decoding from nonstationary signals. By dynamically combining the models according to current brain signals, the assembled model can adapt to changes in signals to achieve the optimal estimation. We further extend the multiple model combination framework considering the properties of brain signals, and study online model updating and learning strategies with evolution computing and brain-inspired models, to maintain the effectiveness of models. The proposed method can adaptively adjust itself along with changes in brain signals, to build reliable and stable BMI systems which are capable for long-term use, thus is of great value.