针对共空间模式算法在处理低通道数和多模式想象动作的脑电信号时，无法获得足够的空间分布信息而导致分类准确率低的问题，提出一种以事件相关频谱扰动为基础的时频域和空间域的特征提取方法。首先根据肢体想象动作在运动感觉皮层区域呈现独立功能映射区的特点，提取特定导联下差异显著的事件相关时频特征信息，并将其与特定导联的空域特征信息融合，最后通过参数优化后的支持向量机来识别不同类别的肢体想象动作。实验结果对比显示，融合特征在多模式想象动作中的识别性能较单一特征有显著提高，不仅能够获得更全面的脑电特征信息，还有效地降低了多通道数的需求，其平均分类准确率达到93.1%。

Aiming at the problem that the common spatial pattern algorithm cannot obtain sufficient spatial distribution information when processing EEG signals with low channel counts and multi-modal imaginative actions, resulting in low classification accuracy, proposing a feature extraction method in time-frequency and spatial domains based on event-related spectral perturbations. Firstly, according to the characteristics of the independent functional mapping area of the limb imagination in the motor sensory cortex region, the event-related time-frequency feature information with significant differences under specific leads was extracted and fused with the spatial feature information of specific leads. Finally, the parameter-optimized support vector machine was used to identify different classes of limb-imagined actions. Comparison of the experimental results shows that the recognition performance of fused features in multi-modal imagined actions is significantly improved over single features. It can not only obtain more comprehensive EEG feature information, but also effectively reduce the demand for multi-channel number, and its average classification accuracy reaches 93.1%.