Motor imagery is a classic research paradigm in the field of brain computer interfaces, which aims to study the information transmission and control of external electronic devices solely through brain imagination. The Common spatial paternal algorithm is an indispensable classic feature extraction algorithm in motor imagery research. This algorithm can obtain highly discriminative features by maximizing inter class variance, thereby obtaining models with good classification performance. However, the common spatial paternal algorithm is sensitive to noise and other interferences, and requires as much inter class information as possible, resulting in poor performance in non-invasive brain imaging research. To address this problem, a data processing algorithm based on phase information in frequency domain and trend information in time domain is proposed. The phase residual sequence is constructed using the instantaneous phase sequence and the empirical mode decomposition residual component of electroencephalogram signals. The algorithm maximizes brain neural activity information while eliminating interference from external or other noise, and extracts more discriminative features through common spatial paternal algorithm, ultimately obtaining a classification model with strong recognition and generalization performance. The experimental results show that the proposed method has an average classification accuracy of 88.19% among 52 subjects, which is higher than the original sequence’s 79.67%. At the same time, it exhibits more stable classification performance in motor imagery data of different subjects, proving that the method has good recognition and generalization ability in electroencephalogram-based motor imagery classification.