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Motor imagery-based brain-computer interface (MI-BCI) applications in stroke rehabilitation aim to match brain activity with real-time feedback, thereby establishing closed-loop neural pathways and providing a basis for evaluating patients' neuroplasticity changes. Thus, constructing EEG datasets under MI paradigms is crucial for optimizing MI-BCI systems and understanding the neural rehabilitation process. However, the current limitations of single MI paradigms and the lack of relevant EEG datasets may restrict the accurate interpretation and effective application in stroke rehabilitation. This study collected EEG data from 24 stroke patients during MI tasks, including a novel "sixth finger" MI and an affected-hand MI paradigm. The dataset comprehensively covers the complete longitudinal stages of stroke rehabilitation: pre-training, post-training, and follow-up periods. The data materials include: (1) raw EEG data, (2) preprocessed data, and (3) patient clinical information. Preliminary analysis using classical machine learning algorithms (CSP + SVM and CSP + LDA) demonstrated an average classification accuracy between the two MI paradigms maintained at approximately 85%~86%. We anticipate that this dataset will facilitate research on MI-BCI paradigms and neuroplasticity for stroke, and contribute to the development of high-efficiency MI-BCI systems in the field of stroke rehabilitation.