Conventional passive lower limb rehabilitation is suboptimal since the brain is not actively involved in the training. An autonomous motor imagery brain-computer interface (MI-BCI) could potentially improve rehabilitation outcomes. However, motor cortex regions associated with the individual feet are anatomically close to each other. This presents a difficulty in distinguishing the left and right foot MI during rehabilitation therapy. To overcome this difficulty, we extracted functional connectivity to measure the global cortical network via electroencephalography (EEG) signals. Fourteen spatial connections (P3-Fp1, P3-F3, P3-F7, P3-C3, T5-F7, T5-C3, T5-T3, Fp2-T5, Fp2-P3, T6-Fp2, T6-T4, Cz-Fp1, Cz-F7 and Fp2-F7) found across twelve subjects significantly differed between the left and right foot MI, evidencing nonlocalized brain activity during MI. Foot MI were distinguished using machine learning algorithms in terms of the time- and frequency-domain connectivities extracted from Pearson's correlation, multivariate autoregression (MVAR), bandpass correlation, and partial directed coherence (PDC) models. The results showed that connectivity extracted by pairwise Pearson's correlation could be distinguished with 86.26 ± 9.95%, while in the frequency-domain, the gamma band presented the best classification accuracy of 73.55 ± 17.11%. We attempted to simulate asynchronous real-time classification paradigms in order to evaluate the classification performance of connectivity features compared to common spatial pattern (CSP) and band power (BP). The results indicate correlation-connectivity has the best outcome, attaining an accuracy of 80.75 ± 9.51% in asynchronous classification.