The description of the event-related desynchronization and synchronization phenomena in the mu and beta frequency bands has to a significant extent shaped our understanding of motor-related brain processes. Accordingly, Brain-Computer Interface applications leveraging attempted or imagined movements usually depend on spatially-and bandlimited power changes as the brain markers of interest. Yet, converging neuroscience evidence question the idea that signal power best describes the movementrelated modulation of brain activity. On a single-trial level, beta band activity is characterized by short, transient and heterogeneous events termed bursts rather than sustained oscillations. In a recent study we demonstrated that a beta burst analysis of hand motor imagery binary classification tasks is often superior to beta power in terms of classification score. Here we expand upon this idea proposing a comparable to stateof-the-art algorithm. We confirm our previous results by using convolution kernels extracted from beta bursts. Moreover, we show that these kernels can effectively be used in inter-session transfer learning strategies.