Markov chain-based computational model to assess user skills in sequential motor imagery tasks

Researchers face challenges in accurately measuring individual skills to generate discernible patterns during motor imagery (MI). Despite this, most studies focus on simple tasks, limiting knowledge of the effects on more complex sequential motor activities. This study proposes and evaluates a computational method based on state clustering and Markov chains to assess user skills during sequential MI tasks, such as object manipulation. The method is evaluated using two Markov-derived metrics-taskDistinct and relativeTaskInconsistency-which capture, respectively, inter-task pattern separability and consistency of cortical states. A dataset from 30 healthy participants who had to sequentially imagine the manipulation of an object was used, where the cup's locations were varied across four positions (right, left, up, and down). The analyses performed determine whether the extracted cortical pattern states and their transitions, using the proposed method, reflected the number and structure of sequential actions involved in the imagined task, and also whether the Markov-derived metrics correlated with conventional classification metrics. Results showed that the number of states during training matched the number of imagined actions, while during testing, slightly fewer states were recognized. However, the transitions preserved the expected ascending order of the task. Correlations between Markov-based and conventional metrics varied by task, highlighting the task-dependent nature of the associations, where significant and non-significant correlations were observed. Notably, the right-cup MI exhibited predominantly significant correlations. In conclusion, the proposed method successfully captures features of sequential MI performance and provides complementary information about user skills, beyond what classification metrics alone reveals.