Brain-machine interfaces (BMIs) are useful tools for controlling assistive devices. One current approach in this field is continuous trajectory reconstruction (CTR), which decodes variables from electroencephalographic (EEG) signals. In this study, the CTR approach was applied to estimate kinetic variables from lower limb joints during pre-gait movements. The methodology consisted of a multi-layer perceptron capable of detecting patterns that represent the relation between EEG and torque signals acquired during motion tasks. The study also searched for an optimal subset of EEG channels based on the resulting patterns. A group of volunteers was recruited to execute pre-gait movements with both lower limbs to test the methodology. Results show that decodings of extracted patterns were successful in terms of the selected performance metrics; that is, the coefficient of determination, correlation coefficient, and signal-to-noise ratio. These metrics suggest that decodings for the right lower limb are better than for the left one, and that the most frequent electrodes in the optimal subsets for each task and joint show a tendency to lateralize. The results obtained suggest that this machine learning scheme could be used for future studies of the CTR of kinetic variables.