A new study proposes a hybrid Markov chain-spatial statistical (MCSS) machine learning framework for classifying motor imagery EEG, reporting classification accuracy above 98% for every subject on BCI Competition III datasets IVa and IVb when paired with a support vector machine. Because the method discretizes signals into symbolic states and works from transition probability matrices rather than raw traces, the original neural waveforms are hard to reconstruct; the authors report that membership inference attacks stayed near chance level and that feature inversion attacks produced low reconstruction similarity.
