Abstract: The mechanisms of movement related potentials (MRPs) are very complex and variable in forms, which makes it very challenging for feature extraction and data mining of brain electrophysiological signals based on MRPs. The purpose of this paper is to apply a variety of machine learning and semantic paradigm models to the data mining of brain electrophysiological signals to meet the above challenges. We used a variety of machine learning algorithms and signal processing methods for analysis and experimental comparison, and presented the best models corresponding to different scenarios and goals; in order to seamlessly connect the three large-span areas of fuzzy electrophysiological signals, deep learning techniques compatible with various heterogeneous signals and explicit semantics models, we had implemented a semantic paradigm framework that used brain electrophysiological signals data as research objects. We had endowed complex signals with grammatical, syntactic and semantic connotations, and constructed semantic interpretations for deep neural networks. Through this paradigm framework, we can identify the specific semantic information blocks in brain electrophysiological signals and semantic combinations between these information blocks, and automatically learn efficient filters to achieve the effect of high accuracy, high transmission rate and high adaptability.

Key words: brain computer interface (BCI）； electroencephalography(EEG）； syntax, paradigm； machine learning； deep learnings； syntax tree； decision tree