  Translation of imagined speech electroencephalogram(EEG) into human
understandable commands greatly facilitates the design of naturalistic brain
computer interfaces. To achieve improved imagined speech unit classification,
this work aims to profit from the parallel information contained in
multi-phasal EEG data recorded while speaking, imagining and performing
articulatory movements corresponding to specific speech units. A bi-phase
common representation learning module using neural networks is designed to
model the correlation and reproducibility between an analysis phase and a
support phase. The trained Correlation Network is then employed to extract
discriminative features of the analysis phase. These features are further
classified into five binary phonological categories using machine learning
models such as Gaussian mixture based hidden Markov model and deep neural
networks. The proposed approach further handles the non-availability of
multi-phasal data during decoding. Topographic visualizations along with
result-based inferences suggest that the multi-phasal correlation modelling
approach proposed in the paper enhances imagined-speech EEG recognition
performance.
