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Translation of imagined speech electroencephalogram(EEG) into humanunderstandable commands greatly facilitates the design of naturalistic braincomputer interfaces. To achieve improved imagined speech unit classification,this work aims to profit from the parallel information contained inmulti-phasal EEG data recorded while speaking, imagining and performingarticulatory movements corresponding to specific speech units. A bi-phasecommon representation learning module using neural networks is designed tomodel the correlation and reproducibility between an analysis phase and asupport phase. The trained Correlation Network is then employed to extractdiscriminative features of the analysis phase. These features are furtherclassified into five binary phonological categories using machine learningmodels such as Gaussian mixture based hidden Markov model and deep neuralnetworks. The proposed approach further handles the non-availability ofmulti-phasal data during decoding. Topographic visualizations along withresult-based inferences suggest that the multi-phasal correlation modellingapproach proposed in the paper enhances imagined-speech EEG recognitionperformance.

Correlation based Multi-phasal models for improved imagined speech EEG recognition