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Decoding inner speech from the brain signal via hybridisation of fMRI and EEGdata is explored to investigate the performance benefits over unimodal models.Two different bimodal fusion approaches are examined: concatenation ofprobability vectors output from unimodal fMRI and EEG machine learning models,and data fusion with feature engineering. Same task inner speech data arerecorded from four participants, and different processing strategies arecompared and contrasted to previously-employed hybridisation methods. Dataacross participants are discovered to encode different underlying structures,which results in varying decoding performances between subject-dependent fusionmodels. Decoding performance is demonstrated as improved when pursuing bimodalfMRI-EEG fusion strategies, if the data show underlying structure.

Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models