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