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Decoding imagined speech from human brain signals is a challenging andimportant issue that may enable human communication via brain signals. Whileimagined speech can be the paradigm for silent communication via brain signals,it is always hard to collect enough stable data to train the decoding model.Meanwhile, spoken speech data is relatively easy and to obtain, implying thesignificance of utilizing spoken speech brain signals to decode imaginedspeech. In this paper, we performed a preliminary analysis to find out whetherif it would be possible to utilize spoken speech electroencephalography data todecode imagined speech, by simply applying the pre-trained model trained withspoken speech brain signals to decode imagined speech. While the classificationperformance of imagined speech data solely used to train and validation was30.5 %, the transferred performance of spoken speech based classifier toimagined speech data displayed average accuracy of 26.8 % which did not havestatistically significant difference compared to the imagined speech basedclassifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, wecompared the result with the visual imagery dataset, which would naturally beless related to spoken speech compared to the imagined speech. As a result,visual imagery have shown solely trained performance of 31.8 % and transferredperformance of 26.3 % which had shown statistically significant differencebetween each other (p = 0.022, chi-square = 7.64). Our results imply thepotential of applying spoken speech to decode imagined speech, as well as theirunderlying common features.

Towards Neural Decoding of Imagined Speech based on Spoken Speech