  Decoding imagined speech from human brain signals is a challenging and
important issue that may enable human communication via brain signals. While
imagined 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 the
significance of utilizing spoken speech brain signals to decode imagined
speech. In this paper, we performed a preliminary analysis to find out whether
if it would be possible to utilize spoken speech electroencephalography data to
decode imagined speech, by simply applying the pre-trained model trained with
spoken speech brain signals to decode imagined speech. While the classification
performance of imagined speech data solely used to train and validation was
30.5 %, the transferred performance of spoken speech based classifier to
imagined speech data displayed average accuracy of 26.8 % which did not have
statistically significant difference compared to the imagined speech based
classifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, we
compared the result with the visual imagery dataset, which would naturally be
less related to spoken speech compared to the imagined speech. As a result,
visual imagery have shown solely trained performance of 31.8 % and transferred
performance of 26.3 % which had shown statistically significant difference
between each other (p = 0.022, chi-square = 7.64). Our results imply the
potential of applying spoken speech to decode imagined speech, as well as their
underlying common features.
