  Brain Computer Interface (BCI) can help patients of neuromuscular diseases
restore parts of the movement and communication abilities that they have lost.
Most of BCIs rely on mapping brain activities to device instructions, but
limited number of brain activities decides the limited abilities of BCIs. To
deal with the problem of limited ablility of BCI, this paper verified the
feasibility of constructing BCI based on decoding imagined speech
electroencephalography (EEG). As sentences decoded from EEG can have rich
meanings, BCIs based on EEG decoding can achieve numerous control instructions.
By combining a modified EEG feature extraction mehtod with connectionist
temporal classification (CTC), this paper simulated decoding imagined speech
EEG using synthetic EEG data without help of speech signal. The performance of
decoding model over synthetic data to a certain extent demonstrated the
feasibility of constructing BCI based on imagined speech brain signal.
