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.