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Brain Computer Interface (BCI) can help patients of neuromuscular diseasesrestore parts of the movement and communication abilities that they have lost.Most of BCIs rely on mapping brain activities to device instructions, butlimited number of brain activities decides the limited abilities of BCIs. Todeal with the problem of limited ablility of BCI, this paper verified thefeasibility of constructing BCI based on decoding imagined speechelectroencephalography (EEG). As sentences decoded from EEG can have richmeanings, BCIs based on EEG decoding can achieve numerous control instructions.By combining a modified EEG feature extraction mehtod with connectionisttemporal classification (CTC), this paper simulated decoding imagined speechEEG using synthetic EEG data without help of speech signal. The performance ofdecoding model over synthetic data to a certain extent demonstrated thefeasibility of constructing BCI based on imagined speech brain signal.

Simulation Experiment of BCI Based on Imagined Speech EEG Decoding