Towards decoding speech production from single-trial magnetoencephalography (MEG) signals

Patients with locked-in-syndrome (fully paralyzed but aware) struggle in their life and communication. Providing a level of communication offers these patients a chance to resume a meaningful life. Current brain-computer interface (BCI) communication requires users to build words from single letters selected on a screen, which is extremely inefficient. Faster approaches for their speech communication are highly needed. This project investigated the possibility to decode spoken phrases from non-invasive brain activity (MEG) signals. This direct brain-to-text mapping approach may provide a significantly faster communication rate than current BCIs can provide. We used dynamic time warping and Wiener filtering for noise reduction and then Gaussian mixture model and artificial neural network as the decoders. Preliminary results showed the possibility of decoding speech production from non-invasive brain signals. The best phrase classification accuracy was up to 94.54% from single-trial whole-head MEG recordings.