Long-term reliable neural decoding based on flexible implantable microelectronics and machine learning for seizure prediction application

Abstract Neural decoding is useful for understanding brain functions and developing neural interface applications. However, neural interfaces based on rigid electronics often suffer from recording instability due to the foreign body responses caused by their mechanical mismatch with soft tissues, limiting the longitudinal accuracy of neural decoding methods. Herein, it is reported that flexible electronics can be integrated with machine learning algorithms to achieve long-term reliable neural decoding. Wet-spun conductive polymer microfibers showed mechanical robustness and flexibility, low impedance, and chronic biocompatibility, enabling intracerebral neural recordings in epileptic mice at a high signal-to-noise ratio eight weeks after implantation. When the signals recorded by the flexible electrodes were used in machine learning analyses with diverse complex algorithms, they consistently showed higher prediction accuracy for epileptic seizures than stiff metal electrode signals, particularly in the case of using long-term recordings for testing or small-sample datasets for training. A real-time warning system based on the flexible neural electrodes was built that predicted seizures eight minutes in advance with a low false alarm rate. Our work bridges flexible electronics and artificial intelligence for neural decoding applications such as long-term treatment of chronic neurological disorders.