Scalable integration of heterogeneous active surface electromyography electrode arrays for neural interfaces

Abstract Surface electromyogram (sEMG) signals are valuable in healthcare and human-machine interaction. However, sEMG signals are inherently weak and unstable bioelectrical signals, rendering them highly susceptible to perturbations from various external factors. In this work, we firstly proposed utilizing the industrially producible Gen-4.5 heterogeneous integration technology to design an active 16-channel microelectrode array (MEA) based on amorphous indium–gallium–zinc oxide thin-film transistors (a-IGZO TFTs) capable of capturing and decoding sEMG signals. The a-IGZO TFTs demonstrate exceptional stability under bias (±20 V), temperature (200 °C), and bending (6 mm, 30 000 cycles), with a threshold voltage shift of less than 0.1 V and a standard deviation under 0.07 V for 100 randomly selected devices. Our state-of-the-art 16-channel active MEAs can collect sEMG signals from various hand gestures and analysis of motor unit action potential trains, expanding possibilities for human-machine interaction and electronic healthcare applications. The signal-to-noise ratio of sEMG signals reaches 85 dB, enabling a high average hand gesture recognition accuracy of 96.2%. This work highlights the potential of the scalable sEMG arrays with exceptional stability for multi-channel sEMG signal acquisition, representing a significant advancement in wearable health monitoring and interactive systems.