With advances in artificial intelligence (AI)-based algorithms, gesture recognition accuracy from sEMG signals has continued to increase. Spatiotemporal multichannel-sEMG signals substantially increase the quantity and reliability of the data for any type of study. Here, we report an array of bipolar stretchable sEMG electrodes with a self-attention-based graph neural network to recognize gestures with high accuracy. The array is designed to spatially cover the skeletal muscles to acquire the regional sampling data of EMG activity from 18 different gestures. The system can differentiate individual static and dynamic gestures with ~97% accuracy when training a single trial per gesture. Moreover, a sticky patchwork of holes adhered to an array sensor enables skin-like attributes such as stretchability and water vapor permeability and aids in delivering stable EMG signals. In addition, the recognition accuracy (~95%) remained unchanged even after long-term testing for over 72 h and being reused more than 10 times.