Electroencephalogram signal analysis with 1T1R arrays toward high-efficiency brain computer interface

Brain computer interface (BCI) is a promising way for automatic driving and exploring brain functions. As the number of electrodes electroencephalogram (EEG) acquisition continues to grow, signal processing capabilities BCI are facing challenges. Considering bottlenecks Von Neumann architecture, it increasingly difficult traditional digital computing pattern meet requirements EEG in terms power consumption efficiency. Here, we propose 1T1R array-based analysis system which biological likelihood memristor used efficiently analyze signals simulated domain. The identification classification achieved experimentally using array with an average recognition rate 89.83%. support vector machine implemented by crossbar provides 34.4 times improvement efficiency compared complementary metal oxide semiconductor-based classifier. This work new ideas application memristors BCI.