A design of EEGNet-based inference processor for pattern recognition of EEG using FPGA

In recent years, brain-machine interface (BMI) is attracting attention. BMI is a technology that enables machine operation using biological signals such as EEG. For further advancement of BMI technology, there is a need for advanced BMI devices. Therefore, the purpose of this study is development of BMI hardware specialized for handling EEG as an interface for human adaptive mechatronics (HAM) that know human's state and operate according to the state. As one of the examinations, we are constructing a pattern recognition processor for EEG in real time on Field Programmable Gate Array (FPGA), which is an LSI that can reconfigure the processor. This paper reports on the designed EEGNet processor and the result of logic circuit simulation and implementation.