Decoding Movement-Related Cortical Potentials Based on Subject-Dependent and Section-Wise Spectral Filtering
An important challenge in developing a movement-related cortical potential (MRCP)-based brain-machine interface (BMI) is an accurate decoding of the user intention for real-world environments. However, the performance remains insufficient for real-time decoding owing to the endogenous signal characteristics compared to other BMI paradigms. This study aims to enhance the MRCP decoding performance from the perspective of preprocessing techniques (i.e., spectral filtering).
Position-Independent Decoding of Movement Intention for Proportional Myoelectric Interfaces
In this decade, myoelectric interfaces based on pattern recognition have gained considerable attention thanks to their naturalness enabling human intentions to be conveyed to and in control of a machine. However, the high variations of electromyogram signal patterns caused by arm position changes prohibit application to the real world.