On Decoding Brain Electrocorticography Data for Volitional Movement Intention Prediction: Theory and On-Chip Implementation

Brain-computer interface (BCI) has recently received an unprecedented level of consideration and appreciation in medical applications, such as augmentation and reparation of human cognitive or sensorimotor activities. Brain signals such as electroencephalogram (EEG) or electrocorticography (ECoG) can be used to generate stimuli or control device though decoding, translating, and actuating; this communication between the brain and computer is known as BCI. Moreover, signals from the sensors can be transmitted to a person's brain enabling them to see, hear, or feel from sensory inputs. This two-way communication is referred as bidirectional brain-computer interface (BBCI). In this work, we propose a field-programmable gate array (FPGA)-based on-chip implementation of two important data processing blocks in BCI systems, namely, feature extraction and decoding. Experimental results showed that our proposed architecture can achieve high prediction accuracy for decoding volitional movement intentions from ECoG data.