A hardware efficient intra-cortical neural decoding approach based on spike train temporal information

Motor intention decoding is one of the most challenging issues in brain machine interface (BMI). Despite several important studies on accurate algorithms, the decoding stage is still processed on a computer, which makes the solution impractical for implantable applications due to its size and power consumption. This study aimed to provide an appropriate real-time decoding approach for implantable BMIs by proposing an agile decoding algorithm with a new input model and implementing efficient hardware. This method, unlike common ones employed firing rate as input, used a new input space based on spike train temporal information. The proposed approach was evaluated based on a real dataset recorded from frontal eye field (FEF) of two male rhesus monkeys with eight possible angles as the output space and presented a decoding accuracy of 62%. Furthermore, a hardware architecture was designed as an application-specific integrated circuit (ASIC) chip for real-time neural decoding based on the proposed algorithm. The designed chip was implemented in the standard complementary metal-oxide-semiconductor (CMOS) 180 nm technology, occupied an area of 4.15 mm2, and consumed 28.58 μW @1.8 V power supply.