In vivo Performance Evaluation of Implantable Wireless Neural Signal Transmission System for Brain Machine Interface

Lee, Hyun Joo; Nyamdorj, Selenge; Shin, Hyung Cheul; Ahn, Jae Mok

Abstract

A brain-machine interface (BMI) has recently been introduced to research a reliable control of machine from the brain information processing through single neural spikes in motor brain areas for paralyzed individuals. Small, wireless, and implantable BMI system should be developed to decode movement information for classifications of neural activities in the brain. In this paper, we have developed a totally implantable wireless neural signal transmission system (TiWiNets) combined with advanced digital signal processing capable of implementing a high performance BMI system. It consisted of a preamplifier with only 2 operational amplifiers (op-amps) for each channel, wireless bluetooth module (BM), a Labview-based monitor program, and 16 bit-RISC microcontroller. Digital finite impulse response (FIR) band-pass filter based on windowed sinc method was designed to transmit neural signals corresponding to the frequency range of 400 Hz to 1.5 kHz via wireless BM, measuring over -48 dB attenuated in the other frequencies. Less than +/-2% error by inputting a sine wave at pass-band frequencies for FIR algorithm test was obtained between simulated and measured FIR results. Because of the powerful digital FIR design, the total dimension could be dramatically reduced to 23x27x4 mm including wireless BM except for battery. The power isolation was built to avoid the effect of radio-frequency interference on the system as well as to protect brain cells from system damage due to excessive power dissipation or external electric leakage. In vivo performance was evaluated in terms of long-term stability and FIR algorithm for 4 months after implantation. Four TiWiNets were implanted into experimental animals' brains, and single neural signals were recorded and analyzed in real time successfully except for one due to silicon- coated problem. They could control remote target machine by classify neural spike trains based on decoding technology. Thus, we concluded that our study could fulfill in vivo needs to study various single neuron-movement relationships in diverse fields of BMI.