Closed-loop neurofeedback is a growing area of research and development for novel therapies to treat brain disorders. A neurofeedback device can detect disease symptoms (such as motor tremors or seizures) in real time from electroencephalogram (EEG) signals, and respond by rapidly delivering neurofeedback stimulation that relieves these symptoms. Conventional EEG processing algorithms rely on acausal filters, which impose delays that can exceed the short feedback latency required for closed-loop stimulation. In this paper, we first introduce a method for causal filtering using long short-term memory (LSTM) networks, which radically reduces the filtering latency. We then propose a reconfigurable architecture that supports time-division multiplexing of LSTM inference engines on a prototype neurofeedback device. We implemented a 128-channel EEG signal processing design on a Zynq-7030 device, and demonstrated its feasibility. Then, we further scaled up the design onto Zynq-7045 and Virtex-690t devices to achieve high performance and energy efficient implementations for massively parallel brain signal processing. We evaluated the performance against optimized implementations on CPU and GPU at the same CMOS technology node. Experiment results show that the Virtex-690t can achieve 1.32x and 11x speed-up against the K40c GPU and the multi-thread Xeon E5-2860 CPU, respectively, while FPGA achieves 6.1x and 26.6x energy efficiency compared to the GPU and CPU.

Neurofeedback device measures brain wave and generates feedback signal in real time and can be employed as treatments for various neurological diseases. Such devices require high energy efficiency because they need to be worn or surgically implanted into patients and support long battery life time. In this paper, we propose CLINK, a compact LSTM inference kernel, to achieve high energy efficient EEG signal processing for neurofeedback devices. The LSTM kernel can approximate conventional filtering functions while saving 84% computational operations. Based on this method, we propose energy efficient customizable circuits for realizing CLINK function. We demonstrated a 128-channel EEG processing engine on Zynq-7030 with 0.8 W, and the scaled up 2048-channel evaluation on Virtex-VU9P shows that our design can achieve 215x and 7.9x energy efficiency compared to highly optimized implementations on E5-2620 CPU and K80 GPU, respectively. We carried out the CLINK design in a 15-nm technology, and synthesis results show that it can achieve 272.8 pJ/inference energy efficiency, which further outperforms our design on the Virtex-VU9P by 99x.