This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18μ m CMOS technology, occupying a silicon area of 3.7 mm ² . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

This paper proposed a wearable smart sEMG recorder integrated gradient boosting decision tree (GBDT) based hand gesture recognition. A hydrogen-silica gel based flexible surface electrode band is used as the tissue interface. The sEMG signal is collected using a neural signal acquisition analog front end (AFE) chip. A quantitative analysis method is proposed to balance the algorithm complexity and recognition accuracy. A parallel GBDT implementation is proposed featuring a low latency. The proposed GBDT based neural signal processing unit (NSPU) is implemented on an FPGA near the AFE. A RF module is used for wireless communication. A hand gesture set including 12 gestures is designed for human-computer interaction. Experimental results show an overall hand gesture recognition accuracy of 91%.

As nitric oxide (NO) plays significant roles in a variety of physiological processes, the capability for real-time and accurate detection of NO in live organisms is in great demand. Traditional assessments of NO rely on indirect colorimetric techniques or electrochemical sensors that often comprise rigid constituent materials and can hardly satisfy sensitivity and spatial resolution simultaneously. Here, we report a flexible and highly sensitive biosensor based on organic electrochemical transistors (OECTs) capable of continuous and wireless detection of NO in biological systems. By modifying the geometry of the active channel and the gate electrodes of OECTs, devices achieve optimum signal amplification of NO. The sensor exhibits a low response limit, a wide linear range, high sensitivity, and excellent selectivity, with a miniaturized active sensing region compared with a conventional electrochemical sensor. The device demonstrates continuous detection of the nanomolar range of NO in cultured cells for hours without significant signal drift. Real-time and wireless measurement of NO is accomplished for 8 d in the articular cavity of New Zealand White rabbits with anterior cruciate ligament (ACL) rupture injuries. The observed high level of NO is associated with the onset of osteoarthritis (OA) at the later stage. The proposed device platform could provide critical information for the early diagnosis of chronic diseases and timely medical intervention to optimize therapeutic efficacy.

Optoelectronic neural interfaces can leverage the photovoltaic effect to convert light into electrical current, inducing charge redistribution and enabling nerve stimulation. This method offers a non-genetic and remote approach for neuromodulation. Developing biodegradable and efficient optoelectronic neural interfaces is important for achieving transdermal stimulation while minimizing infection risks associated with device retrieval, thereby maximizing therapeutic outcomes. We propose a biodegradable, flexible, and miniaturized silicon-based neural interface capable of transdermal optoelectronic stimulation for neural modulation and nerve regeneration. Enhancing the device interface with thin-film molybdenum significantly improves the efficacy of neural stimulation. Our study demonstrates successful activation of the sciatic nerve in rodents and the facial nerve in rabbits. Moreover, transdermal optoelectronic stimulation accelerates the functional recovery of injured facial nerves.