Enhancing biocompatibility of the brain-machine interface:A review

In vivo implantation of microelectrodes opens the door to studying neural circuits and restoring damaged neural pathways through direct electrical stimulation and recording.Although some neuroprostheses have achieved clinical success,electrode material properties,inflammatory response,and glial scar formation at the electrode-tissue interfaces affect performance and sustainability.Those challenges can be addressed by improving some of the materials’mechanical,physical,chemical,and electrical properties.This paper reviews materials and designs of current microelectrodes and discusses perspectives to advance neuroprosthetics performance.

Electrode materials for brain-machine interface:A review

Brain-machine interface(BMI)is a device that translates neuronal information into commands,which is capable of controlling external software or hardware,such as a computer or robotic arm.In consequence,the electrodes with desirable electrical and mechanical properties for direct interacting between neural tissues and machines serves as the crucial and critical part of BMI technology.Nowadays,the development of material science provides many advanced electrodes for neural stimulating and recording.Particularly,the widespread applications of nanotechnologies have innovatively introduced biocompatible electrode that can have similar characteristics with neural tissue.This paper reviews the existing problems and discusses the latest development of electrode materials for BMI,including conducting polymers,silicon,carbon nanowires,graphene,and hybrid organic-inorganic nanomaterials.In addition,we will inspect at the technical and scientific challenges in the development of neural electrode for a broad application of BMI with focus on the biocompatibility,mechanical mismatch,and electrical performance of electrode materials.

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Systems Neuroengineering: Understanding and Interacting with the Brain

In this paper, we review the current state- of-the-art techniques used for understanding the inner workings of the brain at a systems level. The neural activity that governs our everyday lives involves an intricate coordination of many processes that can be attributed to a variety of brain regions. On the surface, many of these functions can appear to be controlled by specific anatomical structures; however, in reality, numerous dynamic networks within the brain contribute to its function through an interconnected web of neuronal and synaptic pathways. The brain, in its healthy or pathological state, can therefore be best understood by taking a systems-level approach. While numerous neuroengineering technologies exist, we focus here on three major thrusts in the field of systems neuroengineering： neuroimaging, neural interfacing, and neuromodulation. Neuroimaging enables us to delineate the structural and functional organization of the brain, which is key in understanding how the neural system functions in both normal and disease states. Based on such knowledge, devices can be used either to communicate with the neural system, as in neural interface systems, or to modulate brain activity, as in neuromodulation systems. The consideration of these three fields is key to the development and application of neuro-devices. Feedback-based neuro-devices require the ability to sense neural activity （via a neuroimaging modality） through a neural interface （invasive or noninvasive） and ultimately to select a set of stimulation parameters in order to alter neural function via a neuromodulation modality. Systems neuroengineering refers to the use of engineering tools and technologies to image, decode, and modulate the brain in order to comprehend its functions and to repair its dysfunction. Interactions between these fields will help to shape the future of systems neuroengineering--to develop neurotechniques for enhancing the understanding of whole- brain function and dysfunction, and the management of neurological and mental disorders.

Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

Finger movement inference using M1 neural activities

The paper presents the neural decoding result of finger or wrist movements using the primary motor cortex(M1)neural activities prior to its movement.It is well known that the observations of motor commands in brain are in advance before motor movements in the central nerve system.Readiness potential(RP)for electroencephalogram(EEG)has become an important domain of research.Likewise,pre-movement neural responses in M1 primary motor cortex have been observed.The neural activity data before 1 s.were used for neural decoding when the actual movements happened around 1 s.The obtained decoding accuracy in novel method reaches as high as 95% with 30 randomly selected neurons.

Odorant discrimination using functional near-infrared spectroscopy of the main olfactory bulb in rats

We characterize the hemodynamic response changes near-infrared spectroscopy （NIRS） during the presentation of in the main olfactory bulb （MOB） of anesthetized rats with three different odorants： （i） plain air as a reference （Blank）, （ii） 2-heptanone （HEP）, and （iii） isopropylbenzene （Ib）. Odorants generate different changes in the concentrations of oxy- hemoglobin. Our results suggest that NIRS technology might be useful in discriminating various odorants in a non-invasive manner using animals with a superb olfactory system.

Neural decoding based on probabilistic neural network

Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decoder and the modified PNN (MPNN) decoder. In the ex-periment,rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity,and pressure was recorded by a pressure sensor synchronously. After training,the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their per-formances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder,with a CC of 0.8657 and an MSE of 0.2563,outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance,indicating that the MPNN decoder can handle different tasks in BMI system,including the detection of movement states and estimation of continuous kinematic parameters.

From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control

In contrast to traditional representational perspectives in which the motor cortex is involved in motor control via neuronal preferenceforkinetics andkinematics,a dynamical system perspective emerging in the last decade views the motor cortex as a dynamical machine that generates motor commands by autonomous temporal evolution.In this review,we first look back at the history of the representational and dynamical perspectives and discuss their explanatory power and controversy from both empirical and computational points of view.Here,we aim to reconcile the above perspectives,and evaluate their theoretical impact,future direction,and potential applications in brain-machine interfaces.

Nanoscience and the nano-bioelectronics frontier

This review describes work presented in the 2014 inaugural Tsinghua University Press-Springer Nano Research Award lecture, as well as current and future opportunities for nanoscience research at the interface with brain science. First, we briefly summarize some of the considerations and the research journey that has led to our focus on bottom-up nanoscale science and technology. Second, we recapitulate the motivation for and our seminal contributions to nanowire- based nanoscience and technology, including the rational design and synthesis of increasingly complex nanowire structures, and the corresponding broad range of ＂applications＂ enabled by the capability to control structure, com- position and size from the atomic level upwards. Third, we describe in more detail nanowire-based electronic devices as revolutionary tools for brain science, including （i） motivation for nanoelectronics in brain science, （ii） demonstration of nanowire nanoelectronic arrays for high-spatial/high-temporal resolution extracellular recording, （iii） the development of fundamentally-new intracellular nanoelectronic devices that approach the sizes of single ion channels, （iv） the introduction and demonstration of a new paradigm for innervating cell networks with addressable nanoelectronic arrays in three-dimensions. Last, we conclude with a brief discussion of the exciting and potentially transformative advances expected to come from work at the nanoelectronics-brain interface.