Optoelectrical Devices for Neural Interfacing: Engineering Integration, Stability, and Multimodal Sensing.

Brain–computer interfaces (BCI) are reliant on the interface between electrodes and neurons to function. The foreign body reaction (FBR) that occurs in response to electrodes in the brain alters this interface and may pollute detected signals, ultimately impeding BCI function. The size of the FBR is influenced by several key factors explored in this review; namely, (a) the size of the animal tested, (b) anatomical location of the BCI, (c) the electrode morphology and coating, (d) the mechanics of electrode insertion, and (e) pharmacological modification (e.g., drug eluting electrodes). Trialing methods to reduce FBR in vivo, particularly in large models, is important to enable further translation in humans, and we systematically reviewed the literature to this effect. The OVID, MEDLINE, EMBASE, SCOPUS and Scholar databases were searched. Compiled results were analysed qualitatively. Out of 8388 yielded articles, 13 were included for analysis, with most excluded studies experimenting on murine models. Cats, rabbits, and a variety of breeds of minipig/marmoset were trialed. On average, over 30% reduction in inflammatory cells of FBR on post mortem histology was noted across intervention groups. Similar strategies to those used in rodent models, including tip modification and flexible and sinusoidal electrode configurations, all produced good effects in histology; however, a notable absence of trials examining the effect on BCI end-function was noted. Future studies should assess whether the reduction in FBR correlates to an improvement in the functional effect of the intended BCI.

P140. Towards an estimation of ECoG decoding results based on fully non-invasive MEG acquisition

High-precision in vivo therapeutic technologies that establish three-dimensional (3D), multimodal neural interfaces with targeted biotissues offer significant clinical potential for the timely treatments of localized peripheral nerve injury (PNI). Current approaches for this purpose such as implantable devices face challenges in terms of percutaneous wires and/or nondegradable designs, and support only single-mode operation that lack microscale spatial resolution. Here, we develop a miniaturized, self-wrapping system that yields wireless, multimodal neural interfaces with 3D adaptation across localized peripheral nerves at scales ranging from tens of micrometers (15 μm) to millimeters. Such platform integrates multilayer architectures that include SiNx layers as the mechanically triggered substrate for 3D wrapping, with multimodal treatments via MXene and drug-loaded layers for photothermal stimulation and pharmacological release. Experimental and computational studies establish operational principle as the basis for the combination of long-term photothermal therapy and transient drug delivery at high spatiotemporal resolution. In vivo tests on living rat models demonstrate that the implantable neural interface can roll up across the localized, dynamic surface of injured nerves, providing sustained treatments over 1 mo in a fully bioresorbable design after the healing process. These findings create future opportunities of such wireless, multimodal system with 3D self-wrapping techniques for precise PNI therapeutic strategies.

Objective. Brain-Machine Interfaces (BMIs) hold great promises for advancing neuroprosthetics, robotics, and for providing treatment options for severe neurological diseases. The objective of this work is the development and in vivo evaluation of electrodes for BMIs that meet the needs to record brain activity at sub-millimeter resolution over a large area of the cortex while being soft and electromechanically robust (i.e. stretchable). Approach. Current electrodes require a trade-off between high spatiotemporal resolution and cortical coverage area. To address the needs for simultaneous high resolution and large cortical coverage, the prototype electrode array developed in this study employs a novel bilayer routing of soft and stretchable lead wires from the recording sites on the surface of the brain (electrocorticography, ECoG) to the data acquisition system. Main results. To validate the recording characteristics, the array was implanted in healthy felines for up to 5 months. Neural signals recorded from both layers of the device showed elevated mid-frequency structures typical of local field potential (LFP) signals that were stable in amplitude over implant duration, and also exhibited consistent frequency-dependent modulation after anesthesia induction by Telazol. Significance. The successful development of a soft and stretchable large-area, high resolution micro ECoG electrode array (lahrμECoG) is an important step to meet the neurotechnological needs of advanced BMI applications.

Intracortical recordings can be used to voluntarily control external devices via brain-machine interfaces (BMI). Multiple factors, including the foreign body response (FBR), limit the stability of these neural signals over time. Current clinically approved devices consist of multi-electrode arrays with a single electrode site at the tip of each shank, confining the recording interface to a single layer of the cortex. Advancements in manufacturing technology have led to the development of high-density electrodes that can record from multiple layers. However, the long-term stability of neural recordings and the extent of neuronal cell loss around the electrode across different cortical depths have yet to be explored. To answer these questions, we recorded neural signals from rats chronically implanted with a silicon-substrate microelectrode array spanning the layers of the cortex. Our results show the long-term stability of intracortical recordings varies across cortical depth, with electrode sites around L4-L5 having the highest stability. Using machine learning guided segmentation, our novel histological technique, DeepHisto, revealed that the extent of neuronal cell loss varies across cortical layers, with L2/3 and L4 electrodes having the largest area of neuronal cell loss. These findings suggest that interfacing depth plays a major role in the FBR and long-term performance of intracortical neuroprostheses.

在长期的载人航天任务中，复杂且极端的太空环境对宇航员的生理、心理及认知功能产生显著的影响，直接威胁任务的安全性和执行效率。脑机接口(brain-computer interface，BCI)技术作为连接大脑与外部设备的关键桥梁，通过精准采集和解析神经信号，为载人航天任务提供了创新的人机协同范式。非侵入式BCI技术在航天员选拔、训练、在轨任务执行及返回后康复等多个阶段展现出广阔的应用潜力。在任务准备阶段，BCI技术通过整合多模态信号评估与神经反馈训练，显著提升航天员的认知功能和心理韧性。在任务执行阶段，BCI技术能够实时监测航天员的生理与心理状态，并通过意念控制优化设备操作，从而提升任务效率与安全性。在任务结束返回后的康复期，BCI技术不仅能够结合非侵入式神经调控改善情绪与认知功能，还能够支持航天员的运动和认知恢复，并有助于其进行长期的健康管理。然而，BCI技术在航天应用中仍面临信号鲁棒性不足、系统自适应性有限及数据处理效率欠佳等挑战。未来，BCI技术应融合多模态生理传感器与深度学习算法，以实现精准监测和个性化干预，并结合虚拟现实及机器人技术开发智能化人机协作模式，为航天任务提供更高效的人机交互支持。

Closed‐loop neuromodulation requires precise, stable, and cell‐specific control of neural circuits with minimal invasiveness. However, conventional optogenetic systems are hindered by invasive optical fibers, viral‐based gene delivery, and disjointed hardware modules. Here, a multifunctional neural interface integrating non‐viral delivery of AAV‐derived Channelrhodopsin‐2 (ChR2) gene plasmid, fiberless intracranial optogenetic stimulation via externally delivered near‐infrared (NIR) excitation, and electrophysiological recording into a single implantable device is reported. The core of this interface is a 3D gold inverse opal (AuIO) microelectrode that provides high surface area, promoting both electroporation‐mediated gene transfection and neural signal acquisition. ChR2‐expressing plasmid DNA is complexed with polyethyleneimine‐neurotensin (NT‐PEI) as non‐viral gene vectors that are immobilized onto designated electrode sites for neuron‐targeted localized gene expression. Upconversion nanoparticles (UCNPs) embedded in a gelatin methacryloyl (GelMA) matrix are precisely integrated onto the microscale AuIO surface via aerosol jet printing, enabling localized surface plasmon resonance (LSPR)‐enhanced NIR‐to‐blue light conversion for remote optogenetic activation. In vivo implantation into the hippocampal dentate gyrus (DG) demonstrates successful opsin expression and real‐time light‐evoked neural activity via single surgical step. This all‐in‐one platform provides a fiber‐free, biocompatible neural interface capable of stable in vivo operation for deep‐brain optogenetic engineering, paving the way for precision closed‐loop neuromodulation.

We present UGR-MINDVOICE, the University of Granada (UGR) multimodal electroencephalography (EEG) and audio dataset for overt and covert speech in Iberian Spanish intended for basic neuroscience and brain-computer interface (BCI) research. The dataset features EEG and audio recordings from 15 native Spanish speakers engaged in both overt and covert speech production tasks. This dataset is unique in its inclusion of all Spanish phonemes and a diverse set of words spanning various semantic categories and different usage frequencies. Validation of the dataset confirmed the presence of robust sensory event-related potentials, including the visual P100 and the auditory N1 (N100), indicating reliable early perceptual processing and sustained participant attention to both visual and auditory stimuli. Additionally, the EEG data were classified into rest, covert speech, and overt speech conditions with an accuracy of 81.40%, demonstrating active participant engagement in the tasks. By providing synchronised EEG and audio data for overt speech, along with EEG data for the same stimuli during covert speech, UGR-MINDVOICE constitutes a valuable resource for advancing research in basic neuroscience and brain-computer interfaces, particularly in the domain of silent speech communication. The full dataset is openly available on the Open Science Framework (OSF) ( https://osf.io/6sh5d ), and all accompanying code and analysis scripts are provided in a public GitHub repository ( https://github.com/owaismujtaba/mind-voice ). • UGR-MINDVOICE is the first open-access EEG-audio dataset designed specifically for Iberian Spanish, capturing both overt and covert speech across all Spanish phonemes and a wide range of lexical items. • The dataset supports the development of brain-computer interfaces for silent speech decoding, offering a non-invasive alternative to intracranial methods and enabling communication for individuals with severe motor impairments. • With high-density EEG, multimodal stimuli (text, audio, and images), and carefully controlled tasks, UGR-MINDVOICE enables robust modelling of speech production and perception.

Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent (targets), motor-task irrelevant infrequent (deviants), and motor-task irrelevant frequent (standards) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive ERPs can successfully be detected while subjects are performing additional ongoing motor activity. This supports single-trial detection of ERPs evoked by target events to, e.g., infer a patient's attentional state during therapeutic intervention.

Biomaterials are currently and will continue to exert a high impact in the field of medicine and biological systems. Despite their great importance to humans, biomaterials have been reported to increase the risk of various kinds of cancers. Herein we shed light on the impact of biomaterial on the accumulation of genetic and epigenetic alterations that may boost cancer risk. In addition, we identify the key elements for the selection of the most reconcilable biomaterials and tests to ensure their biocompatibility. We examined the downsides of the usage of these biomaterials; specifically, immunological foreign body response due to wear and corrosion and their ability to cause cancers. The main focus of our review is to highlight the current knowledge about how orthopedic implants may contribute to cancer formation. We conclude that, although the risk appears to be minimal, implants may contribute to the development of cancer demanding the employment of a test that gauges the mutagenicity of the biomaterial used in the various implants’ applications.

Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states

Enhanced genetically encoded voltage indicators advance their applications in neuroscience

Functional ultrasound (fUS) is a neuroimaging method that uses ultrasound to track changes in cerebral blood volume as an indirect readout of neuronal activity at high spatiotemporal resolution. fUS is capable of imaging head-fixed or freely behaving rodents and of producing volumetric images of the entire mouse brain. It has been applied to many species, including primates and humans. Now that fUS is reaching maturity, it is being adopted by the neuroscience community. However, the nature of the fUS signal and the different implementations of fUS are not necessarily accessible to nonspecialists. This review aims to introduce these ultrasound concepts to all neuroscientists. We explain the physical basis of the fUS signal and the principles of the method, present the state of the art of its hardware implementation, and give concrete examples of current applications in neuroscience. Finally, we suggest areas for improvement during the next few years.

The development of a biomimetic neuronal network from neural cells is a big challenge for researchers. Recent advances in nanotechnology, on the other hand, have enabled unprecedented tools and techniques for guiding and directing neural stem cell proliferation and differentiation in vitro to construct an in vivo-like neuronal network. Nanotechnology allows control over neural stem cells by means of scaffolds that guide neurons to reform synaptic networks in suitable directions in 3D architecture, surface modification/nanopatterning to decide cell fate and stimulate/record signals from neurons to find out the relationships between neuronal circuit connectivity and their pathophysiological functions. Overall, nanotechnology-mediated methods facilitate precise physiochemical controls essential to develop tools appropriate for applications in neuroscience. This review emphasizes the newest applications of nanotechnology for examining central nervous system (CNS) roles and, therefore, provides an insight into how these technologies can be tested in vitro before being used in preclinical and clinical research and their potential role in regenerative medicine and tissue engineering.

Photothermal neural stimulation enables optical excitation or inhibition of neural activity depending on the dynamics of localized temperature changes, offering high spatial resolution without genetic modification. However, quantitative analysis of these temperature dynamics remains limited due to the lack of suitable direct sensing technologies, posing a challenge to the safe and controlled application of photothermal neural stimulation techniques. This challenge is addressed by developing transparent thermoelectric temperature sensor arrays with high spatiotemporal resolution, integrated with electrical and optical recording capabilities. These microscale sensors stably and accurately capture rapid temperature increases and decreases, and thermal equilibrium induced by thermo‐plasmonic effects at the neural interface, regardless of the environment. The multifunctional platform allows simultaneous electrical and optical monitoring of neural responses during the photothermal stimulation, enabling detailed analysis of the correlation between localized temperature changes and neural activities. a reversible neural inhibition window (1.4–4.5 °C) and thresholds for irreversible damage (>6.1 °C) are identifyed. Using high temporal‐resolution sensing, localized thermo‐plasmonic temperature dynamics over tens of milliseconds, and associated neural signal suppression and reactivation are captured. This approach provides unprecedented insight into the interplay between photothermal effects and neural activity, establishing a foundation for precise, temperature‐guided neuromodulation therapies and advanced neural circuit research.