Neuroprosthetic Research Articles

Application of Multi-Input Multi-Output Brain-Computer Interface in Cognitive Assessment for Alzheimer’s Disease

This research aims to augment human cognitive abilities, particularly memory function, through the development of innovative prosthetics. The hippocampus, a crucial brain region involved in memory encoding and recall, has been the focal point of numerous studies seeking to develop prosthetics that can restore or enhance memory function in individuals with impairments such as Alzheimer’s disease. This review examines two studies that delve into the development and application of hippocampal neural prosthetics, highlighting the potential of these prosthetics to revolutionize the treatment of memory impairments. One study utilized static neural stimulation patterns to enhance memory for specific information content, while another developed a nonlinear Multiple Input Multiple Output (MIMO) dynamic model to predict and stimulate neural activity patterns during memory encoding. Both studies demonstrated significant improvements in memory performance. Additionally, this paper discusses the clinical significance and application prospects of hippocampal neural prosthetics, including the potential for implantable prosthetics and the widespread applicability of MIMO stimulation. Furthermore, the paper explores advanced modeling and classification techniques for neural ensembles and visual memory decoding, including Volterra-type hierarchical modeling, optimal multi-unit stimulation patterns, and a sparse classification model for decoding visual memories. Finally, the paper presents the implementation and evaluation of neural prosthesis systems, including an application-specific integrated circuit (ASIC) design for a hippocampal prosthesis and a high-performance, scalable system architecture for real-time estimation of neural activity. These advancements hold promise for future research and applications in neural prosthetics for memory enhancement.

Adhesion of retinal cells to gold surfaces by biomimetic molecules.

Neural cell-electrode coupling is crucial for effective neural and retinal prostheses. Enhancing this coupling can be achieved through surface modification and geometrical design to increase neuron-electrode proximity. In the current research, we focused on designing and studying various biomolecules as a method to elicit neural cell-electrode adhesion via cell-specific integrin mechanisms. We designed extracellular matrix biomimetic molecules with different head sequences (RGD or YIGSR), structures (linear or cyclic), and spacer lengths (short or long). These molecules, anchored by a thiol (SH) group, were deposited onto gold surfaces at various concentrations. We assessed the modifications using contact angle measurements, fluorescence imaging, and X-ray Photoelectron Spectroscopy (XPS). We then analyzed the adhesion of retinal cells and HEK293 cells to the modified surfaces by measuring cell density, surface area, and focal adhesion spots, and examined changes in adhesion-related gene and integrin expression. Results showed that YIGSR biomolecules significantly enhanced retinal cell adhesion, regardless of spacer length. For HEK293 cells, RGD biomolecules were more effective, especially with cyclic RGD and long spacers. Both cell types showed increased expression of specific adhesion integrins and proteins like vinculin and PTK2; these results were in agreement with the adhesion studies, confirming the cell-specific interactions with modified surfaces. This study highlights the importance of tailored biomolecules for improving neural cell adhesion to electrodes. By customizing biomolecules to foster specific and effective interactions with adhesion integrins, our study provides valuable insights for enhancing the integration and functionality of retinal prostheses and other neural implants.

Transformation of neural coding for vibrotactile stimuli along the ascending somatosensory pathway

In mammals, action potentials fired by rapidly adapting mechanosensitive afferents are known to reliably time lock to the cycles of a vibration. How and where along the ascending neuraxis is the peripheral afferent temporal code transformed into a rate code are currently not clear. Here, we probed the encoding of vibrotactile stimuli with electrophysiological recordings along major stages of the ascending somatosensory pathway in mice. We discovered the main transformation step was identified at the level of the thalamus, and parvalbumin-positive interneurons in thalamic reticular nucleus participate in sharpening frequency selectivity and in disrupting the precise spike timing. When frequency-specific microstimulation was applied within the brainstem, it generated frequency selectivity reminiscent of real vibration responses in the somatosensory cortex and could provide informative and robust signals for learning in behaving mice. Taken together, these findings could guide biomimetic stimulus strategies to activate specific nuclei along the ascending somatosensory pathway for neural prostheses.

Evolution, perception, and the mind

The classical mind–body problem persists as one of the deepest scientific mysteries. Despite the grand claims of the new AI, some of the most basic facts of human vision cannot be explained by current or proposed theories of brain structure and function. This paper reviews some well-known mysteries including the neural binding problem, blind sight, subjective experience and prosthetics. There is continuing progress, but the core mysteries of the mind seem to require fundamental advances for any reductionist resolution.

Decreasing the physical gap in the neural-electrode interface and related concepts to improve cochlear implant performance.

Cochlear implants (CI) represent incredible devices that restore hearing perception for those with moderate to profound sensorineural hearing loss. However, the ability of a CI to restore complex auditory function is limited by the number of perceptually independent spectral channels provided. A major contributor to this limitation is the physical gap between the CI electrodes and the target spiral ganglion neurons (SGNs). In order for CI electrodes to stimulate SGNs more precisely, and thus better approximate natural hearing, new methodologies need to be developed to decrease this gap, (i.e., transitioning CIs from a far-field to near-field device). In this review, strategies aimed at improving the neural-electrode interface are discussed in terms of the magnitude of impact they could have and the work needed to implement them. Ongoing research suggests current clinical efforts to limit the CI-related immune response holds great potential for improving device performance. This could eradicate the dense, fibrous capsule surrounding the electrode and enhance preservation of natural cochlear architecture, including SGNs. In the long term, however, optimized future devices will likely need to induce and guide the outgrowth of the peripheral process of SGNs to be in closer proximity to the CI electrode in order to better approximate natural hearing. This research is in its infancy; it remains to be seen which strategies (surface patterning, small molecule release, hydrogel coating, etc.) will be enable this approach. Additionally, these efforts aimed at optimizing CI function will likely translate to other neural prostheses, which face similar issues.

Conformal Neuromorphic Bioelectronics for Sense Digitalization.

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency ofbiological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Multiplexing intensity and frequency sensations for artificial touch by modulating temporal features of electrical pulse trains.

In neural prostheses, intensity modulation of a single channel (i.e., through a single stimulating electrode) has been achieved by increasing the magnitude or width of each stimulation pulse, which risks eliciting pain or paraesthesia; and by changing the stimulation rate, which leads to concurrent changes in perceived frequency. In this study, we sought to render a perception of tactile intensity and frequency independently, by means of temporal pulse train patterns of fixed magnitude, delivered non-invasively. Our psychophysical study exploits a previously discovered frequency coding mechanism, where the perceived frequency of stimulus pulses grouped into periodic bursts depends on the duration of the inter-burst interval, rather than the mean pulse rate or periodicity. When electrical stimulus pulses were organised into bursts, perceived intensity was influenced by the number of pulses within a burst, while perceived frequency was determined by the time between the end of one burst envelope and the start of the next. The perceived amplitude was modulated by 1.6× while perceived frequency was varied independently by 2× within the tested range (20-40 Hz). Thus, the sensation of intensity might be controlled independently from frequency through a single stimulation channel without having to vary the injected electrical current. This can form the basis for improving strategies in delivering more complex and natural sensations for prosthetic hand users.

Stability Study of Synthetic Diamond Using a Thermally Controlled Biological Environment: Application towards Long-Lasting Neural Prostheses.

This paper demonstrates, for the first time, the stability of synthetic diamond as a passive layer within neural implants. Leveraging the exceptional biocompatibility of intrinsic nanocrystalline diamond, a comprehensive review of material aging analysis in the context of in-vivo implants is provided. This work is based on electric impedance monitoring through the formulation of an analytical model that scrutinizes essential parameters such as the deposited metal resistivity, insulation between conductors, changes in electrode geometry, and leakage currents. The evolution of these parameters takes place over an equivalent period of approximately 10 years. The analytical model, focusing on a fractional capacitor, provides nuanced insights into the surface conductivity variation. A comparative study is performed between a classical polymer material (SU8) and synthetic diamond. Samples subjected to dynamic impedance analysis reveal distinctive patterns over time, characterized by their physical degradation. The results highlight the very high stability of diamond, suggesting promise for the electrode's enduring viability. To support this analysis, microscopic and optical measurements conclude the paper and confirm the high stability of diamond and its strong potential as a material for neural implants with long-life use.

An electrocorticography-based speech decoder for neural speech prostheses

An electrocorticography-based speech decoder for neural speech prostheses

Ergodic sequential logic spiking neural network: reproductions of biologically plausible spatio-temporal phenomena and low-power implementation towards neural prosthesis

Ergodic sequential logic spiking neural network: reproductions of biologically plausible spatio-temporal phenomena and low-power implementation towards neural prosthesis

Dual Interactive Mode Human-Machine Interfaces Based on Triboelectric Nanogenerator and IGZO/In2O3 Heterojunction Synaptic Transistor.

Imitating human neural networks via bio-inspired electronics advances human-machine interfaces (HMI), overcoming von Neumann limitations and enabling efficient, low-energy data processing in the big data era. However, single-contact mode HMIs have inherent limitations in terms of their capabilities and performances, such as constrained adaptability to dynamic environments, and reduced cognitive processing capabilities. Here, a dual-interactive-mode HMI system based on a triboelectric nanogenerator (TENG) and heterojunction synaptic transistor (HJST) is proposed for both contact and non-contact applications. The TENG incorporates a poly-methyl meth-acrylate (PMMA)-NiCo2S4/S film, in which the NiCo2S4/S composite traps and blocks electrons to optimize charge generation and storage. The heterojunction structure, mitigates the Debye screening effect, thereby improving transistor characteristics and reliability. The integrated TENG-HJST system exhibits synaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/depression (PPF/PPD), and synaptic plasticity, enabling emulation of neural behavior and advanced information processing. Moreover, neural morphology manipulation is demonstrated in practical tasks, such as controlling international chess games. By integrating the TENG-HJST device with a robotic hand, conscious artificial responses are generated, enhancing event accuracy. This breakthrough in dual-interactive-mode interfacing holds promise for HMI systems and neural prostheses.

A neural speech decoding framework leveraging deep learning and speech synthesis

Decoding human speech from neural signals is essential for brain–computer interface (BCI) technologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity and high dimensionality. Here we present a novel deep learning-based neural speech decoding framework that includes an ECoG decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable speech synthesizer that maps speech parameters to spectrograms. We have developed a companion speech-to-speech auto-encoder consisting of a speech encoder and the same speech synthesizer to generate reference speech parameters to facilitate the ECoG decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Our experimental results show that our models can decode speech with high correlation, even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. Finally, we successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with deficits resulting from left hemisphere damage.

Computational analysis of electrode structure and configuration for efficient and localized neural stimulation.

Neuromodulation technique using electric stimulation is widely applied in neural prosthesis, therapy, and neuroscience research. Various stimulation techniques have been developed to enhance stimulation efficiency and to precisely target the specific area of the brain which involves optimizing the geometry and the configuration of the electrode, stimulation pulse type and shapes, and electrode materials. Although the effects of electrode shape, size, and configuration on the performance of neural stimulation have individually been characterized, to date, there is no integrative investigation of how this factor affects neural stimulation. In this study, we computationally modeled the various types of electrodes with varying shapes, sizes, and configurations and simulated the electric field to calculate the activation function. The electrode geometry is then integratively assessed in terms of stimulation efficiency and stimulation focality. We found that stimulation efficiency is enhanced by making the electrode sharper and smaller. A center-to-vertex distance exceeding 100µm shows enhanced stimulation efficiency in the bipolar configuration. Additionally, the separation distance of less than 1mm between the reference and stimulation electrodes exhibits higher stimulation efficiency compared to the monopolar configuration. The region of neurons to be stimulated can also be modified. We found that sharper electrodes can locally activate the neuron. In most cases, except for the rectangular electrode shape with a center-to-vertex distance smaller than 100µm, the bipolar electrode configuration can locally stimulate neurons as opposed to the monopolar configuration. These findings shed light on the optimal selection of neural electrodes depending on the target applications.

Speech decoding using cortical and subcortical electrophysiological signals.

Language impairments often result from severe neurological disorders, driving the development of neural prosthetics utilizing electrophysiological signals to restore comprehensible language. Previous decoding efforts primarily focused on signals from the cerebral cortex, neglecting subcortical brain structures' potential contributions to speech decoding in brain-computer interfaces. In this study, stereotactic electroencephalography (sEEG) was employed to investigate subcortical structures' role in speech decoding. Two native Mandarin Chinese speakers, undergoing sEEG implantation for epilepsy treatment, participated. Participants read Chinese text, with 1-30, 30-70, and 70-150 Hz frequency band powers of sEEG signals extracted as key features. A deep learning model based on long short-term memory assessed the contribution of different brain structures to speech decoding, predicting consonant articulatory place, manner, and tone within single syllable. Cortical signals excelled in articulatory place prediction (86.5% accuracy), while cortical and subcortical signals performed similarly for articulatory manner (51.5% vs. 51.7% accuracy). Subcortical signals provided superior tone prediction (58.3% accuracy). The superior temporal gyrus was consistently relevant in speech decoding for consonants and tone. Combining cortical and subcortical inputs yielded the highest prediction accuracy, especially for tone. This study underscores the essential roles of both cortical and subcortical structures in different aspects of speech decoding.

Development of a feline model for preclinical research of a new translabyrinthine auditory nerve implant.

Cochlear implants are among the most successful neural prosthetic devices to date but exhibit poor frequency selectivity and the inability to consistently activate apical (low frequency) spiral ganglion neurons. These issues can limit hearing performance in many cochlear implant patients, especially for understanding speech in noisy environments and in perceiving or appreciating more complex inputs such as music and multiple talkers. For cochlear implants, electrical current must pass through the bony wall of the cochlea, leading to widespread activation of auditory nerve fibers. Cochlear implants also cannot be implanted in some individuals with an obstruction or severe malformations of the cochlea. Alternatively, intraneural stimulation delivered via an auditory nerve implant could provide direct contact with neural fibers and thus reduce unwanted current spread. More confined current during stimulation can increase selectivity of frequency fiber activation. Furthermore, devices such as the Utah Slanted Electrode Array can provide access to the full cross section of the auditory nerve, including low frequency fibers that are difficult to reach using a cochlear implant. However, further scientific and preclinical research of these Utah Slanted Electrode Array devices is limited by the lack of a chronic large animal model for the auditory nerve implant, especially one that leverages an appropriate surgical approach relevant for human translation. This paper presents a newly developed transbullar translabyrinthine surgical approach for implanting the auditory nerve implant into the cat auditory nerve. In our first of a series of studies, we demonstrate a surgical approach in non-recovery experiments that enables implantation of the auditory nerve implant into the auditory nerve, without damaging the device and enabling effective activation of the auditory nerve fibers, as measured by electrode impedances and electrically evoked auditory brainstem responses. These positive results motivate performing future chronic cat studies to assess the long-term stability and function of these auditory nerve implant devices, as well as development of novel stimulation strategies that can be translated to human patients.

An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation.

A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals' independence. A comprehensive model incorporating PT's physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim's realistic musculoskeletal representation of the human wrist with two degrees of freedom. We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological H∞ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson's disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection. The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson's patients than the essential tremor. The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics. Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses.

Cochlear Implant Artifacts Removal in EEG-Based Objective Auditory Rehabilitation Assessment.

Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in auditory rehabilitation outcomes following cochlear implantation may be due to cerebral reorganization. Electroencephalography (EEG), favored for its CI compatibility and non-invasiveness, has become a staple in clinical objective assessments of cerebral plasticity post-implantation. However, the electrical activity of CI distorts neural responses, and EEG susceptibility to these artifacts presents significant challenges in obtaining reliable neural responses. Despite the use of various artifact removal techniques in previous studies, the automatic identification and reduction of CI artifacts while minimizing information loss or damage remains a pressing issue in objectively assessing advanced auditory functions in CI recipients. To address this problem, we propose an approach that combines machine learning algorithms-specifically, Support Vector Machines (SVM)-along with Independent Component Analysis (ICA) and Ensemble Empirical Mode Decomposition (EEMD) to automatically detect and minimize electrical artifacts in EEG data. The innovation of this research is the automatic detection of CI artifacts using the temporal properties of EEG signals. By applying EEMD and ICA, we can process and remove the identified CI artifacts from the affected EEG channels, yielding a refined signal. Comparative analysis in the temporal, frequency, and spatial domains suggests that the corrected EEG recordings of CI recipients closely align with those of peers with normal hearing, signifying the restoration of reliable neural responses across the entire scalp while eliminating CI artifacts.

Neural Manifold Constraint for Spike Prediction Models Under Behavioral Reinforcement.

Spike prediction models effectively predict downstream spike trains from upstream neural activity for neural prostheses. Such prostheses could potentially restore damaged neural communication pathways using predicted patterns to guide electrical stimulations on downstream. Since the ground truth of downstream neural activity is unavailable for subjects with the damage, reinforcement learning (RL) with behavior-level rewards becomes necessary for model training. However, existing models do not involve any constraint on the generated firing patterns and neglect the correlations among neural activities. Thus, the model outputs can greatly deviate from the natural range of neural activities, causing concerns for clinical usage. This study proposes the neural manifold constraint to solve this problem, shaping RL-generated spike trains in the feature space. The constraint terms describe the first and second order statistics of the neural manifold estimated from neural recordings during subjects' freely moving period. Then, the models can be optimized within the neural manifold by behavioral reinforcement. We test the method to predict primary motor cortex (M1) spikes from medial prefrontal (mPFC) spikes when rats perform the two-lever discrimination task. Results show that the neural activity generated by constrained models resembles the real M1 recordings. Compared with models without constraints, our approach achieves similar behavioral success rates, but reduces the mean squared error of neural firing by 61%. The constraints also increase the model's robustness across data segments and induce realistic neural correlations. Our method provides a promising tool to restore transregional communication with high behavioral performance and more realistic microscopic patterns.

Neural interface technology for human-computer interaction

The Brain-Computer Interface (BCI) is a highly promising way to establish a direct link between the human brain and external computerised apparatus, enabling individuals with severe disabilities to interact with their external environment. By harnessing BCI technology, these individuals can exert control over specific computerized devices, ranging from computers and wheelchairs to neural prosthetics, thus facilitating meaningful interaction with the world around them. However, while BCI technology holds immense potential, several aspects remain in conceptual stages or are constrained by specific circumstances. This review offers a comprehensive exploration of the fundamental principles governing BCI classification. It also provides an overview of applications that BCI has to offer and its multifaceted utility, particularly in the application of neuroprostheses, in conjunction with FES, and in the rehabilitation of stroke and epilepsy. The review navigates through the landscape of BCI application technologies, elucidating their challenges, and constraints. After analyses, potential developments in BCI technology were identified, and possible future challenges were highlighted.

Simulation of Spinal Cord Reflexes

The importance of spinal reflexes is connected to the rehabilitation processes in neural prostheses and to the neuromuscular junction. In order to model neuron networks as electronic circuits, a simulation environment like LTSpice XVII or PSpice can be used to create a complete electronic description. There are four types of neurons employed in spinal reflexes: α-motoneurons, sensitive neurons, excitatory interneurons, and inhibitory interneurons. Many proposals have been made regarding methods that can be used for assimilating neurons using electronic circuits. In this paper, only a single internal model of a neuron is considered enough to simulate all four types of neurons implicated in the control loops. The main contribution of this paper is to propose the modeling of neurons using some electronic circuits designed either with a bipolar transistor or with CMOS transistors for the input and output of circuits stages. In this way, it is possible to mimic the neural pulses’ circulation along the loops of the spinal reflexes and to prove the accuracy of the simulation results with respect to the biological signals collected from the bibliographic materials.