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Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, cognitive systems have been proposed as useful and effective frameworks for the modeling and understanding of brain activity patterns. They also enable direct communication pathways between the brain and external devices (brain–computer/machine interfaces). However, most of the research so far has focused on lab-based applications in constrained scenarios, which cannot be extrapolated to realistic field contexts. Considering the decoding of brain activity, biomedical engineers provide excellent tools to overcome the challenges of learning from brain activity patterns that are very likely to be affected by nonstationary behaviors and high uncertainty. The application of health and neural engineering to learning and modeling has recently demonstrated its remarkable usefulness for coping with the effects of extremely noisy environments, as well as the variability and dynamicity of brain signals. In addition, neurobiological studies have suggested that the behavior of neural cells exhibits functional patterns that resemble the properties of computational neuroscience to encode logical perception.

Superparamagnetic iron oxide nanoparticles (SPIONs) have been used for a variety of biomedical applications, from multimodal imaging to the mechanical activity of cells and tissues. Herein, we present fluorescently mantled carbon coated core-shell superparamagnetic iron oxide nanoparticles (FC-SPIONs) as an excellent material to promote the neuronal differentiation and neuronal network outgrowth in neural tissue engineering applications. Morphological, structural, and functional group characterizations were systematically investigated. FC-SPIONs showed superior magnetic and inherent fluorescence characteristic properties. Furthermore, FC-SPIONs interactions against neuronal PC12 cells showed promising results and deliberate their potential for significant applications in neuroengineering. Interestingly, FC-SPIONs were assessed as biocompatible and promoted the neuronal PC12 cell differentiation process. Accompanied by these results, network outgrowth and branching patterns of neuronal processes can be regulated using FC-SPIONs. Importantly, FC-SPIONs are promising due to their biocompatibility and selective affinity toward neuronal cells, paving the way for neuronal differentiation and outgrowth and neuronal therapeutics in neuroengineering applications.

No abstract available.

Microelectrode-Arrays (MEAs) allow neural recording of thousands of neurons/mm2 by sensing: Extracellular Action Potentials (EAP) and Local Field Potentials (LFP). MEAs arrange several recording sites (or pixels) in a spatial grid/matrix, planarly and capacitively coupled with in-vitro cell cultures (growth above the chip surface) and/or integrated in electrocorticography grids. This paper focuses on Electrolyte-Oxide (C)MOS Field-Effect-Transistors MEAs for cell-level recording. In this type of biosensors, each single row of the matrix is composed of N planar metal electrodes and is scanned synchronously and regularly for N clock cycles, adopting Time-Division-Multiplexing (TDM) schemes. TDM approach generates an analogue output signal for each biosensor row, which includes in a single time track the information acquired by the N electrodes of the matrix. It is therefore of fundamental importance to estimate the noise power of the output signal of the single row because this power defines the minimum detectable threshold of the neuro-potential signal power. Noise in planar MEA is determined by the classical contributions of electronic noise (thermal and flicker, coming from both biological environment and semiconductor devices) and from the spurious corrupting signal due to multiplexing action (which behaves to all effects as a statistical noise signal following a Gaussian probability distribution). This paper presents the complete procedure for designing an (active) biosensor matrix/array (embedding the analog signal processing channels) as a function of a specific Noise Figure requirement (that measures the Signal-to-Noise-Ratio (SNR) degradation and thus is defined as the ratio between the biosensor array input SNR and the output SNR of the analog acquisition channel). This procedure is applied to a single row of the biosensor matrix, can be easily extended to 2D array, and allows to define all the design parameters (including electrode area, gain, bandwidth and noise power of the analog stages building the array) to obtain the specific Noise Figure.

Retinal degenerative diseases that progressively lead to severe blindness impact the affected individual’s quality-of-life. Visual prosthesis technology aims to provide an individual a potential means of obtaining visual information lost to them by blindness. Since the proof-of-concept success in 1968 of a device implanted in a human, visual prostheses have had sustained academic research and commercial interest. However, commercial failure of two retinal prosthesis device have raised concerns for the visual prosthesis field. To learn from this experience, research in this dissertation is aimed at understanding the impact of electric stimulation on the target neural tissue and investigating technology for a visual cortex prosthesis, which can reach a larger patient population (compared to a retinal prosthesis). My first set of experiments assessed, in an animal model of retinal degeneration, the condition of the brain and its ability to receive artificial vision information. Retinitis Pigmentosa has been proven to impact the human brain. My study investigated the extent to which this was replicated in a rat animal model of a single genetic mutation of Retinitis Pigmentosa. The P23H-1 rat was investigated with electrophysiology and immunohistochemistry to understand the brain’s function and structural condition. Visually evoked potentials changed as a result of blindness progression and electrically evoked response was maintained during retinal degeneration. Histology images show a relatively stable macrostructure of the blind rat brain. Neural activity measure using c-Fos saw a change due to weekly stimulation, but the results may be spurious when put next to an auxiliary analysis using high magnification VGluT2 images. I also created a rodent retinal implant procedure to test newly developed visual prosthesis devices. A retinal device with Parylene-C as its main component was tested and its feasibility in the small eye of a rat animal model was investigated. The device can survive 4-weeks of implantation and is mechanically stable within the eye. In support of the development of a novel cortical visual prosthesis device that fits the need of blind individuals, I used a rat animal model to prove the efficacy and safety of a novel neurostimulation device in preclinical development (StiMote). I worked to characterize the full ability of the neural interface, High-density carbon fiber arrays with electrodeposited Platinum-Iridium. The ability of PtIr-HDCF as a simultaneous recording and stimulation neural interface device was verified with nominal electrochemical measurements before, during, and after a long-duration 7-hour electric stimulation session that simulated a full day of device use. Based on my previous work and prior literature of HDCF as a neural interface that reduces neuroinflammatory response compared to other microelectrode array archetypes, PtIr-HDCF can be used as a device to monitor the brain and can better extract the effect of electric stimulation on the brain alone. I recorded neural electrophysiology to verify the rat brain’s sensitivity to stimuli before and after 7-hour stimulation. Based on the visually evoked potential and intracortical microstimulation data, possible increase in cortical sensitivity to stimuli was hypothesized. To add another layer of information, Spatial Transcriptomics as a novel method was used to define electric stimulation safety. Spatial Transcriptomics showed that PtIr-HDCF, when compared to a conventional microwire array, performs better in reducing proinflammatory cytokines. Findings of this dissertation can be used to better inform future investigations into brain electrophysiology and transcriptomics projects aimed to assist cortical visual prosthesis device development.

Brain-computer interfaces are becoming increasingly popular in both commercial and research contexts, yet their diversity and impact in application have been relatively limited. This thesis introduces Brain-Computer Integration – a framework that guides researchers and practitioners in designing, evaluating, and describing novel brain-computer interfaces where the system and the user’s brain become tightly coupled through synergizing each other’s processes as an integrated whole. Through this framework, this thesis presents guidance for human-computer interaction in unlocking the potential of this emerging technology and deploying it to foster human flourishing through enabling deep self-reflection; enhancing cognition; and extending the mind beyond the individual.

Sudden unexpected death in epilepsy (SUDEP) is a fatal complication of epilepsy that kills 1̃2 of every 10,000 epileptic patients every year. SUDEP has proven difficult to study because it frequently occurs unobserved and cannot be predicted. What limited clinical data exists suggests that SUDEP occurs as a cardiorespiratory collapse immediately following a seizure. In this work, I explore how a group of autonomic reflexes termed collectively as “oxygen‐conserving reflexes (OCRs)” lead to sudden death when activated during seizures. I also demonstrate multiple physiological parallels between the OCR‐mediated deaths that I report and the clinical data on cases of human SUDEP. Additionally, I explore the neural pathway underlying OCRs, identify the carotid body as a potential target for intervention, and demonstrate the efficacy of electroceutical intervention in reducing the mortality risk of OCR activation during seizures. This work seeks to both offer a neural explanation for SUDEP as well as present a promising target and means for potential intervention.

Summary Current technological advances in neural probing and modulation have enabled an extraordinary glimpse into the intricacies of the nervous system. Particularly, nanomaterials are proving to be an incredibly versatile platform for neurological applications owing to their biocompatibility, tunability, highly specific targeting and sensing, and long-term chemical stability. Among the most desirable nanomaterials for neuroengineering, freestanding nanomaterials are minimally invasive and remotely controlled. This review outlines the most recent developments of freestanding nanomaterials that operate on the neuronal interface. First, the different nanomaterials and their mechanisms for modulating neurons are explored to provide a basis for how freestanding nanomaterials operate. Then, the three main applications of subcellular neuronal engineering—modulating neuronal behavior, exploring fundamental neuronal mechanism, and recording neuronal signal—are highlighted with specific examples of current advancements. Finally, we conclude with our perspective on future nanomaterial designs and applications., Graphical abstract, Biotechnology; Materials science; Nanomaterials; Neuroscience

Validation of neural probe performance often includes implantation in live animals, to assess ability to detect and distinguish signals generated by individual neurons. While this method is informative, an effective in vitro alternative would streamline device development and improve ethical considerations by reducing the use of animals in the validation of neural recording devices. Here, we describe a simple system using ball electrodes to apply multiple neural waveforms to phosphate buffered saline, which are simultaneously recorded by a microelectrode probe. Using this technique, our neural probe was able to detect and distinguish spikes from multiple units of roughly physiological amplitudes (~100 microvolts peak to peak), indicating promise as an in vitro alternative to animal testing for initial validation of neural recording devices.

Although vagus nerve stimulation (VNS) can be used to reduce heartrate by enhancing parasympathetic activity, a fully controllable intervention would also require a method for downregulating parasympathetic activity. A direct current (DC) block can be applied to a nerve to block its action potential conduction. This nerve block can be used to downregulate parasympathetic activity by blocking afferent reflexes. The damaging effects of reactions that occur at the electrode-nerve interface using conventional platinum electrodes can be avoided by separating the electrode from the nerve. Using a biocompatible, ionically conducting medium, the electrode and the damaging reactions can be isolated in a vessel away from the nerve. This type of electrode has been called the Separated Interface Nerve Electrode (SINE). Fuzzy logic control (FLC) is a controller approach that is well suited to physiological systems. The SINE, controlled by an FLC, was utilized to block a stimulated vagus nerve and regulate heart rate. The FLC was able to maintain the heartrate at a pre-determined setpoint while still achieving instant recovery when the block was removed.

Summary Along with the advancement in neural engineering techniques, unprecedented progress in the development of neural interfaces has been made over the past few decades. However, despite these achievements, there is still room for further improvements especially toward the possibility of monitoring and modulating neural activities with high resolution and specificity in our daily lives. In an effort of taking a step toward the next-generation neural interfaces, we want to highlight the recent progress in neural technologies. We will cover a wide scope of such developments ranging from novel platforms for highly specific recording and modulation to system integration for practical applications of novel interfaces., Graphical abstract, Neuroscience; Techniques in neuroscience; Biotechnology; Bioelectronics

This paper presents specific noise minimization strategies to be adopted in silicon–cell interfaces. For this objective, a complete and general model for the analog processing of the signal coming from cell–silicon junctions is presented. This model will then be described at the level of the single stages and of the fundamental parameters that characterize them (bandwidth, gain and noise). Thanks to a few design equations, it will therefore be possible to simulate the behavior of a time-division multiplexed acquisition channel, including the most relevant parameters for signal processing, such as amplification (or power of the analog signal) and noise. This model has the undoubted advantage of being particularly simple to simulate and implement, while maintaining high accuracy in estimating the signal quality (i.e., the signal-to-noise ratio, SNR). Thanks to the simulation results of the model, it will be possible to set an optimal operating point for the front-end to minimize the artifacts introduced by the time-division multiplexing (TDM) scheme and to maximize the SNR at the a-to-d converter input. The proposed results provide an SNR of 12 dB at 10 µVRMS of noise power and 50 µVRMS of signal power (both evaluated at input of the analog front-end, AFE). This is particularly relevant for cell–silicon junctions because it demonstrates that it is possible to detect weak extracellular events (of the order of few µVRMS) without necessarily increasing the total amplification of the front-end (and, therefore, as a first approximation, the dissipated electrical power), while adopting a specific gain distribution through the acquisition chain.

Despite increasing use of in vivo multielectrode array (MEA) implants for basic research and medical applications, the critical structural interfaces formed between the implants and the brain parenchyma, remain elusive. Prevailing view assumes that formation of multicellular inflammatory encapsulating-scar around the implants [the foreign body response (FBR)] degrades the implant electrophysiological functions. Using gold mushroom shaped microelectrodes (gMμEs) based perforated polyimide MEA platforms (PPMPs) that in contrast to standard probes can be thin sectioned along with the interfacing parenchyma; we examined here for the first time the interfaces formed between brains parenchyma and implanted 3D vertical microelectrode platforms at the ultrastructural level. Our study demonstrates remarkable regenerative processes including neuritogenesis, axon myelination, synapse formation and capillaries regrowth in contact and around the implant. In parallel, we document that individual microglia adhere tightly and engulf the gMμEs. Modeling of the formed microglia-electrode junctions suggest that this configuration suffice to account for the low and deteriorating recording qualities of in vivo MEA implants. These observations help define the anticipated hurdles to adapting the advantageous 3D in vitro vertical-electrode technologies to in vivo settings, and suggest that improving the recording qualities and durability of planar or 3D in vivo electrode implants will require developing approaches to eliminate the insulating microglia junctions.

Hybrid-modality brain-computer Interfaces (BCIs), which combine motor imagery (MI) bio-signals and steady-state visual evoked potentials (SSVEPs), has attracted wide attention in the research field of neural engineering. The number of channels should be as small as possible for real-life applications. However, most of recent works about channel selection only focus on either the performance of classification task or the effectiveness of device control. Few works conduct channel selection for MI and SSVEP classification tasks simultaneously. In this paper, a multitasking-based multiobjective evolutionary algorithm (EMMOA) was proposed to select appropriate channels for these two classification tasks at the same time. Moreover, a two-stage framework was introduced to balance the number of selected channels and the classification accuracy in the proposed algorithm. The experimental results verified the feasibility of multiobjective optimization methodology for channel selection of hybrid BCI tasks.

Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.

Arrays of floating neural sensors with high channel count that cover an area of square centimeters and larger would be transformative for neural engineering and brain-machine interfaces. Meeting the power and wireless data communications requirements within the size constraints for each neural sensor has been elusive due to the need to incorporate sensing, computing, communications, and power functionality in a package of approximately 100 micrometers on a side. In this work, we demonstrate a near infrared optical power and data communication link for a neural recording system that satisfies size requirements to achieve dense arrays and power requirements to prevent tissue heating. The optical link is demonstrated using an integrated optoelectronic device consisting of a tandem photovoltaic cell and microscale light emitting diode. End-to-end functionality of a wireless neural link within system constraints is demonstrated using a pre-recorded neural signal between a self-powered CMOS integrated circuit and single photon avalanche photodiode.

Computational electromagnetics has evolved greatly over the past several decades, owing in great part to the trasnformational improvements in computing infrastructures and power. Biomedical applications stand among the applications that have benefited the most from this transformational change - the evolution of computational algorithms paired with unprecedented computational resources fueled significant progress in medical devices development, neuroengineering, imaging, and more.

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm electroencephalography (EEG)-based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.

Objective. For decades electrical stimulation has been used in neuroscience to investigate brain networks and been deployed clinically as a mode of therapy. Classically, all methods of electrical stimulation require implanted electrodes to be connected in some manner to an apparatus which provides power for the stimulation itself. Approach. We show the use of novel organic electronic devices, specifically organic electrolytic photocapacitors (OEPCs), which can be activated when illuminated with deep-red wavelengths of light and correspondingly do not require connections with external wires or power supplies when implanted at various depths in vivo. Main results. We stimulated cortical brain tissue of mice with devices implanted subcutaneously, as well as beneath both the skin and skull to demonstrate a wireless stimulation of the whisker motor cortex. Devices induced both a behavior response (whisker movement) and a sensory response in the corresponding sensory cortex. Additionally, we showed that coating OEPCs with a thin layer of a conducting polymer formulation (PEDOT:PSS) significantly increases their charge storage capacity, and can be used to further optimize the applied photoelectrical stimulation. Significance. Overall, this new technology can provide an on-demand electrical stimulation by simply using an OEPC and a deep-red wavelength illumination. Wires and interconnects to provide power to implanted neurostimulation electrodes are often problematic in freely-moving animal research and with implanted electrodes for long-term therapy in patients. Our wireless brain stimulation opens new perspectives for wireless electrical stimulation for applications in fundamental neurostimulation and in chronic therapy. Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [716867, 949191]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation; Swedish Research Council (Vetenskapsradet)Swedish Research Council [2018-04505]

Objective. Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI. Methods. Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control. Results: Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home. Significance. Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.

This report presents characterizations of in vivo neural recordings performed with a CMOS multichannel neural recording chip that uses rapid multiplexing directly at the electrodes, without any pre-amplification or buffering. Neural recordings were taken from a 16-channel microwire array implanted in rodent cortex, with comparison to a gold-standard commercial bench-top recording system. We were able to record well-isolated threshold crossings from 10 multiplexed electrodes and typical local field potential waveforms from 16, with strong agreement with the standard system (average SNR = 2.59 and 3.07 respectively). For 10 electrodes, the circuit achieves an effective area per channel of 0.0077 mm2, which is >5x smaller than typical multichannel chips. Extensive characterizations of noise and signal quality are presented and compared to fundamental theory, as well as results from in vivo and in vitro experiments. By demonstrating the validation of rapid multiplexing directly at the electrodes, this report confirms it as a promising approach for reducing circuit area in massively-multichannel neural recording systems, which is crucial for scaling recording site density and achieving large-scale sensing of brain activity with high spatiotemporal resolution.

The brain is made out of an incredible diversity of cells called neurons, which have different shapes, are made of different molecules, and that change in different ways in diseases. In optogenetics, microbial opsins, natural proteins that convert light into electrical signals, are genetically expressed in neurons. Then, light pulses can be used to turn neurons on, revealing how they trigger behaviors, disease states, or therapeutic effects, or to turn neurons off, revealing what functions or dysfunctions they are necessary for.

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here, we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.

Summary Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care., Graphical Abstract, Highlights • We developed a novel real-time neuromorphic system acting as a neuroprosthesis • The neuroprosthesis can bidirectionally reconnect two disconnected networks • The neuroprosthesis implements a real-time spiking neural network • The spiking network can successfully replace the activity of a neural population, Neuroscience; Systems Neuroscience; Computer Science; Evolvable Hardware; Electronic Materials

Idiopathic Parkinsons disease (PD) is currently the second most important neurodegenerative disease in incidence. Deep brain stimulation (DBS) constitutes a successful and necessary therapy; however, the continuous stimulation it provides can be associated with multiple side effects. DBS uses an implanted pulse generator that delivers, through a set of electrodes, electrical stimulation to the target area, normally the Sub Thalamic Nucleus. Recently, Closed-loop DBS has emerged as a promising new strategy, where the device stimulates only when necessary, thereby reducing any adverse effects. Here, we present a Closed-loop DBS system for PD, which is able to recognize, with 100% accuracy, when the patient is going to enter into the tremor phase, thus allowing the device to stimulate only in such cases. The expert system has been designed and implemented within the data stream mining paradigm, suitable for our scenario since it can cope with continuous data of a theoretical infinite length and with a certain variability, which uses the synchronization among the neural population within the Sub Thalamic Nucleus as the continuous data stream input to the system.

Decoding speech from intracranial recordings serves two main purposes: understanding the neural correlates of speech processing and decoding speech features for targeting speech neuroprosthetic devices. Intracranial recordings have high spatial and temporal resolution, and thus offer a unique opportunity to investigate and decode the electrophysiological dynamics underlying speech processing. In this review article, we describe current approaches to decoding different features of speech perception and production - such as spectrotemporal, phonetic, phonotactic, semantic, and articulatory components - using intracranial recordings. A specific section is devoted to the decoding of imagined speech, and potential applications to speech prosthetic devices. We outline the challenges in decoding human language, as well as the opportunities in scientific and neuroengineering applications.

Although the mechanisms of steady-state visual evoked potentials (SSVEPs) have been well studied, none of them have been implemented with strictly experimental conditions. Our objective was to create an ideal observer condition to exploit the features of SSVEPs. We present here an electroencephalographic (EEG) eye tracking experimental paradigm that provides biofeedback for gaze restriction during the visual stimulation. Specifically, we designed an EEG eye tracking synchronous data recording system for successful trial selection. Forty-six periodic flickers within a visual field of 11.5° were successively presented to evoke SSVEP responses, and online biofeedback based on an eye tracker was provided for gaze restriction. For eight participants, SSVEP responses in the visual field and topographic maps from full-brain EEG were plotted and analyzed. The experimental results indicated that the optimal visual flicking arrangement to boost SSVEPs should include the features of circular stimuli within a 4–6° spatial distance and increased stimulus area below the fixation point. These findings provide a basis for determining stimulus parameters for neural engineering studies, e.g. SSVEP-based brain-computer interface (BCI) designs. The proposed experimental paradigm could also provide a precise framework for future SSVEP-related studies.

This review paper defines NeuroRehabilitation Mechatronics (NRM) as an overlap between two areas of applied science: Bio-mechatronics and Neural Engineering. NRM is an umbrella terminology which covers diverse existing mechatronic technologies that assist patients to regain their motor functions that are lost due to neural and/or physical damage. Two major categories of NRM technologies are identified in this paper: (a) Haptics-enabled Interactive Robotic Neuro-rehabilitation (HIRN) systems, and (b) Assistive Neural Technologies. The main functional difference between the two categories is explained in this paper to provide a better understanding of the boundaries and main functionality of each category. HIRN systems accelerate brain or spinal cord plasticity and recovery, over time. However, assistive neural systems instantly augment the capabilities of an injured individual in performing activities of daily living and the primary goal of this category is not to produce a carryover recovery effect. Accelerated trends of society aging and under-resourced world healthcare systems are discussed as the factors which necessitate further development of NRM technology. This review paper mainly focuses on the first category of NRM systems, i.e., HIRN technology. The paper introduces different classes of this technology and aims to provide a view of the existing technical, technological and control challenges of the current state of this technology together with the ongoing lines of research. For this purpose, the existing commercialized HIRN systems, the specific design, modes of operation, functionality, effectiveness, existing technical issues, control design and possible future developments are studied. In this regard, it is shown that although the effectiveness of HIRN technology is widely accepted and endorsed by official organizations, such as the American Heart Association, there are still conflicting clinical studies with contradictory conclusions. Reasons for these contradictions are discussed in the paper. Two major challenges are identified in this regard, namely conservative patient-robot interaction stabilizing algorithms, and insufficient adaptability of the control parameters to the needs and biomechanics of the patient. Accordingly, based on recent literature, possible future trends for this technology are envisioned such as (a) making the control design of the robots more flexible and intelligent to better match the patients’ needs and biomechanics; (b) designing stabilizing algorithms which can guarantee physical patient-robot interaction stability with minimum conservatism while maximizing the fidelity of force field applied to the patient’s limb; and (c) making it possible to have rehabilitation robots in patients’ homes (e.g., via cloud-based and remote neurorehabilitation) to increase the duration of interactive rehabilitation and thereby improve outcomes while reducing cost.