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Clinical applications such as artificial vision require extraordinary, diverse, lengthy and intimate collaborations among basic scientists, engineers and clinicians. In this review, we present the state of research on a visual neuroprosthesis designed to interface with the occipital visual cortex as a means through which a limited, but useful, visual sense could be restored in profoundly blind individuals. We review the most important physiological principles regarding this neuroprosthetic approach and emphasize the role of neural plasticity in order to achieve desired behavioral outcomes. While full restoration of fine detailed vision with current technology is unlikely in the immediate near future, the discrimination of shapes and the localization of objects should be possible allowing blind subjects to navigate in a unfamiliar environment and perhaps even to read enlarged text. Continued research and development in neuroprosthesis technology will likely result in a substantial improvement in the quality of life of blind and visually impaired individuals.

Rapid advances in neuroscience have sparked numerous efforts to study the neural correlate of consciousness. Prominent subjects include higher sensory area, distributed assemblies bound by synchronization of neuronal activity and neurons in specific cortical laminae. In contrast, it has been suggested that the quality of sensory awareness is determined by systematic change of afferent signals resulting from behaviour and knowledge thereof. Support for such skill-based theories of perception is provided by experiments on sensory substitution. Here, we pursue this line of thought and create new sensorimotor contingencies and, hence, a new quality of perception. Adult subjects received orientation information, obtained by a magnetic compass, via vibrotactile stimulation around the waist. After six weeks of training we evaluated integration of the new input by a battery of tests. The results indicate that the sensory information provided by the belt (1) is processed and boosts performance, (2) if inconsistent with other sensory signals leads to variable performance, (3) does interact with the vestibular nystagmus and (4) in half of the experimental subjects leads to qualitative changes of sensory experience. These data support the hypothesis that new sensorimotor contingencies can be learned and integrated into behaviour and affect perceptual experience.

One potential negative impact on the quality of life of a spinal cord injured person is the loss of the ability to control devices in their immediate environment. Consequently, research and development has been conducted on technology designed to restore some measure of independence by providing means of control over these devices. A previous assistive device using changes in brain signals from eye closure as its switching system was created. Brain signals were processed using spectral analysis and although this was a successful technique, there were limitations that resulted in higher than desired switching errors. This paper presents results of an alternative method for processing brain signals as the basis for switching, called fractal dimension. In comparison to the spectral technique, the fractal dimension technique was successful in reducing the number of false positive and false negative errors. Additionally, it eliminated the need for a baseline setup for this system. This suggests that fractal dimension is a potentially viable method for analysing brain signals for use in assistive control systems.

To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)—for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.

Methods are presented to incorporate polymer-based bioactive matrices into micro-fabricated implantable microelectrode arrays. Using simple techniques, hydrogels infused with bioactive molecules are deposited within wells in the substrate of the device. This method allows local drug delivery without increasing the footprint of the device. In addition, each well can be loaded individually, allowing spatial and temporal control over diffusion gradients in the microenvironment of the implanted neural interface probe. In vivo testing verified the following: diffusion of the bioactive molecules, integration of the bioactive molecules with the intended neural target and concurrent extracellular recording using nearby electrodes. These results support the feasibility of using polymer gels to deliver bioactive molecules to the region close to microelectrode shanks. This technique for microdrug delivery may serve as a means to intervene with the initial phases of the neuroinflammatory tissue response to permanently implanted microelectrode arrays.

Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain–computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time–frequency analysis approach for classifying motor imagery tasks. Time–frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.

The functional effects of selective hypoglossal nerve (HG) stimulation with a multi-contact peripheral nerve electrode were assessed using images of the upper airways and the tongue in anesthetized beagles. A biphasic pulse train of 50 Hz frequency and 2 s duration was applied through each one of the tripolar contact sets of the nerve electrode while the pharyngeal images were acquired into a computer. The stimulation current was limited to 20% above the activation threshold for maximum selectivity. The images showed that various contact sets could generate several different activation patterns of the tongue muscles resulting in medial and/or lateral dilation and closing of the airways at the tongue root. Some of these patterns translated into an increase in the oropharyngeal size while others did not have any effect. The pharyngeal sizes were not statistically different during stimulation either between the two different positions of the head (30° and 60°), or when the lateral contacts were compared with the medial ones. The contacts that had the least effect generated an average of 53 ± 15% pharyngeal dilation relative to the best contacts, indicating that the results are marginally sensitive to the contact position around the HG nerve trunk. These results suggest that selective HG nerve stimulation can be a useful technique to produce multiple tongue activation patterns that can dilate the pharynx. This may in turn increase the size of the patient population who can benefit from HG nerve stimulation as a treatment method for obstructive sleep apnea.

The ability to successfully interface the brain to external electrical systems is important both for fundamental understanding of our nervous system and for the development of neuroprosthetics. Silicon microelectrode arrays offer great promise in realizing this potential. However, when they are implanted into the brain, recording sensitivity is lost due to inflammation and astroglial scarring around the electrode. The inflammation and astroglial scar are thought to result from acute injury during electrode insertion as well as chronic injury caused by micromotion around the implanted electrode. To evaluate the validity of this assumption, the finite element method (FEM) was employed to analyze the strain fields around a single Michigan Si microelectrode due to simulated micromotion. Micromotion was mimicked by applying a force to the electrode, fixing the boundaries of the brain region and applying appropriate symmetry conditions to nodes lying on symmetry planes. Characteristics of the deformation fields around the electrode including maximum electrode displacement, strain fields and relative displacement between the electrode and the adjacent tissue were examined for varying degrees of physical coupling between the brain and the electrode. Our analysis demonstrates that when physical coupling between the electrode and the brain increases, the micromotion-induced strain of tissue around the electrode decreases as does the relative slip between the electrode and the brain. These results support the use of neuro-integrative coatings on electrode arrays as a means to reduce the micromotion-induced injury response.

Evoked motor signals descending down the corticospinal tract can be recorded selectively with multi-contact electrodes from the spinal cord surface. This method of extracting motor signals from the spinal cord may provide a means of communication for people with spinal cord injury. The information rate obtained with such an interface will improve if the separation of neural channels can be increased. In this study, the feasibility of increasing the channel separation was investigated using the blind source separation (BSS) technique. Neural signals recorded with multi-contact surface electrodes were treated as a linear mixture of independent neural sources located inside the spinal cord. Principal component analysis (PCA) was applied to estimate the dimensionality of the raw signals, and then the fixed-point FastICA algorithm was used to separate the primary neural sources from the secondary (smaller) ones. In all trials but one, the separation between the neural channels has increased by eliminating the secondary sources. These results suggest that the information rate of a spinal cord interface can be improved by separating the neural recordings into their independent components and selecting the ones with the largest distance between them. Comparison of independent component analysis (ICA) and PCA reveals that ICA performs better in this application.

This research presents immunohistochemical strategies for assessing the interactions at the immediate interface between micro-scale implanted devices and the surrounding brain tissue during inflammatory astrogliotic reactions. This includes preparation, microscopy and analysis techniques for obtaining images of the intimate contact between neural cells and the surface of implantable micro-electromechanical systems (MEMS) devices. The ability to visualize the intact interface between an implant and the surrounding tissue allows researchers to examine tissue that is unchanged from its native implanted state. Conversely, current popular techniques involve removing the implant. This tends to cause damage to the tissue immediately surrounding the implant and can hinder one's ability to differentiate inflammatory responses to the implant versus physical damage occurring from removal of the implant from the tissue. Due to advances in microscopy and staining techniques, it is now possible to visualize the intact tissue–implant interface. This paper presents the development of imaging techniques for visualizing the intact interface between neural tissue and implanted devices. This is particularly important for understanding both the acute and chronic neuroinflammatory responses to devices intended for long-term use in a prosthetic system. Non-functional, unbonded devices were imaged in vitro and in vivo at different times post-implantation via a range of techniques. Using these techniques, detailed interactions could be seen between delicate cellular processes and the electrode surface, which would have been destroyed using conventional histology processes.

The viability of chronic neural microelectrodes for electrophysiological recording and stimulation depends on several factors, including the encapsulation of the implant by a reactive tissue response. We postulate that mechanical strains induced around the implant site may be one of the leading factors responsible for the sustained tissue response in chronic implants. The objectives of this study were to develop a finite-element model of the probe–brain tissue interface and analyze the effects of tethering forces, probe–tissue adhesion and stiffness of the probe substrate on the interfacial strains induced around the implant site. A 3D finite-element model of the probe–brain tissue microenvironment was developed and used to simulate interfacial strains created by 'micromotion' of chronically implanted microelectrodes. Three candidate substrates were considered: (a) silicon, (b) polyimide and (c) a hypothetical 'soft' material. Simulated tethering forces resulted in elevated strains both at the tip and at the sharp edges of the probe track in the tissue. The strain fields induced by a simulated silicon probe were similar to those induced by a simulated polyimide probe, albeit at higher absolute values for radial tethering forces. Simulations of poor probe–tissue adhesion resulted in elevated strains at the tip and delamination of the tissue from the probe. A tangential tethering force results in 94% reduction in the strain value at the tip of the polyimide probe track in the tissue, whereas the simulated 'soft' probe induced two orders of magnitude smaller values of strain compared to a simulated silicon probe. The model results indicate that softer substrates reduce the strain at the probe–tissue interface and thus may also reduce tissue response in chronic implants.

Delivery of pharmacological agents in vitro can often be a difficult, time consuming and costly process. In this paper, we describe an economical method for in vitro delivery using a hydrogel of poly hydroxyethyl methacrylate (PHEMA) that can absorb up to 50% of its weight of any water-solubilized pharmacological agent. This agent will then passively diffuse into surrounding media upon application in vitro. An in vitro test of PHEMA as a drug delivery device was conducted using dissociated rat-cortical neurons cultured on micro-electrode arrays. These micro-electrode arrays permit the real-time measurement of neural activity at 60 different sites across a network of neurons. Neural activity was compared during the application of PHEMA saturated with cell culture media and PHEMA saturated with bicuculline, a widely used pharmacological agent with stereotypical effects on neural activity patterns. Application of PHEMA saturated with bicuculline produced a gradual increase in concentration in vitro. When the minimum effective concentration of bicuculline was reached, which was found to be 0.59 µM using the diffusion properties of PHEMA, it produced the rapid almost periodic synchronized bursting characteristically associated with this agent. In contrast, the application of PHEMA saturated in culture media alone had no effect on neural activity reinforcing its inherent inert properties. Since PHEMA is nontoxic, can be molded into a variety of shapes, quickly manufactured in any laboratory and is inexpensive to produce, the material represents a promising alternative to drug delivery systems on the market today.

Brain–computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.

In this paper, we attempt to determine a subject's intention of generating torque at the shoulder or elbow, two neighboring joints, using scalp electroencephalogram signals from 163 electrodes for a brain–computer interface (BCI) application. To achieve this goal, we have applied a time–frequency synthesized spatial patterns (TFSP) BCI algorithm with a presorting procedure. Using this method, we were able to achieve an average recognition rate of 89% in four healthy subjects, which is comparable to the highest rates reported in the literature but now for tasks with much closer spatial representations on the motor cortex. This result demonstrates, for the first time, that the TFSP BCI method can be applied to separate intentions between generating static shoulder versus elbow torque. Furthermore, in this study, the potential application of this BCI algorithm for brain-injured patients was tested in one chronic hemiparetic stroke subject. A recognition rate of 76% was obtained, suggesting that this BCI method can provide a potential control signal for neural prostheses or other movement coordination improving devices for patients following brain injury.

Deep brain stimulation (DBS) electrodes are designed to stimulate specific areas of the brain. The most widely used DBS electrode has a linear array of 4 cylindrical contacts that can be selectively turned on depending on the placement of the electrode and the specific area of the brain to be stimulated. The efficacy of DBS therapy can be improved by localizing the current delivery into specific populations of neurons and by increasing the power efficiency through a suitable choice of electrode geometrical characteristics. We investigated segmented electrode designs created by sectioning each cylindrical contact into multiple rings. Prototypes of these designs, made with different materials and larger dimensions than those of clinical DBS electrodes, were evaluated in vitro and in simulation. A finite element model was developed to study the effects of varying the electrode characteristics on the current density and field distributions in an idealized electrolytic medium and in vitro experiments were conducted to measure the electrode impedance. The current density over the electrode surface increased towards the edges of the electrode, and multiple edges increased the non-uniformity of the current density profile. The edge effects were more pronounced over the end segments than over the central segments. Segmented electrodes generated larger magnitudes of the second spatial difference of the extracellular potentials, and thus required lower stimulation intensities to achieve the same level of neuronal activation as solid electrodes. For a fixed electrode conductive area, increasing the number of segments (edges) decreased the impedance compared to a single solid electrode, because the average current density over the segments increased. Edge effects played a critical role in determining the current density distributions, neuronal excitation patterns, and impedance of cylindrical electrodes, and segmented electrodes provide a means to increase the efficiency of DBS.

During a traumatic insult to the brain, tissue is subjected to large stresses at high rates which often surpass cellular thresholds leading to cell dysfunction or death. The acute response of neurons to a mechanical trauma, however, is poorly understood. Plasma membrane disruption may be the earliest cellular outcome from a mechanical trauma. The increase in membrane permeability due to such disruptions may therefore play an important role in the initiation of deleterious cascades following brain injury. The immediate consequences of an increase in plasma membrane permeability on the electrophysiological behavior of a neuronal network exposed to the trauma have not been elucidated. We have developed an in vitro model of traumatic brain injury (TBI) that utilizes a novel device capable of applying stress at high rates to neuronal cells cultured on a microelectrode array. The mechanical insult produced by the device caused a transient increase in neuronal plasma membrane permeability, which subsided after 10 min. We were able to monitor acute spontaneous electrophysiological activity of injured cultures for at least 10 min following the insult. Firing frequency, average burst interval and spikes within burst were assessed before and after injury. The electrophysiological responses to the insult were heterogeneous, although an increase in burst intervals and in the variability of the assessed parameters were common. This study provides a multi-faceted approach to elucidate the role of neuronal plasma membrane disruptions in TBI and its functional consequences.

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