It has been hypothesized that a vision prosthesis capable of evoking useful visual percepts can be based upon electrically stimulating the primary visual cortex (V1) of a blind human subject via penetrating microelectrode arrays. As a continuation of earlier work, we examined several spatial and temporal characteristics of V1 microstimulation.
An array of 100 penetrating microelectrodes was chronically implanted in V1 of a behaving macaque monkey. Microstimulation thresholds were measured using a two-alternative forced choice detection task. Relative locations of electrically-evoked percepts were measured using a memory saccade-to-target task.
The principal finding was that two years after implantation we were able to evoke behavioural responses to electric stimulation across the spatial extent of the array using groups of contiguous electrodes. Consistent responses to stimulation were evoked at an average threshold current per electrode of 204 ± 49 µA (mean ± std) for groups of four electrodes and 91 ± 25 µA for groups of nine electrodes. Saccades to electrically-evoked percepts using groups of nine electrodes showed that the animal could discriminate spatially distinct percepts with groups having an average separation of 1.6 ± 0.3 mm (mean ± std) in cortex and 1.0 ± 0.2 degrees in visual space.
These results demonstrate chronic perceptual functionality and provide evidence for the feasibility of a cortically-based vision prosthesis for the blind using penetrating microelectrodes.
Modern multielectrode array (MEA) systems can record the neuronal activity from thousands of electrodes, but their ability to provide spatio-temporal patterns of electrical stimulation is very limited. Furthermore, the stimulus-related artifacts significantly limit the ability to record the neuronal responses to the stimulation. To address these issues, we designed a multichannel integrated circuit for patterned MEA-based electrical stimulation and evaluated its performance in experiments with isolated mouse and rat retina.
The Stimchip includes 64 independent stimulation channels. Each channel comprises an internal digital-to-analog converter that can be configured as a current or voltage source. The shape of the stimulation waveform is defined independently for each channel by the real-time data stream. In addition, each channel is equipped with circuitry for reduction of the stimulus artifact.
Using a high-density MEA stimulation/recording system, we effectively stimulated individual retinal ganglion cells (RGCs) and recorded the neuronal responses with minimal distortion, even on the stimulating electrodes. We independently stimulated a population of RGCs in rat retina and, using a complex spatio-temporal pattern of electrical stimulation pulses, we replicated visually-evoked spiking activity of a subset of these cells with high fidelity.
Compared with current state-of-the-art MEA systems, the Stimchip is able to stimulate neuronal cells with much more complex sequences of electrical pulses and with significantly reduced artifacts. This opens up new possibilities for studies of neuronal responses to electrical stimulation, both in the context of neuroscience research and in the development of neuroprosthetic devices.
Penetrating cortical neural probe technologies allow investigators to record electrical signals in the brain. Implantation of probes results in acute tissue damage, and microglia density increases around implanted devices over weeks. However, the mechanisms underlying this encapsulation are not well understood in the acute temporal domain. The objective here was to evaluate dynamic microglial response to implanted probes using two-photon microscopy.
Using two-photon in vivo microscopy, cortical microglia ~200 µm below the surface of the visual cortex were imaged every minute in mice with green fluorescent protein-expressing microglia.
Following probe insertion, nearby microglia immediately extended processes toward the probe at (1.6 ± 1.3) µmmin−1 during the first 30–45 min, but showed negligible cell body movement for the first 6 h. Six hours following probe insertion, microglia at distances <130.0 µm (p = 0.5) from the probe surface exhibit morphological characteristics of transitional stage (T-stage) activation, similar to the microglial response observed with laser-induced blood–brain barrier damage. T-stage morphology and microglia directionality indexes were developed to characterize microglial response to implanted probes. Evidence suggesting vascular reorganization after probe insertion and distant vessel damage was also observed hours after probe insertion.
A precise temporal understanding of the cellular response to microelectrode implantation will facilitate the search for molecular cues initiating and attenuating the reactive tissue response.
This paper presents a general methodology for the optimal design of stimulation patterns applied to neuronal ensembles in order to elicit a desired effect. The methodology follows a variant of the hierarchical Volterra modeling approach that utilizes input-output data to construct predictive models that describe the effects of interactions among multiple input events in an ascending order of interaction complexity. The illustrative example presented in this paper concerns the multi-unit activity of CA1 neurons in the hippocampus of a rodent performing a learned Delayed-Non-Match-to-Sample (DNMS) task. The multi-unit activity of the hippocampal CA1 neurons is recorded via chronically implanted multi-electrode arrays during this task. The obtained model quantifies the likelihood of having correct performance of the specific task for a given multi-unit (spatiotemporal) activity pattern of a CA1 neuronal ensemble during the “Sample Presentation” phase of the DNMS task. The model can be used to determine computationally (off-line) the “optimal” multi-unit stimulation pattern that maximizes the likelihood of inducing the correct performance of the DNMS task. Our working hypothesis is that application of this optimal stimulation pattern will enhance performance of the DNMS task due to enhancement of memory formation and storage during the “Sample Presentation” phase of the task.
Multi-unit neurostimulation; Nonlinear modeling; Hippocampal activity
Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference.
We introduce the concept of the burst suppression probability (BSP) to define the brain’s instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia.
The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times.
The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.
Many centers are now using high-density microelectrodes during traditional intracranial EEG (iEEG) both for research and clinical purposes. These microelectrodes are FDA-approved and integrate into clinical EEG acquisition systems. However, the electrical characteristics of these electrodes are poorly described and clinical systems were not designed to use them; thus it is possible that this shift into clinical practice could have unintended consequences. In this study, we characterized the impedance of over 100 commercial macro- and microelectrodes using electrochemical impedance spectroscopy (EIS) to determine how electrode properties could affect signal acquisition and interpretation. The EIS data were combined with the published specifications of several commercial EEG systems to design digital filters that mimic the behavior of the electrodes and amplifiers. These filters were used to analyze simulated brain signals that contain a mixture of characteristic features commonly observed in iEEG. Each output was then processed with several common quantitative EEG measurements. Our results show that traditional macroelectrodes had low impedances and produced negligible distortion of the original signal. Brain tissue and electrical wiring also had negligible filtering effects. However, microelectrode impedances were much higher and more variable than the macroelectrodes. When connected to clinical amplifiers, higher impedance electrodes produced considerable distortion of the signal at low frequencies (< 60 Hz), which caused significant changes in amplitude, phase, variance, and spectral band power. In contrast, there were only minimal changes to the signal content for frequencies above 100 Hz. In order to minimize distortion with microelectrodes, we determined that an acquisition system should have an input impedance of at least 1 GΩ, which is much higher than most clinical systems. These results show that it is critical to account for variations in impedance when analyzing EEG from different-sized electrodes. Data from microelectrodes may yield misleading results unless recorded with high-impedance amplifiers.
EEG; microelectrodes; electrodes; impedance; electrocorticography
In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.
Maintenance of cognitive control is a major concern for many human disease condition, therefore a major goal of human neuroprosthetics is to facilitate and/or recover cognitive function when such circumstances impair appropriate decision making.
Nonhuman primates trained to perform a delayed match to sample (DMS) were employed to record mini-columnar activity in the prefrontal cortex (PFC) via custom designed conformal multielectrode arrays that provided inter-laminar recordings from neurons in PFC layer 2/3 and layer 5. Such recordings were analyzed via a previously demonstrated nonlinear multi-input multi-output (MIMO) neuroprosthesis in rodents, which extracted and characterized multi-columnar firing patterns during DMS performance.
The MIMO model verified that the conformal recorded individual PFC minicolumns responded to entrained target selections in patterns critical for successful DMS performance. This allowed substitution of task-related layer 5 neuron firing patterns with electrical stimulation in the same recording regions during columnar transmission from layer 2/3 at the time of target selection. Such stimulation facilitated normal task performance, but more importantly, recovered performance when applied as a neuroprosthesis following pharmacological disruption of decision making in the same task.
Significance and potential impact
These findings provide the first successful application of a neuroprosthesis in primate brain designed specifically to restore or repair disrupted cognitive function.
cognitive neuroprosthesis; recovery of cognitive performance; prefrontal cortex; mini-column processing; decision making; nonhuman primates
Neurons cultured on multielectrode arrays almost always lack external stimulation except during the acute experimental phase. We have investigated the effects of chronic stimulation during the course of development in cultured hippocampal neural networks by applying paired pulses at half of the electrodes for 0, 1, or 3 hr/day for 8 days. Spike latencies increased from 4 to 16 ms as the distance from the stimulus increased 200–1700 μm, suggesting an average of 4 synapses over this distance. Compared to no chronic stimulation, our results indicate that, chronic stimulation increased evoked spike counts per stimulus by 50% at recording sites near the stimulating electrode and increased the instantaneous firing rate. On trials where both pulses elicited responses, spike count was 40–80% higher than when only one of the pulses elicited a response. In attempts to identify spike amplitude plasticity, we found mainly amplitude variation with different latencies suggesting recordings from neurons with different identities. These data suggest plastic network changes induced by chronic stimulation that enhance the reliability of information transmission and the efficiency of multisynaptic network communication.
Electrode array; MEA; Paired-pulse; Chronic stimulation; Plasticity
Several stories in the popular media have speculated that it may be possible to infer from the brain which word a person is speaking or even thinking. While recent studies have demonstrated that brain signals can give detailed information about actual and imagined actions, such as different types of limb movements or spoken words, concrete experimental evidence for the possibility to “read the mind,” i.e., to interpret internally-generated speech, has been scarce. In this study, we found that it is possible to use signals recorded from the surface of the brain (electrocorticography (ECoG)) to discriminate the vowels and consonants embedded in spoken and in imagined words, and we defined the cortical areas that held the most information about discrimination of the vowels and consonants. The results shed light on the distinct mechanisms associated with production of vowels and consonants, and could provide the basis for brainbased communication using imagined speech.
Historically the rectangular pulse waveform has been the choice for neural stimulation. The strength–duration curve is thus defined for rectangular pulses. Not much attention has been paid to alternative waveforms to determine if the pulse shape has an effect on the strength–duration relation. Similarly the charge injection capacity of neural electrodes has also been measured with rectangular pulses. In this study we questioned if non-rectangular waveforms can generate a stronger stimulation effect, when applied through practical electrodes, by minimizing the neural activation threshold and maximizing the charge injection capacity of the electrode. First, the activation threshold parameters were studied with seven different pulse shapes using computer simulations of a local membrane model. These waveforms were rectangular, linear increase and decrease, exponential increase and decrease, Gaussian, and sinusoidal. The chronaxie time was found to be longer with all the non-rectangular pulses and some provided more energy efficient stimulation than the rectangular waveform. Second, the charge injection capacity of titanium nitride microelectrodes was measured experimentally for the same waveforms. Linearly decreasing ramp provided the best charge injection for all pulse widths tested from 0.02 to 0.5 ms. Finally, the most efficient waveform that maximized the charge injection capacity of the electrode while providing the lowest threshold charge for neural activation was searched. Linear and exponential decrease, and Gaussian waveforms were found to be the most efficient pulse shapes.
There is a growing interest in the use of Deep Brain Stimulation for the treatment of medically refractory movement disorders and other neurological and psychiatric conditions. The extent of temperature increases around DBS electrodes during normal operation (joule heating and increased metabolic activity) or coupling with an external source (e.g. MRI) remains poorly understood and methods to mitigate temperature increases are being actively investigated. We developed a heat transfer finite element method simulation of DBS incorporating the realistic architecture of Medtronic 3389 leads. The temperature changes were analyzed considering different electrode configurations, stimulation protocols, and tissue properties. The heat-transfer model results were then validated using micro-thermocouple measurements during DBS lead stimulation in a saline bath. FEM results indicate that lead design (materials and geometry) may have a central role in controlling temperature rise by conducting heat. We show how modifying lead design can effectively control temperature increases. The robustness of this heat-sink approach over complimentary heat-mitigation technologies follows from several features: 1) it is insensitive to the mechanisms of heating (e.g. nature of magnetic coupling); 2) does not interfere with device efficacy; and 3) can be practically implemented in a broad range of implanted devices without modifying the normal device operations or the implant procedure.
Bioheat model; DBS; Implanted Devices; Joule heat; Temperature control; safety; FEM
To develop and test a photovoltaic retinal prosthesis for restoring sight to patients blinded by degenerative retinal diseases.
A silicon photodiode array for subretinal stimulation has been fabricated by a silicon-integrated-circuit/MEMS process. Each pixel in the two-dimensional array contains three series-connected photodiodes, which photovoltaically convert pulsed near-infrared light into bi-phasic current to stimulate nearby retinal neurons without wired power connections. The device thickness is chosen to be 30 μm to absorb a significant portion of light while still being thin enough for subretinal implantation. Active and return electrodes confine current near each pixel and are sputter coated with iridium oxide to enhance charge injection levels and provide a stable neural interface. Pixels are separated by 5 μm-wide trenches to electrically isolate them and to allow nutrient diffusion through the device. Three sizes of pixels (280μm, 140 μm, and 70 μm) with active electrodes of 80 μm, 40 μm and 20 μm in diameter were fabricated.
The turn-on voltages of one-, two- and three-series-connected photodiode structures are approximately 0.6V, 1.2V and 1.8V, respectively. The measured photo-responsivity per diode at 880 nm wavelength is ~0.36 A/W, at zero voltage bias and scales with the exposed silicon area. For all three pixel sizes, the reverse-bias dark current is sufficiently low (<100 pA) for our application. Pixels of all three sizes reliably elicit retinal responses at safe near-infrared light irradiances, with good acceptance of the photodiode array in the subretinal space.
The fabricated device delivers efficient retinal stimulation at safe near-infrared light irradiances without any wired power connections, which greatly simplifies the implantation procedure. Presence of the return electrodes in each pixel helps to localize the current, and thereby improves resolution.
retinal prosthesis; photodiode; photovoltaic; photo-responsivity; MEMS
The recent explosion of interest in brain-machine interfaces (BMIs) has spurred research into optimally choosing the input signal source for a desired application. The signals with highest bandwidth—single neuron action potentials, or spikes—typically are difficult to record for more than a few years after implantation of intracortical electrodes. Fortunately, field potentials recorded within the cortex (local field potentials, LFPs), at its surface (electrocorticograms, ECoG) and at the dural surface (epidural, EFPs) have also been shown to contain significant information about movement. However, the relative performance of these signals has not yet been directly compared. Furthermore, while it is widely postulated, it has not yet been demonstrated that these field potential signals are more durable than spike recordings. The aim of this study was to address both of these questions.
We assessed the offline decoding performance of EFPs, LFPs, and spikes, recorded sequentially, in primary motor cortex (M1) in terms of their ability to decode the target of reaching movements, as well as the endpoint trajectory. We also examined the decoding performance of LFPs on electrodes that are not recording spikes, compared with the performance when they did record spikes. Spikes were still present on some of the other electrodes throughout this study.
We showed that LFPs performed nearly as well as spikes in decoding velocity, and slightly worse in decoding position and in target classification. EFP performance was slightly inferior to that reported for ECoG in humans. We also provided evidence demonstrating that movement-related information in the LFP remains high regardless of the ability to record spikes concurrently on the same electrodes.
This is the first study to provide evidence that LFPs retain information about movement in the absence of spikes on the same electrodes. These results suggest that LFPs may indeed remain informative after spike recordings are lost, thereby providing a robust, accurate signal source for BMIs.
Deep brain stimulation (DBS) in the ventral intermediate nucleus of thalamus (Vim) is known to exert a therapeutic effect on postural and kinetic tremor in patients with essential tremor. For DBS leads implanted near the caudal border of Vim, however, there is an increased likelihood that one will also induce paresthesia side-effects by stimulating neurons within the sensory pathway of the ventral caudal (Vc) nucleus of thalamus. The aim of this computational study was to 1) investigate the neuronal pathways modulated by therapeutic, sub-therapeutic, and paresthesia-inducing DBS settings in three patients with essential tremor, and 2) determine how much better of an outcome could have been achieved had these patients been implanted with a DBS lead containing directionally-segmented electrodes (dDBS). Multi-compartment neuron models of the thalamocortical, cerebellothalamic, and medial lemniscal pathways were first simulated in the context of patient-specific anatomies, lead placements, and programming parameters from three ET patients who had been implanted with Medtronic 3389 DBS leads. The models showed that in these patients, complete suppression of tremor was associated most closely with activating an average of 62% of the cerebellothalamic afferent input into Vim (n=10), while persistent paresthesias were associated with activating 35% of the medial lemniscal tract input into Vc thalamus (n=12). The dDBS lead design demonstrated superior targeting of the cerebello-thalamo-cortical pathway, especially in cases of misaligned DBS leads. Given the close proximity of Vim to Vc thalamus, the models suggest that dDBS will enable clinicians to more effectively sculpt current through and around thalamus in order to achieve a more consistent therapeutic effect without inducing side effects.
Deep brain stimulation; essential tremor; computational modeling; segmented; electrode
The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.
Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68 and 91% within 15 minutes. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.
cortex; electrocorticography; gamma rhythms; human; speech; phoneme
The characteristics of transcranial magnetic stimulation (TMS) pulses influence the physiological effect of TMS. However, available TMS devices allow very limited adjustment of the pulse parameters. We describe a novel TMS device that uses a circuit topology incorporating two energy storage capacitors and two insulated-gate bipolar transistor (IGBT) modules to generate near-rectangular electric field pulses with adjustable number, polarity, duration, and amplitude of the pulse phases. This controllable pulse parameter TMS (cTMS) device can induce electric field pulses with phase widths of 10–310 μs and positive/negative phase amplitude ratio of 1–56. Compared to conventional monophasic and biphasic TMS, cTMS reduces energy dissipation by up to 82% and 57%, and decreases coil heating by up to 33% and 41%, respectively. We demonstrate repetitive TMS trains of 3,000 pulses at frequencies up to 50 Hz with electric field pulse amplitude and width variability less than the measurement resolution (1.7% and 1%, respectively). Offering flexible pulse parameter adjustment and reduced power consumption and coil heating, cTMS enhances existing TMS paradigms, enables novel research applications, and could lead to clinical applications with potentially enhanced potency.
Deep brain stimulation (DBS) is an effective treatment for movement disorders, but the selection of stimulus parameters is a clinical burden and often yields sub-optimal outcomes for patients. Measurement of electrically evoked compound action potentials (ECAPs) during DBS could offer insight into the type and spatial extent of neural element activation and provide a potential feedback signal for the rational selection of stimulus parameters and closed-loop DBS. However, recording ECAPs presents a significant technical challenge due to the large stimulus artefact, which can saturate recording amplifiers and distort short latency ECAP signals. We developed DBS-ECAP recording instrumentation combining commercial amplifiers and circuit elements in a serial configuration to reduce the stimulus artefact and enable high fidelity recording. We used an electrical circuit equivalent model of the instrumentation to understand better the sources of the stimulus artefact and the mechanisms of artefact reduction by the circuit elements. In vitro testing validated the capability of the instrumentation to suppress the stimulus artefact and increase gain by a factor of 1,000 to 5,000 compared to a conventional biopotential amplifier. The distortion of mock ECAP (mECAP) signals was measured across stimulation parameters, and the instrumentation enabled high fidelity recording of mECAPs with latencies of only 0.5 ms for DBS pulse widths of 50 to 100 μs/phase. Subsequently, the instrumentation was used to record in vivo ECAPs, without contamination by the stimulus artefact, during thalamic DBS in an anesthetized cat. The characteristics of the physiological ECAP were dependent on stimulation parameters. The novel instrumentation enables high fidelity ECAP recording and advances the potential use of the ECAP as a feedback signal for the tuning of DBS parameters.
Brain computer interface (BCI) systems have emerged as a method to restore function and enhance communication in motor impaired patients. To date, this has been primarily applied to patients who have a compromised motor outflow due to spinal cord dysfunction, but an intact and functioning cerebral cortex. The cortical physiology associated with movement of the contralateral limb has typically been the signal substrate that has been used as a control signal. While this is an ideal control platform in patients with an intact motor cortex, these signals are lost after a hemispheric stroke. Thus, a different control signal is needed that could provide control capability for a patient with a hemiparetic limb. Previous studies have shown that there is a distinct cortical physiology associated with ipsilateral, or same sided, limb movements. Thus far, it was unknown whether stroke survivors could intentionally and effectively modulate this ipsilateral motor activity from their unaffected hemisphere. Therefore, this study seeks to evaluate whether stroke survivors could effectively utilize ipsilateral motor activity from their unaffected hemisphere to achieve this BCI control.
To investigate this possibility, electroencephalographic (EEG) signals were recorded from four chronic hemispheric stroke patients as they performed (or attempted to perform) real and imagined hand tasks using either their affected or unaffected hand. Following performance of the screening task, the ability of patients to utilize a BCI system was investigated during on-line control of a 1-dimensional control task.
Significant ipsilateral motor signals (associated with movement intentions of the affected hand) in the unaffected hemisphere, which were found to be distinct from rest and contralateral signals, were identified and subsequently used for a simple online BCI control task. We demonstrate here for the first time that EEG signals from the unaffected hemisphere, associated with overt and imagined movements of the affected hand, can enable stroke survivors to control a one-dimensional computer cursor rapidly and accurately. This ipsilateral motor activity enabled users to achieve final target accuracies between 68 and 91% within 15 minutes.
These findings suggest that ipsilateral motor activity from the unaffected hemisphere in stroke survivors could provide a physiological substrate for BCI operation that can be further developed as a long-term assistive device or potentially provide a novel tool for rehabilitation.
Electroencephalography; EEG; Ipsilateral; Motor; Brain Computer Interface; Neuroprosthetics; Stroke; Hemiplegia; BCI
Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.
Urinary retention is the inability to empty the bladder completely, and may result from bladder hypocontractility, increases in outlet resistance, or both. Chronic urinary retention can lead to several urological complications and is often refractory to pharmacologic, behavioral, and surgical treatments. We sought to determine whether electrical stimulation of sensory fibers in the pudendal nerve could engage an augmenting reflex and thereby improve bladder emptying in an animal model of urinary retention. We measured the efficiency of bladder emptying with and without concomitant electrical stimulation of pudendal nerve afferents in urethane anesthetized rats. Voiding efficiency (VE=voided volume/initial volume) was reduced from 72±7% to 29±7% following unilateral transection of the sensory branch of the pudendal nerve (UST) and from 70±5% to 18±4% following bilateral transection (BST). Unilateral electrical stimulation of the proximal transected sensory pudendal nerve during distention-evoked voiding contractions significantly improved VE. Low intensity stimulation at frequencies of 1–50 Hz increased VE to 40–51% following UST and to 39–49% following BST, while high intensity stimulation was ineffective at increasing VE. The increase in VE was mediated by increases in the duration of distention-evoked voiding bladder contractions, rather than increases in contraction amplitude. These results are consistent with an essential role for pudendal sensory feedback in efficient bladder emptying, and raise the possibility that electrical activation of pudendal nerve afferents may provide a new approach to restore efficient bladder emptying in persons with urinary retention.
We report on the development and online testing of an EEG-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects’ modulation of N1 and P3 ERP components measured during single 5-second stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare “oddball” stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly-known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention-modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject’s attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.
brain-computer interface (BCI); auditory event-related potentials (ERP); N100; P300; steady-state evoked potentials; auditory steady-state responses; dichotic listening; auditory attention