Set Of Stimulation Parameters Research Articles

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.

Online Bayesian optimization of vagus nerve stimulation.

Objective.In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects.Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve (VN) innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate (HR), making it an ideal candidate for demonstrating the effectiveness of our approach.Main results.The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to VN stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target HRs and optimizing neural B-fiber activations despite high intersubject variability.Significance.An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing.

Effects of transcutaneous auricular vagus nerve stimulation on P300 magnitudes and salivary alpha-amylase during an auditory oddball task

Transcutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive neurostimulation technique that is thought to modulate noradrenergic activity. Previous studies have demonstrated inconsistent effects of taVNS on noradrenergic activity, which is possibly due to insufficient statistical power, suboptimal stimulation parameter settings, and data collection procedures. In this preregistered within-subject experiment, 44 healthy participants received taVNS and sham (earlobe) stimulation during two separate experimental sessions. Stimulation intensity was individually calibrated to the maximum level below pain. During each session, participants received the stimulation continuously ten minutes before an auditory novelty oddball task till the end of the experimental session. The P3b component of the event-related potential served as a marker of phasic noradrenergic activity, whereas P3a magnitude was explored as an index of dopaminergic activity. Salivary alpha-amylase (sAA) was measured as an index of tonic noradrenergic activity before and at the end of the stimulation. The taVNS and sham conditions did not differ in P3a or P3b magnitudes, nor sAA secretion. These findings call into question whether taVNS, administered continuously at high, nonpainful stimulation intensities, reliably augments noradrenergic activity via the vagus nerve.

Model-Based Analysis of Pathway Recruitment During Subthalamic Deep Brain Stimulation

BackgroundSubthalamic deep brain stimulation (DBS) is an established clinical therapy, but an anatomically clear definition of the underlying neural target(s) of the stimulation remains elusive. Patient-specific models of DBS are commonly used tools in the search for stimulation targets, and recent iterations of those models are focused on characterizing the brain connections that are activated by DBS. ObjectiveThe goal of this study was to quantify axonal pathway activation in the subthalamic region from DBS at different electrode locations and stimulation settings. Materials and MethodsWe used an anatomically and electrically detailed computational model of subthalamic DBS to generate recruitment curves for eight different axonal pathways of interest, at three generalized DBS electrode locations in the subthalamic nucleus (STN) (ie, central STN, dorsal STN, posterior STN). These simulations were performed with three levels of DBS electrode localization uncertainty (ie, 0.5 mm, 1.0 mm, 1.5 mm). ResultsThe recruitment curves highlight the diversity of pathways that are theoretically activated with subthalamic DBS, in addition to the dependence of the stimulation location and parameter settings on the pathway activation estimates. The three generalized DBS locations exhibited distinct pathway recruitment curve profiles, suggesting that each stimulation location would have a different effect on network activity patterns. We also found that the use of anodic stimuli could help limit activation of the internal capsule relative to other pathways. However, incorporating realistic levels of DBS electrode localization uncertainty in the models substantially limits their predictive capabilities. ConclusionsSubtle differences in stimulation location and/or parameter settings can impact the collection of pathways that are activated during subthalamic DBS.

Development of a muscle electrical stimulation parameter selection method with an intelligent system

In this study, we propose a methodology for the selection of electrical stimulation parameters for muscle rehabilitation. This methodology uses a particle swarm optimizer to find a set of parameters that maximizes the muscle contraction force during the stimulation session. The optimization makes use of a previously validated mathematical muscle contraction model. To adjust the model to this application, we propose a series of adaptations to the model, such as the inclusion of a muscle fatigue function and different levels of muscle atrophy representations thru parametric variation. We based our model adaptations on the results of parametric sensitivity analysis and the qualitative changes in muscle properties during atrophy and fatigue described in the current literature. With the fatigue function, we successfully recreated the qualitative behavior reported in the literature for muscle fatigue caused by electrical stimulation. The parametric variations proposed following the sensitivity analysis are consistent with the physiological changes occurring during muscle atrophy, and the simulations with the model supported our hypothesis. Finally, we showed that with the model and the particle swarm optimizer, it is possible to find a set of electrical stimulation parameters that maximize the muscle contraction force during the stimulation session.

LINCs Are Vulnerable to Epileptic Insult and Fail to Provide Seizure Control via On-Demand Activation.

Temporal lobe epilepsy (TLE) is notoriously pharmacoresistant, and identifying novel therapeutic targets for controlling seizures is crucial. Long-range inhibitory neuronal nitric oxide synthase-expressing cells (LINCs), a population of hippocampal neurons, were recently identified as a unique source of widespread inhibition in CA1, able to elicit both GABAA-mediated and GABAB-mediated postsynaptic inhibition. We therefore hypothesized that LINCs could be an effective target for seizure control. LINCs were optogenetically activated for on-demand seizure intervention in the intrahippocampal kainate (KA) mouse model of chronic TLE. Unexpectedly, LINC activation at 1 month post-KA did not substantially reduce seizure duration in either male or female mice. We tested two different sets of stimulation parameters, both previously found to be effective with on-demand optogenetic approaches, but neither was successful. Quantification of LINCs following intervention revealed a substantial reduction of LINC numbers compared with saline-injected controls. We also observed a decreased number of LINCs when the site of initial insult (i.e., KA injection) was moved to the amygdala [basolateral amygdala (BLA)-KA], and correspondingly, no effect of light delivery on BLA-KA seizures. This indicates that LINCs may be a vulnerable population in TLE, regardless of the site of initial insult. To determine whether long-term circuitry changes could influence outcomes, we continued testing once a month for up to 6 months post-KA. However, at no time point did LINC activation provide meaningful seizure suppression. Altogether, our results suggest that LINCs are not a promising target for seizure inhibition in TLE.

Parasympathetic control of gastrointestinal motility and cross-branch actions of parasympathetic neuromodulation.

Parasympathetic control of gastrointestinal motility and cross-branch actions of parasympathetic neuromodulation.

Dual-site TMS as a tool to probe effective interactions within the motor network: a review.

Dual-site transcranial magnetic stimulation (ds-TMS) is well suited to investigate the causal effect of distant brain regions on the primary motor cortex, both at rest and during motor performance and learning. However, given the broad set of stimulation parameters, clarity about which parameters are most effective for identifying particular interactions is lacking. Here, evidence describing inter- and intra-hemispheric interactions during rest and in the context of motor tasks is reviewed. Our aims are threefold: (1) provide a detailed overview of ds-TMS literature regarding inter- and intra-hemispheric connectivity; (2) describe the applicability and contributions of these interactions to motor control, and; (3) discuss the practical implications and future directions. Of the 3659 studies screened, 109 were included and discussed. Overall, there is remarkable variability in the experimental context for assessing ds-TMS interactions, as well as in the use and reporting of stimulation parameters, hindering a quantitative comparison of results across studies. Further studies examining ds-TMS interactions in a systematic manner, and in which all critical parameters are carefully reported, are needed.

Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets

Objective. Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians’ experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms. Approach. This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described. Main results. Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (P = 0.002). Significance. The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.

Effect of stimulation parameters on sequential current-steered stimuli in cochlear implants.

Manipulation of cochlear implant (CI) place pitch was carried out with current steering by stimulating two CI electrodes sequentially. The objective was to investigate whether shifts in activated neural populations could be achieved to produce salient pitch differences and to determine which stimulation parameters would be more effective in steering of current. These were the pulse rate and pulse width of electrical stimuli and the distance between the two current-steering electrodes. Nine CI users participated, and ten ears were tested. The pattern of pitch changes was not consistent across listeners, but the data suggest that individualized selection of stimulation parameters may be used to effect place pitch changes with sequential current steering. Individual analyses showed that pulse width generally had little influence on the effectiveness of current steering with sequential stimuli, while more salient place pitch shifts were often achieved at wider electrode spacing or when the stimulation pulse rate was the same as that indicated on the clinical MAP (the set of stimulation parameters) of the listener. Results imply that current steering may be used in CIs that allow only sequential stimulation to achieve place pitch manipulation.

Subcutaneous Stimulation as Add-on Therapy to Spinal Cord Stimulation in Patients With Persistent Spinal Pain Syndrome Significantly Increases the Total Electrical Charge per Second: Aspects on Stimulation Parameters and Energy Requirements of the Implanted Neurostimulators

ObjectiveIn our previous multicenter randomized controlled trial, we demonstrated the clinical effectiveness of peripheral nerve field stimulation (PNFS) as add-on therapy to spinal cord stimulation (SCS) for the treatment of chronic back pain in patients with persistent spinal pain syndrome (PSPS) or failed back surgery syndrome (FBSS). To our knowledge, no previous study has investigated the effect of PNFS as an add-on to SCS on the energy consumption of the implanted neurostimulators. Therefore, in this study, we compared the specific stimulation parameters and energy requirements of a previously unreported group of patients with only SCS with those of a group of patients with SCS and add-on PNFS. We also investigated differences that might explain the need for PNFS in the treatment of chronic low back pain. Materials and MethodsWe analyzed 75 patients with complete sets of stimulation parameters, with 21 patients in the SCS-only group and 54 patients in the SCS + PNFS group. Outcome measures were average visual analog scale score, SCS parameters (voltage, frequency, and pulse width), SCS charge per second, and total charge per second. We analyzed baseline characteristics and differences between and within groups over time. ResultsBoth groups had comparable patient characteristics at baseline and showed a significant decrease in back and leg pain. SCS charge per second did not significantly differ between the groups at baseline or at 12 months. The total charge per second was significantly higher in the active SCS + PNFS group than in the SCS-only group at baseline; in the SCS + PNFS group, this persisted for up to 12 months, and the SCS charge per second and total charge per second increased significantly over time. ConclusionsOur results show that add-on PNFS increases the total charge per second compared with SCS alone, as expected. However, further research is needed because our results do not directly explain why some patients require add-on PNFS to treat low back pain.

Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters

ObjectivesSubthalamic nucleus (STN) deep brain stimulation (DBS) programming in patients with Parkinson disease (PD) may be challenging, especially when using segmented leads. In this study, we integrated a previously validated probabilistic STN sweet spot into a commercially available software to evaluate its predictive value for clinically effective DBS programming. Materials and MethodsA total of 14 patients with PD undergoing bilateral STN DBS with segmented leads were included. A nonlinear co-registration of a previously defined probabilistic sweet spot onto the manually segmented STN was performed together with lead reconstruction and tractography of the corticospinal tract (CST) in each patient. Contacts were ranked (level and direction), and corresponding effect and side-effect thresholds were predicted based on the overlap of the volume of activated tissue (VTA) with the sweet spot and CST. Image-based findings were correlated with postoperative clinical testing results during monopolar contact review and chronic stimulation parameter settings used after 12 months. ResultsImage-based contact prediction showed high interrater reliability (Cohen kappa 0.851–0.91). Image-based and clinical ranking of the most efficient ring level and direction of stimulation were matched in 72% (95% CI 57.0–83.3) and 65% (95% CI 44.9–81.2), respectively, across the whole cohort. The mean difference between the predicted and clinically observed effect thresholds was 0.79 ± 0.69 mA (p = 0.72). The median difference between the predicted and clinically observed side-effect thresholds was −0.5 mA (p < 0.001, Wilcoxon paired signed rank test). ConclusionsIntegration of a probabilistic STN functional sweet spot into a surgical programming software shows a promising capability to predict the best level and directional contact(s) as well as stimulation settings in DBS for PD and could be used to optimize programming with segmented lead technology. This integrated image-based programming approach still needs to be evaluated on a bigger data set and in a future prospective multicenter cohort.

The trade-off between pulse duration and power in optical excitation of midbrain dopamine neurons approximates Bloch’s law

Optogenetic experiments reveal functional roles of specific neurons. However, functional inferences have been limited by widespread adoption of a restricted set of stimulation parameters. Broader exploration of the parameter space can deepen insight into the mapping between selective neural activity and behavior. In this way, characteristics of the activated neural circuit, such as temporal integration, can be inferred. Our objective was to determine whether an equal-energy principle accounts for the interaction of pulse duration and optical power in optogenetic excitation. Six male TH::Cre rats worked for optogenetic (ChannelRhodopsin-2) stimulation of VTA dopamine neurons. We used a within-subject design to describe the trade-off between pulse duration and optical power in determining reward seeking. Parameters were customized for each subject based on behavioral effectiveness. Within a useful range of powers (~12.6–31.6 mW) the product of optical power and pulse duration required to produce a given level of reward seeking was roughly constant. Such reciprocity is consistent with Bloch’s law, which posits an equal-energy principle of temporal summation over short durations in human vision. The trade-off between pulse duration and power broke down at higher powers. Thus, optical power and duration can be adjusted reciprocally for brief durations and lower powers, and power can be substituted for pulse duration to scale the region of excitation in behavioral optogenetic experiments. The findings demonstrate the utility of within-subject and trade-off designs in optogenetics and of parameter adjustment based on functional endpoints instead of physical properties of the stimulation.

Human intracranial recordings reveal distinct cortical activity patterns during invasive and non-invasive vagus nerve stimulation

Vagus nerve stimulation (VNS) is being used increasingly to treat a wide array of diseases and disorders. This growth is driven in part by the putative ability to stimulate the nerve non-invasively. Despite decades of use and a rapidly expanding application space, we lack a complete understanding of the acute effects of VNS on human cortical neurophysiology. Here, we investigated cortical responses to sub-perceptual threshold cervical implanted (iVNS) and transcutaneous auricular (taVNS) vagus nerve stimulation using intracranial neurophysiological recordings in human epilepsy patients. To understand the areas that are modulated by VNS and how they differ depending on invasiveness and stimulation parameters, we compared VNS-evoked neural activity across a range of stimulation modalities, frequencies, and amplitudes. Using comparable stimulation parameters, both iVNS and taVNS caused subtle changes in low-frequency power across broad cortical networks, which were not the same across modalities and were highly variable across participants. However, within at least some individuals, it may be possible to elicit similar responses across modalities using distinct sets of stimulation parameters. These results demonstrate that both invasive and non-invasive VNS cause evoked changes in activity across a set of highly distributed cortical networks that are relevant to a diverse array of clinical, rehabilitative, and enhancement applications.

StimVision v2: Examples and Applications in Subthalamic Deep Brain Stimulation for Parkinson’s Disease

ObjectiveSubthalamic deep brain stimulation (DBS) is an established therapy for Parkinson’s disease. Connectomic DBS modeling is a burgeoning subfield of research aimed at characterizing the axonal connections activated by DBS. This article describes our approach and methods for evolving the StimVision software platform to meet the technical demands of connectomic DBS modeling in the subthalamic region. Materials and MethodsStimVision v2 was developed with Visualization Toolkit (VTK) libraries and integrates four major components: 1) medical image visualization, 2) axonal pathway visualization, 3) electrode positioning, and 4) stimulation calculation. ResultsStimVision v2 implemented two key technological advances for connectomic DBS analyses in the subthalamic region. First was the application of anatomical axonal pathway models to patient-specific DBS models. Second was the application of a novel driving-force method to estimate the response of those axonal pathways to DBS. Example simulations with directional DBS electrodes and clinically defined therapeutic DBS settings are presented to demonstrate the general outputs of StimVision v2 models. ConclusionsStimVision v2 provides the opportunity to evaluate patient-specific axonal pathway activation from subthalamic DBS using anatomically detailed pathway models and electrically detailed electric field distributions with interactive adjustment of the DBS electrode position and stimulation parameter settings.

Deep brain stimulation reduces evoked potentials with a dual time course in freely moving rats: Potential neurophysiological basis for intermittent as an alternative to continuous stimulation.

Deep brain stimulation (DBS) is an increasingly applied treatment for various neuropsychiatric disorders including drug-resistant epilepsy, and it may be optimized by rationalizing the stimulation protocol based on increased knowledge of its mechanism of action. We evaluated the effects of minutes to hours of hippocampal DBS on hippocampal evoked potentials (EPs) and local field potentials (LFPs) in freely moving male rats to further investigate some of the previously proposed mechanisms of action. Hippocampal high-frequency (130Hz) DBS was administered for 0, 1, or 6min every 10min for 160min. Stimulation parameter settings were similar to those that had previously been shown to reduce seizures in epileptic rats. EPs and LFPs were recorded in the stimulation-free intervals. We investigated both the immediate temporary effects of 1 or 6min of DBS and the effects of 160min of intermittent DBS. Input specificity was investigated by using two different stimulation electrodes. Relatively low DBS intensities corresponding to only 1.8% of the intensity evoking a maximum EP were required to prevent unintended seizure occurrence in healthy rats. Both 1 and 6min of DBS caused input-specific short-lasting (<60 s) reductions (5%-7%) of the field excitatory postsynaptic potential (fEPSP) slope (P=.005). We observed longer-lasting, input-specific EP reductions during the 160min intermittent DBS, with statistically significant reductions (3%-4%) of the fEPSP slope (P=.009-.018). The LFP spectrogram remained unaltered. Deep brain stimulation induced both acute temporary effects compatible with axonal block and/or synaptic depression, and longer-lasting potentially cumulative EP reductions, suggesting the involvement of homeostatic plasticity or long-term depression. This dual time course may parallel the different temporal patterns of improvement observed in clinical trials. The longer-lasting reductions provide a potential neurophysiological basis for the use of intermittent DBS-as typically used in epilepsy patients-as an alternative to continuous DBS.

Specific vagus nerve stimulation parameters alter serum cytokine levels in the absence of inflammation

BackgroundElectrical stimulation of peripheral nerves is a widely used technique to treat a variety of conditions including chronic pain, motor impairment, headaches, and epilepsy. Nerve stimulation to achieve efficacious symptomatic relief depends on the proper selection of electrical stimulation parameters to recruit the appropriate fibers within a nerve. Recently, electrical stimulation of the vagus nerve has shown promise for controlling inflammation and clinical trials have demonstrated efficacy for the treatment of inflammatory disorders. This application of vagus nerve stimulation activates the inflammatory reflex, reducing levels of inflammatory cytokines during inflammation.MethodsHere, we wanted to test whether altering the parameters of electrical vagus nerve stimulation would change circulating cytokine levels of normal healthy animals in the absence of increased inflammation. To examine this, we systematically tested a set of electrical stimulation parameters and measured serum cytokine levels in healthy mice.ResultsSurprisingly, we found that specific combinations of pulse width, pulse amplitude, and frequency produced significant increases of the pro-inflammatory cytokine tumor necrosis factor (TNF), while other parameters selectively lowered serum TNF levels, as compared to sham-stimulated mice. In addition, serum levels of the anti-inflammatory cytokine interleukin-10 (IL-10) were significantly increased by select parameters of electrical stimulation but remained unchanged with others.ConclusionsThese results indicate that electrical stimulation parameter selection is critically important for the modulation of cytokines via the cervical vagus nerve and that specific cytokines can be increased by electrical stimulation in the absence of inflammation. As the next generation of bioelectronic therapies and devices are developed to capitalize on the neural regulation of inflammation, the selection of nerve stimulation parameters will be a critically important variable for achieving cytokine-specific changes.

Cognitive Task Performance During Titration Predicts Deep Brain Stimulation Treatment Efficacy: Evidence From a Case Study.

Device titration is a major challenge when using deep brain stimulation (DBS) to treat behavioral disorders. Unlike in movement disorders, there is no reliable real-time clinical feedback for changes in complex behaviors resulting from DBS. Here, a female patient receiving DBS of the nucleus accumbens for the treatment of morbid obesity underwent cognitive testing via the flanker task alongside traditional methods of device titration. One set of stimulation parameters administered during titration resulted in acute cognitive improvement (p = 0.033) and increased frontal engagement as measured by electroencephalography (left anterior: p = 0.007, right anterior: p = 0.005) relative to DBS-OFF. The same parameters resulted in the most weight-loss during long-term continuous stimulation (47.8 lbs lost in 129 days) compared to the results of other stimulation settings. Diffusion tensor imaging analyses showed increased connectivity to dorsal attention networks and decreased connectivity to the default mode network for optimal parameters (p < 0.01). Our results provide evidence that targeted cognitive testing is a potentially useful tool for capturing acute effects of DBS stimulation during titration and predicting long-term treatment outcomes.Clinical Trial Registrationwww.ClinicalTrials.gov, identifier: NCT01512134.

High-speed brain-computer communication interface based on code-modulated visual evoked potentials

Brain-computer interface (BCI) technologies are actively used in clinical practice to communicate with patients unable to speak and move. Such interfaces imply identifying potentials evoked by stimuli meaningful to the patient in his/her EEG and interpreting these potentials into inputs for the communication software. The stimuli can take form of highlighted letters on a screen, etc. This study aimed to investigate EEG indicators and assess the command input performance of a promising type of BCI utilizing the so-called code-modulated visual evoked potentials (CVEP) appearing in response to a certain sequence of highlights of the desired letter. The operation of the interface was studied on 15 healthy volunteers. During the experiments, we changed the speed of stimuli demonstration and inverted the order of flashing. It was established that the optimal speed of stimulation significantly depends on individual traits of the person receiving the stimuli, and inversion of their sequence does not affect operation of the interface. The median accuracy of selection of commands was as follows: 1 s stimulation cycle mode — 0.96 and 0.95 (information transfer rate 142 and 141 bit per minute); 2 s stimulation cycle mode — 1; 0.5 s cycle — 0.33. The evoked potentials were most expressed at the Oz site. It was assumed that CVEP-based brain-computer interfaces can be optimized through individualization of the set of stimulation parameters.

Tinnitus Treatment Using Noninvasive and Minimally Invasive Electric Stimulation: Experimental Design and Feasibility.

Noninvasive transcranial or minimally invasive transtympanic electric stimulation may offer a desirable treatment option for tinnitus because it can activate the deafferented auditory nerve fibers while posing little to no risk to hearing. Here, we built a flexible research interface to generate and control accurately charge-balanced current stimulation as well as a head-mounted instrument capable of holding a transtympanic electrode steady for hours. We then investigated the short-term effect of a limited set of electric stimulation parameters on tinnitus in 10 adults with chronic tinnitus. The preliminary results showed that 63% of conditions of electric stimulation produced some degree of tinnitus reduction, with total disappearance of tinnitus in six subjects in response to at least one condition. The present study also found significant side effects such as visual, tactile, and even pain sensations during electric stimulation. In addition to masking and residual inhibition, neuroplasticity is likely involved in the observed tinnitus reduction. To translate the present electric stimulation into a safe and effective tinnitus treatment option, we need to optimize stimulation parameters that activate the deafferented auditory nerve fibers and reliably suppress tinnitus, with minimal side effects and tolerable sensations. Noninvasive or minimally invasive electric stimulation can be integrated with sound therapy, invasive cochlear implants, or other forms of coordinated stimulation to provide a systematic strategy for tinnitus treatment or even a cure.