Journal of Neural Engineering (J Neural Eng)

Publications in this journal

• Article: Direct electrical stimulation of the somatosensory cortex in humans using electrocorticography electrodes: a qualitative and quantitative report.

  L A Johnson, J D Wander, D Sarma, D K Su, E E Fetz, J G Ojemann
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  ABSTRACT: Objective. Recently, electrocorticography-based brain-computer interfaces have been successfully used to translate cortical activity into control signals for external devices. However, the utility of such devices would be greatly enhanced by somatosensory feedback. Direct stimulation of somatosensory cortex evokes sensory perceptions, and is thus a promising option for closing the loop. Before this can be implemented in humans it is necessary to evaluate how changes in stimulus parameters are perceived and the extent to which they can be discriminated. Approach. Electrical stimulation was delivered to the somatosensory cortex of human subjects implanted with electrocorticography grids. Subjects were asked to discriminate between stimuli of different frequency and amplitude as well as to report the qualitative sensations elicited by the stimulation. Main results. In this study we show that in humans implanted with electrocorticography grids, variations in the amplitude or frequency of cortical electrical stimulation produce graded variations in percepts. Subjects were able to reliably distinguish between different stimuli. Significance. These results indicate that direct cortical stimulation is a feasible option for sensory feedback with brain-computer interface devices.
  Journal of Neural Engineering 05/2013; 10(3):036021.
• Article: Electrical stimulation with a penetrating optic nerve electrode array elicits visuotopic cortical responses in cats.

  Yiliang Lu, Yan Yan, Xinyu Chai, Qiushi Ren, Yao Chen, Liming Li
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  ABSTRACT: Objective. A visual prosthesis based on penetrating electrode stimulation within the optic nerve (ON) is a potential way to restore partial functional vision for blind patients. We investigated the retinotopic organization of ON stimulation and its spatial resolution. Approach. A five-electrode array was inserted perpendicularly into the ON or a single electrode was advanced to different depths within the ON (∼1-2 mm behind the eyeball, 13 cats). A sparse noise method was used to map ON electrode position and the visual cortex. Cortical responses were recorded by a 5 × 6 array. The visuotopic correspondence between the retinotopic position of the ON electrode was compared with the visual evoked cortical map and the electrical evoked potentials elicited in response to ON stimulation. Main results. Electrical stimulation with penetrating ON electrodes elicited cortical responses in visuotopographically corresponding areas of the cortex. Stimulation of the temporal side of the ON elicited cortical responses corresponding to the central visual field. The visual field position shifted from the lower to central visual field as the electrode penetrated through the depth of the ON. A spatial resolution of ∼ 2° to 3° within a limited cortical visuotopic representation could be obtained by this approach. Significance. Visuotopic electrical stimulation with a relatively fine spatial resolution can be accomplished using penetrating electrodes implanted at multiple sites and at different depths within the ON just behind the globe. This study also provides useful experimental data for the design of electrode density and the distribution of penetrating ON electrodes for a visual prosthesis.
  Journal of Neural Engineering 05/2013; 10(3):036022.
• Article: Laminar stream of detergents for subcellular neurite damage in a microfluidic device: a simple tool for the study of neuroregeneration.

  Chang Young Lee, Elena V Romanova, Jonathan V Sweedler
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  ABSTRACT: Objective. The regeneration and repair of damaged neuronal networks is a difficult process to study in vivo, leading to the development of multiple in vitro models and techniques for studying nerve injury. Here we describe an approach for generating a well-defined subcellular neurite injury in a microfluidic device. Approach. A defined laminar stream of sodium dodecyl sulfate (SDS) was used to damage selected portions of neurites of individual neurons. The somata and neurites unaffected by the SDS stream remained viable, thereby enabling the study of neuronal regeneration. Main results. By using well-characterized neurons from Aplysia californica cultured in vitro, we demonstrate that our approach is useful in creating neurite damage, investigating neurotrophic factors, and monitoring somata migration during regeneration. Supplementing the culture medium with acetylcholinesterase (AChE) or Aplysia hemolymph facilitated the regeneration of the peptidergic Aplysia neurons within 72 h, with longer (p < 0.05) and more branched (p < 0.05) neurites than in the control medium. After the neurons were transected, their somata migrated; intriguingly, for the control cultures, the migration direction was always away from the injury site (7/7). In the supplemented cultures, the number decreased to 6/8 in AChE and 4/8 in hemolymph, with reduced migration distances in both cases. Significance. The SDS transection approach is simple and inexpensive, yet provides flexibility in studying neuroregeneration, particularly when it is important to make sure there are no retrograde signals from the distal segments affecting regeneration. Neurons are known to not only be under tension but also balanced in terms of force, and the balance is obviously disrupted by transection. Our experimental platform, verified with Aplysia, can be extended to mammalian systems, and help us gain insight into the role that neurotrophic factors and mechanical tension play during neuronal regeneration.
  Journal of Neural Engineering 05/2013; 10(3):036020.
• Article: Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design.

  Ishita Basu, Daniel Graupe, Daniela Tuninetti, Pitamber Shukla, Konstantin V Slavin, Leo Verhagen Metman, Daniel M Corcos
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  ABSTRACT: Objective. We present a proof of concept for a novel method of predicting the onset of pathological tremor using non-invasively measured surface electromyogram (sEMG) and acceleration from tremor-affected extremities of patients with Parkinson's disease (PD) and essential tremor (ET). Approach. The tremor prediction algorithm uses a set of spectral (Fourier and wavelet) and nonlinear time series (entropy and recurrence rate) parameters extracted from the non-invasively recorded sEMG and acceleration signals. Main results. The resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in 4 PD patients and for all 91 trials recorded in 4 ET patients. The predictor achieves a 100% sensitivity for all trials considered, along with an overall accuracy of 85.7% for all ET trials and 80.2% for all PD trials. By using a Pearson's chi-square test, the prediction results are shown to significantly differ from a random prediction outcome. Significance. The tremor prediction algorithm can be potentially used for designing the next generation of non-invasive closed-loop predictive ON-OFF controllers for deep brain stimulation (DBS), used for suppressing pathological tremor in such patients. Such a system is based on alternating ON and OFF DBS periods, an incoming tremor being predicted during the time intervals when DBS is OFF, so as to turn DBS back ON. The prediction should be a few seconds before tremor re-appears so that the patient is tremor-free for the entire DBS ON-OFF cycle and the tremor-free DBS OFF interval should be maximized in order to minimize the current injected in the brain and battery usage.
  Journal of Neural Engineering 05/2013; 10(3):036019.
• Article: Validation of finite element model of transcranial electrical stimulation using scalp potentials: implications for clinical dose.

  Abhishek Datta, Xiang Zhou, Yuzhou Su, Lucas C Parra, Marom Bikson
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  ABSTRACT: Objective. During transcranial electrical stimulation, current passage across the scalp generates voltage across the scalp surface. The goal was to characterize these scalp voltages for the purpose of validating subject-specific finite element method (FEM) models of current flow. Approach. Using a recording electrode array, we mapped skin voltages resulting from low-intensity transcranial electrical stimulation. These voltage recordings were used to compare the predictions obtained from the high-resolution model based on the subject undergoing transcranial stimulation. Main results. Each of the four stimulation electrode configurations tested resulted in a distinct distribution of scalp voltages; these spatial maps were linear with applied current amplitude (0.1 to 1 mA) over low frequencies (1 to 10 Hz). The FEM model accurately predicted the distinct voltage distributions and correlated the induced scalp voltages with current flow through cortex. Significance. Our results provide the first direct model validation for these subject-specific modeling approaches. In addition, the monitoring of scalp voltages may be used to verify electrode placement to increase transcranial electrical stimulation safety and reproducibility.
  Journal of Neural Engineering 05/2013; 10(3):036018.
• Article: On-off control of burst high frequency electrical stimulation to suppress 4-AP induced seizures.

  Chia-Chu Chiang, Chou-Ching K Lin, Ming-Shaung Ju
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  ABSTRACT: Objective. The goal of this study was to investigate, using model simulations and animal experiments, the efficiency and the side effects of burst high frequency stimulation combined with on-off control in seizure suppression. Approach. A modified mathematical hippocampal seizure model was created to provide evidence of the eligibility of this approach. In the experimental setup, two recording electrodes were inserted into bilateral septal CA1 of the hippocampus, and a stimulation electrode was placed on the ventral hippocampal commissure of a rat. After seizures had been induced by 4-aminopyridine treatment, on-off control stimulation was used to suppress the seizures at 20 s intervals. The stimulation time, cumulative charge and post-stimulation suppression were used to assess the effects of burst duration. Main results. The results showed that burst stimulation could suppress the seizures during the control period and burst stimulation of a shorter duration could keep the seizure suppressed with less effort. By decreasing the burst duration, the cumulative stimulation time became shorter, the delivered cumulative charge became lower, and the cumulative time of post-stimulation suppression became longer. Significance. The on-off control stimulation not only prolonged the duration of suppression but also avoided the side effects of the conversion of seizure patterns. In particular, decreasing the specified burst duration increased the efficiency of the burst stimulation.
  Journal of Neural Engineering 05/2013; 10(3):036017.
• Article: Fibre-selective recording from the peripheral nerves of frogs using a multi-electrode cuff.

  Martin Schuettler, Nick Donaldson, Vipin Seetohul, John Taylor
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  ABSTRACT: Objective. We investigate the ability of the method of velocity selective recording (VSR) to determine the fibre types that contribute to a compound action potential (CAP) propagating along a peripheral nerve. Real-time identification of the active fibre types by determining the direction of action potential propagation (afferent or efferent) and velocity might allow future neural prostheses to make better use of biological sensor signals and provide a new and simple tool for use in fundamental neuroscience. Approach. Fibre activity was recorded from explanted Xenopus Laevis frog sciatic nerve using a single multi-electrode cuff that records whole nerve activity with 11 equidistant ring-shaped electrodes. The recorded signals were amplified, delayed against each other with variable delay times, added and band-pass filtered. Finally, the resulting amplitudes were measured. Main Result. Our experiments showed that electrically evoked frog CAP was dominated by two fibre populations, propagating at around 20 and 40 m/s, respectively. The velocity selectivity, i.e. the ability of the system to discriminate between individual populations was increased by applying band-pass filtering. The method extracted an entire velocity spectrum from a 10 ms CAP recording sample in real time. Significance. Unlike the techniques introduced in the 1970s and subsequently, VSR requires only a single nerve cuff and does not require averaging to provide velocity spectral information. This makes it potentially suitable for the generation of highly-selective real-time control-signals for future neural prostheses. In our study, electrically evoked CAPs were analysed and it remains to be proven whether the method can reliably classify physiological nerve traffic.
  Journal of Neural Engineering 05/2013; 10(3):036016.
• Article: An analysis of performance evaluation for motor-imagery based BCI.

  Eoin Thomas, Matthew Dyson, Maureen Clerc
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  ABSTRACT: In recent years, numerous brain-computer interfaces (BCIs) based on motor-imagery have been proposed which incorporate features such as adaptive classification, error detection and correction, fusion with auxiliary signals and shared control capabilities. Due to the added complexity of such algorithms, the evaluation strategy and metrics used for analysis must be carefully chosen to accurately represent the performance of the BCI. In this article, metrics are reviewed and contrasted using both simulated examples and experimental data. Furthermore, a review of the recent literature is presented to determine how BCIs are evaluated, in particular, focusing on the relationship between how the data are used relative to the BCI subcomponent under investigation. From the analysis performed in this study, valuable guidelines are presented regarding the choice of metrics and evaluation strategy dependent upon any chosen BCI paradigm.
  Journal of Neural Engineering 05/2013; 10(3):031001.
• Article: Single trial analysis of slow cortical potentials: a study on anticipation related potentials.

  Gangadhar Garipelli, Ricardo Chavarriaga, José Del R Millán
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  ABSTRACT: Objective. Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signal's spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering. Approach. We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days. Main results. We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers. Significance. We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.
  Journal of Neural Engineering 04/2013; 10(3):036014.
• Article: New stimulation pattern design to improve P300-based matrix speller performance at high flash rate.

  Chantri Polprasert, Pratana Kukieattikool, Tanee Demeechai, James A Ritcey, Siwaruk Siwamogsatham
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  ABSTRACT: Objective. We propose a new stimulation pattern design for the P300-based matrix speller aimed at increasing the minimum target-to-target interval (TTI). Approach. Inspired by the simplicity and strong performance of the conventional row-column (RC) stimulation, the proposed stimulation is obtained by modifying the RC stimulation through alternating row and column flashes which are selected based on the proposed design rules. The second flash of the double-flash components is then delayed for a number of flashing instants to increase the minimum TTI. The trade-off inherited in this approach is the reduced randomness within the stimulation pattern. Main results. We test the proposed stimulation pattern and compare its performance in terms of selection accuracy, raw and practical bit rates with the conventional RC flashing paradigm over several flash rates. By increasing the minimum TTI within the stimulation sequence, the proposed stimulation has more event-related potentials that can be identified compared to that of the conventional RC stimulations, as the flash rate increases. This leads to significant performance improvement in terms of the letter selection accuracy, the raw and practical bit rates over the conventional RC stimulation. Significance. These studies demonstrate that significant performance improvement over the RC stimulation is obtained without additional testing or training samples to compensate for low P300 amplitude at high flash rate. We show that our proposed stimulation is more robust to reduced signal strength due to the increased flash rate than the RC stimulation.
  Journal of Neural Engineering 04/2013; 10(3):036012.
• Article: Spatially restricted electrical activation of retinal ganglion cells in the rabbit retina by hexapolar electrode return configuration.

  Amgad G Habib, Morven A Cameron, Gregg J Suaning, Nigel H Lovell, John W Morley
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  ABSTRACT: Objective. Visual prostheses currently in development aim to restore some form of vision to patients suffering from diseases such as age-related macular degeneration and retinitis pigmentosa. Most rely on electrically stimulating inner retinal cells via electrodes implanted on or near the retina, resulting in percepts of light termed 'phosphenes'. Activation of spatially distinct populations of cells in the retina is key for pattern vision to be produced. To achieve this, the electrical stimulation must be localized, activating cells only in the direct vicinity of the stimulating electrode(s). With this goal in mind, a hexagonal return (hexapolar) configuration has been proposed as an alternative to the traditional monopolar or bipolar return configurations for electrically stimulating the retina. This study investigated the efficacy of the hexapolar configuration in localizing the activation of retinal ganglion cells (RGCs), compared to a monopolar configuration. Approach. Patch-clamp electrophysiology was used to measure the activation thresholds of RGCs in whole-mount rabbit retina to monopolar and hexapolar electrical stimulation, applied subretinally. Main results. Hexapolar activation thresholds for RGCs located outside the hex guard were found to be significantly (>2 fold) higher than those located inside the area of tissue bounded by the hex guard. The hexapolar configuration localized the activation of RGCs more effectively than its monopolar counterpart. Furthermore, no difference in hexapolar thresholds or localization was observed when using cathodic-first versus anodic-first stimulation. Significance. The hexapolar configuration may provide an improved method for electrically stimulating spatially distinct populations of cells in retinal tissue.
  Journal of Neural Engineering 04/2013; 10(3):036013.
• Article: Decoding continuous limb movements from high-density epidural electrode arrays using custom spatial filters.

  A R Marathe, D M Taylor
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  ABSTRACT: Objective. Our goal was to identify spatial filtering methods that would improve decoding of continuous arm movements from epidural field potentials as well as demonstrate the use of the epidural signals in a closed-loop brain-machine interface (BMI) system in monkeys. Approach. Eleven spatial filtering options were compared offline using field potentials collected from 64-channel high-density epidural arrays in monkeys. Arrays were placed over arm/hand motor cortex in which intracortical microelectrodes had previously been implanted and removed leaving focal cortical damage but no lasting motor deficits. Spatial filters tested included: no filtering, common average referencing (CAR), principle component analysis, and eight novel modifications of the common spatial pattern (CSP) algorithm. The spatial filtering method and decoder combination that performed the best offline was then used online where monkeys controlled cursor velocity using continuous wrist position decoded from epidural field potentials in real time. Main results. Optimized CSP methods improved continuous wrist position decoding accuracy by 69% over CAR and by 80% compared to no filtering. Kalman decoders performed better than linear regression decoders and benefitted from including more spatially-filtered signals but not from pre-smoothing the calculated power spectra. Conversely, linear regression decoders required fewer spatially-filtered signals and were improved by pre-smoothing the power values. The 'position-to-velocity' transformation used during online control enabled the animals to generate smooth closed-loop movement trajectories using the somewhat limited position information available in the epidural signals. The monkeys' online performance significantly improved across days of closed-loop training. Significance. Most published BMI studies that use electrocorticographic signals to decode continuous limb movements either use no spatial filtering or CAR. This study suggests a substantial improvement in decoding accuracy could be attained by using our new version of the CSP algorithm that extends the traditional CSP method for use with continuous limb movement data.
  Journal of Neural Engineering 04/2013; 10(3):036015.
• Article: Baseplate for two-stage cranial mounting of BMI connectors.

  Angus J C McMorland, Meel Velliste
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  ABSTRACT: Objective. Intracortical electrode arrays provide the best spatial and temporal resolution signals for brain-machine interfaces. Wireless technologies are being developed to handle this information capacity, but currently the only means to deliver neural information from the implant to a signal processing unit is by a physical connection starting at a skull-mounted connector. The failure rate of the attachment of these connectors is significant. In this study we report an improvement to the traditional connectors. Approach. We have designed and applied an intermediary mounting plate that incorporates several features that provide better, more stable fixation to the skull: (1) wide legs allowing distribution of loading forces and distancing the intracranial screws from the skin interface, (2) a thin shelf to allow early osseointegration, (3) a concave interior to accommodate the curvature of the cranium, and (4) two-stage fixation process providing time for osseointegration prior to the application of loading forces from the connector. Main results. Six baseplates, over four design iterations, have now been tested in three non-human primates. The baseplates are associated with a substantially lower attachment failure rate. Significance. Our baseplate design improves on the current skull-mounted connectors, leading to better outcomes for subjects and fewer catastrophic failure events that can terminate resource intensive intracortical recording experiments.
  Journal of Neural Engineering 04/2013; 10(3):034001.
• Article: Sampled sinusoidal stimulation profile and multichannel fuzzy logic classification for monitor-based phase-coded SSVEP brain-computer interfacing.

  Nikolay V Manyakov, Nikolay Chumerin, Arne Robben, Adrien Combaz, Marijn van Vliet, Marc M Van Hulle
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  ABSTRACT: Objective. The performance and usability of brain-computer interfaces (BCIs) can be improved by new paradigms, stimulation methods, decoding strategies, sensor technology etc. In this study we introduce new stimulation and decoding methods for electroencephalogram (EEG)-based BCIs that have targets flickering at the same frequency but with different phases. Approach. The phase information is estimated from the EEG data, and used for target command decoding. All visual stimulation is done on a conventional (60-Hz) LCD screen. Instead of the 'on/off' visual stimulation, commonly used in phase-coded BCI, we propose one based on a sampled sinusoidal intensity profile. In order to fully exploit the circular nature of the evoked phase response, we introduce a filter feature selection procedure based on circular statistics and propose a fuzzy logic classifier designed to cope with circular information from multiple channels jointly. Main results. We show that the proposed visual stimulation enables us not only to encode more commands under the same conditions, but also to obtain EEG responses with a more stable phase. We also demonstrate that the proposed decoding approach outperforms existing ones, especially for the short time windows used. Significance. The work presented here shows how to overcome some of the limitations of screen-based visual stimulation. The superiority of the proposed decoding approach demonstrates the importance of preserving the circularity of the data during the decoding stage.
  Journal of Neural Engineering 04/2013; 10(3):036011.
• Article: Model-based analysis and design of nerve cuff electrodes for restoring bladder function by selective stimulation of the pudendal nerve.

  Alexander R Kent, Warren M Grill
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  ABSTRACT: Objective. Electrical stimulation of the pudendal nerve (PN) is being developed as a means to restore bladder function in persons with spinal cord injury. A single nerve cuff electrode placed on the proximal PN trunk may enable selective stimulation of distinct fascicles to maintain continence or evoke micturition. The objective of this study was to design a nerve cuff that enabled selective stimulation of the PN. Approach. We evaluated the performance of both flat interface nerve electrode (FINE) cuff and round cuff designs, with a range of FINE cuff heights and number of contacts, as well as multiple contact orientations. This analysis was performed using a computational model, in which the nerve and fascicle cross-sectional positions from five human PN trunks were systematically reshaped within the nerve cuff. These cross-sections were used to create finite element models, with electric potentials calculated and applied to a cable model of a myelinated axon to evaluate stimulation selectivity for different PN targets. Subsequently, the model was coupled to a genetic algorithm (GA) to identify solutions that used multiple contact activation to maximize selectivity and minimize total stimulation voltage. Main results. Simulations did not identify any significant differences in selectivity between FINE and round cuffs, although the latter required smaller stimulation voltages for target activation due to preserved localization of targeted fascicle groups. Further, it was found that a ten contact nerve cuff generated sufficient selectivity for all PN targets, with the degree of selectivity dependent on the relative position of the target within the nerve. The GA identified solutions that increased fitness by 0.7-45.5% over single contact activation by decreasing stimulation of non-targeted fascicles. Significance. This study suggests that using an optimal nerve cuff design and multiple contact activation could enable selective stimulation of the human PN trunk for restoration of bladder function.
  Journal of Neural Engineering 04/2013; 10(3):036010.
• Article: The response of retinal neurons to high-frequency stimulation.

  Changsi Cai, Perry Twyford, Shelley Fried
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  ABSTRACT: Objective. High-rate pulse trains have proven to be effective in cochlear prosthetics and, more recently, have been shown to elicit a wide range of interesting response properties in axons of the peripheral nervous system. Surprisingly, the effectiveness of such trains for use in retinal prostheses has not been explored. Approach. Using cell-attached patch clamp methods, we measured the in vitro response of two rabbit retinal ganglion cell types, OFF-brisk transient (OFF-BT) and ON-OFF directionally selective (DS), to trains of biphasic pulses delivered at 2000 pulses per second (PPS). Main Results. For OFF-BT cells, response onset occurred at ∼20 µA, and maximum response occurred at ∼40 µA. Interestingly, spiking levels decreased for further increases in amplitude. In contrast, DS cells had a spiking onset at ∼25 µA and maintained strong spiking as stimulus amplitude was increased, even at the highest levels tested. Thus, a low-amplitude stimulus train at 2000 PPS (∼25 µA) will activate OFF-BT cells strongly, while simultaneously activating DS cells only weakly. In contrast, a high amplitude train (∼75 µA) will activate DS cells strongly while suppressing responses in OFF-BT cells. Significance. The response differences between cell types suggest some forms of preferential activation may be possible, and further testing is warranted. Further, the scope of the response differences found here suggests activation mechanisms that are more complex than those described in previous studies.
  Journal of Neural Engineering 04/2013; 10(3):036009.
• Article: Dynamically weighted ensemble classification for non-stationary EEG processing.

  Sidath Ravindra Liyanage, Cuntai Guan, Haihong Zhang, Kai Keng Ang, Jianxin Xu, Tong Heng Lee
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  ABSTRACT: Objective. The non-stationary nature of EEG poses a major challenge to robust operation of brain-computer interfaces (BCIs). The objective of this paper is to propose and investigate a computational method to address non-stationarity in EEG classification. Approach. We developed a novel dynamically weighted ensemble classification (DWEC) framework whereby an ensemble of multiple classifiers are trained on clustered features. The decisions from these multiple classifiers are dynamically combined based on the distances of the cluster centres to each test data sample being classified. Main Results. The clusters of the feature space from the second session spanned a different space compared to the clusters of the feature space from the first session which highlights the processes of session-to-session non-stationarity. The session-to-session performance of the proposed DWEC method was evaluated on two datasets. The results on publicly available BCI Competition IV dataset 2A yielded a significantly higher mean accuracy of 81.48% compared to 75.9% from the baseline support vector machine (SVM) classifier without dynamic weighting. Results on the data collected from our twelve in-house subjects yielded a significantly higher mean accuracy of 73% compared to 69.4% from the baseline SVM classifier without dynamic weighting. Significance. The cluster based analysis provides insight into session-to-session non-stationarity in EEG data. The results demonstrate the effectiveness of the proposed method in addressing non-stationarity in EEG data for the operation of a BCI.
  Journal of Neural Engineering 04/2013; 10(3):036007.
• Article: Minimum energy control for in vitro neurons.

  Ali Nabi, Tyler Stigen, Jeff Moehlis, Theoden Netoff
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  ABSTRACT: Objective. To demonstrate the applicability of optimal control theory for designing minimum energy charge-balanced input waveforms for single periodically-firing in vitro neurons from brain slices of Long-Evans rats. Approach. The method of control uses the phase model of a neuron and does not require prior knowledge of the neuron's biological details. The phase model of a neuron is a one-dimensional model that is characterized by the neuron's phase response curve (PRC), a sensitivity measure of the neuron to a stimulus applied at different points in its firing cycle. The PRC for each neuron is experimentally obtained by measuring the shift in phase due to a short-duration pulse injected into the periodically-firing neuron at various phase values. Based on the measured PRC, continuous-time, charge-balanced, minimum energy control waveforms have been designed to regulate the next firing time of the neuron upon application at the onset of an action potential. Main result. The designed waveforms can achieve the inter-spike-interval regulation for in vitro neurons with energy levels that are lower than those of conventional monophasic pulsatile inputs of past studies by at least an order of magnitude. They also provide the advantage of being charge-balanced. The energy efficiency of these waveforms is also shown by performing several supporting simulations that compare the performance of the designed waveforms against that of phase shuffled surrogate inputs, variants of the minimum energy waveforms obtained from suboptimal PRCs, as well as pulsatile stimuli that are applied at the point of maximum PRC. It was found that the minimum energy waveforms perform better than all other stimuli both in terms of control and in the amount of energy used. Specifically, it was seen that these charge-balanced waveforms use at least an order of magnitude less energy than conventional monophasic pulsatile stimuli. Significance. The significance of this work is that it uses concepts from the theory of optimal control and introduces a novel approach in designing minimum energy charge-balanced input waveforms for neurons that are robust to noise and implementable in electrophysiological experiments.
  Journal of Neural Engineering 04/2013; 10(3):036005.
• Article: Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

  Julie Dethier, Paul Nuyujukian, Stephen I Ryu, Krishna V Shenoy, Kwabena Boahen
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  ABSTRACT: Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.
  Journal of Neural Engineering 04/2013; 10(3):036008.
• Article: A capacitive, biocompatible and adhesive electrode for long-term and cap-free monitoring of EEG signals.

  Seung Min Lee, Jeong Hun Kim, Hang Jin Byeon, Yoon Young Choi, Kwang Suk Park, Sang-Hoon Lee
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  ABSTRACT: Objective. Long-term electroencephalogram (EEG) monitoring broadens EEG applications to various areas, but it requires cap-free recording of EEG signals. Our objective here is to develop a capacitive, small-sized, adhesive and biocompatible electrode for the cap-free and long-term EEG monitoring. Approach. We have developed an electrode made of polydimethylsiloxane (PDMS) and adhesive PDMS for EEG monitoring. This electrode can be attached to a hairy scalp and be completely hidden by the hair. We tested its electrical and mechanical (adhesive) properties by measuring voltage gain to frequency and adhesive force using 30 repeat cycles of the attachment and detachment test. Electrode performance on EEG was evaluated by alpha rhythm detection and measuring steady state visually evoked potential and N100 auditory evoked potential. Main results. We observed the successful recording of alpha rhythm and evoked signals to diverse stimuli with high signal quality. The biocompatibility of the electrode was verified and a survey found that the electrode was comfortable and convenient to wear. Significance. These results indicate that the proposed EEG electrode is suitable and convenient for long term EEG monitoring.
  Journal of Neural Engineering 04/2013; 10(3):036006.