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Patent
Neural Inc. | Date: 2016-12-15

A neurotrophic electrode system includes a non-conductive cone, a multi-channel electrode assembly, a dielectric ribbon and a neurite-attracting substance disposed within the cone. The non-conductive cone consists essentially of a material that is stable in a neural environment and defines a cavity. The cavity opens to a small opening at a first end of the cone and opens to a large opening at a second end of the cone that is opposite the first end. The multi-channel electrode assembly includes at least two recording sites that are disposed within the cavity defined by the cone. Each recording site is coupled to a wire that extends out of the large end of the cone. Each wire ends in a connection pad. The dielectric ribbon encases all of the wires but exposes each recording site and exposes each connection pad.


Brumberg J.S.,Boston University | Wright E.J.,Neural Inc. | Andreasen D.S.,Neural Inc. | Andreasen D.S.,Georgia Institute of Technology | And 3 more authors.
Frontiers in Neuroscience | Year: 2011

We conducted a neurophysiological study of attempted speech production in a paralyzed human volunteer using chronic microelectrode recordings. The volunteer suffers from locked-in syndrome leaving him in a state of near-total paralysis, though he maintains good cognition and sensation. In this study, we investigated the feasibility of supervised classification techniques for prediction of intended phoneme production in the absence of any overt movements including speech. Such classification or decoding ability has the potential to greatly improve the qualityof-life of many people who are otherwise unable to speak by providing a direct communicative link to the general community. We examined the performance of three classifiers on a multi-class discrimination problem in which the items were 38 American English phonemes including monophthong and diphthong vowels and consonants. The three classifiers differed in performance, but averaged between 16 and 21% overall accuracy (chance-level is 1/38 or 2.6%). Further, the distribution of phonemes classified statistically above chance was nonuniform though 20 of 38 phonemes were classified with statistical significance for all three classifiers. These preliminary results suggest supervised classification techniques are capable of performing large scale multi-class discrimination for attempted speech production and may provide the basis for future communication prostheses.


Mullen T.,Neural Inc. | Acar Z.A.,SCCN INC | Makeig S.,SCCN INC
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2011

Mapping the dynamics and spatial topography of brain source processes critically involved in initiating and propagating seizure activity is critical for effective epilepsy diagnosis, intervention, and treatment. In this report we analyze neuronal dynamics before and during epileptic seizures using adaptive multivariate autoregressive (VAR) models applied to maximally-independent (ICA) sources of intracranial EEG (iEEG, ECoG) data recorded from subdural electrodes implanted in a human patient for evaluation of surgery for epilepsy. We visualize the spatial distribution of causal sources and sinks of ictal activity on the cortical surface (gyral and sulcal) using a novel combination of multivariate Granger-causal and graph-theoretic metrics combined with distributed source localization by Sparse Bayesian Learning applied to a multi-scale patch basis. This analysis reveals and visualizes distinct, seizure stage-dependent shifts in inter-component spatiotemporal dynamics and connectivity including the clinically-identified epileptic foci. © 2011 IEEE.


Brumberg J.S.,Boston University | Brumberg J.S.,Neural Inc. | Nieto-Castanon A.,StatsANC LLC | Kennedy P.R.,Neural Inc. | And 3 more authors.
Speech Communication | Year: 2010

This paper briefly reviews current silent speech methodologies for normal and disabled individuals. Current techniques utilizing electromyographic (EMG) recordings of vocal tract movements are useful for physically healthy individuals but fail for tetraplegic individuals who do not have accurate voluntary control over the speech articulators. Alternative methods utilizing EMG from other body parts (e.g., hand, arm, or facial muscles) or electroencephalography (EEG) can provide capable silent communication to severely paralyzed users, though current interfaces are extremely slow relative to normal conversation rates and require constant attention to a computer screen that provides visual feedback and/or cueing. We present a novel approach to the problem of silent speech via an intracortical microelectrode brain-computer interface (BCI) to predict intended speech information directly from the activity of neurons involved in speech production. The predicted speech is synthesized and acoustically fed back to the user with a delay under 50 ms. We demonstrate that the Neurotrophic Electrode used in the BCI is capable of providing useful neural recordings for over 4 years, a necessary property for BCIs that need to remain viable over the lifespan of the user. Other design considerations include neural decoding techniques based on previous research involving BCIs for computer cursor or robotic arm control via prediction of intended movement kinematics from motor cortical signals in monkeys and humans. Initial results from a study of continuous speech production with instantaneous acoustic feedback show the BCI user was able to improve his control over an artificial speech synthesizer both within and across recording sessions. The success of this initial trial validates the potential of the intracortical microelectrode-based approach for providing a speech prosthesis that can allow much more rapid communication rates. © 2010 Elsevier B.V. All rights reserved.


In many brain areas, modulations in neuronal firing rates are thought to code information. However, in electrophysiological recording experiments, especially recordings in human patients, the type of information that is coded by a neuron's discharge patterns is often not known, or difficult to determine. From our long experience with chronic recordings in humans, we have come to suspect that such unexplained modulations in firing rates are often due to state changes in the subject. We here present two case studies, with extensive data in one subject to illustrate the point that a change in the subject's emotions, such as sudden fear, surprise, or happiness, may trigger substantial changes in firing rates. © 2011 Psychology Press.


Kennedy P.,Neural Inc.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2012

For development of a long-term, reliable cortical recording electrode, animal and human data support the approach of trapping the brain inside the electrode. © 2012 IEEE.


Kennedy P.,Neural Inc.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

For development of a long-term, reliable cortical recording electrode, animal and human data support the approach of trapping the brain inside the electrode.


Mullen T.,Neural Inc.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

Mapping the dynamics of neural source processes critically involved in initiating and propagating seizure activity is important for effective epilepsy diagnosis, intervention, and treatment. Tracking time-varying shifts in the oscillation modes of an evolving seizure may be useful for both seizure onset detection as well as for improved non-surgical interventions such as microstimulation. In this report we apply a multivariate eigendecomposition method to analyze the time-varying principal oscillation patterns (POPs, or eigenmodes) of maximally-independent (ICA) sources of intracranial EEG data recorded from subdural electrodes implanted in a human patient for evaluation of surgery for epilepsy. Our analysis of a subset of the most dynamically important eigenmodes reveals distinct shifts in characteristic frequency and damping time before, throughout, and following seizures providing insight into the dynamical structure of the system throughout seizure evolution.


A neural sensor includes a substrate defining an array of vias passing therethrough, a plurality of conductive surfaces, a light source, a plurality of groups of quantum dot-based luminescence units and a charge-coupled device (CCD) array. Each via allows a neurite to grow therethrough. Each conductive surface is adjacent to a different via and is electrically coupled thereto. The light source directs light toward the substrate. Each group of quantum dot-based luminescence units extends upwardly from a different one of the conductive surfaces generates light at a different predetermined wavelength when excited with light from the light source. Each luminescence unit changes its luminescence when electrically stimulated by a neural potential generated by a neurite. The CCD detects luminescence from each of the plurality of groups of quantum dot-based luminescence units and generates a signal representative of intensity of each wavelength of light detected.


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