9 research outputs found

    Wireless Performance of a Fully Passive Neurorecording Microsystem Embedded in Dispersive Human Head Phantom

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    This paper reports the wireless performance of a biocompatible fully passive microsystem implanted in phantom media simulating the dispersive dielectric properties of the human head, for potential application in recording cortical neuropotentials. Fully passive wireless operation is achieved by means of backscattering electromagnetic (EM) waves carrying 3rd order harmonic mixing products (2f(sub 0) plus or minus f(sub m)=4.4-4.9 GHZ) containing targeted neuropotential signals (fm approximately equal to 1-1000 Hz). The microsystem is enclosed in 4 micrometer thick parylene-C for biocompatibility and has a footprint of 4 millimeters x 12 millimeters x 500 micrometers. Preliminary testing of the microsystem implanted in the lossy biological simulating media results in signal-to-noise ratio's (SNR) near 22 (SNR approximately equal to 38 in free space) for millivolt level neuropotentials, demonstrating the potential for fully passive wireless microsystems in implantable medical applications

    Recent Advances in Neural Recording Microsystems

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    The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

    ANALIZA SZUMOWA KANAŁU ODCZYTOWEGO PRZEZNACZONEGO DO WIELOKANAŁOWYCH UKŁADÓW SCALONYCH DEDYKOWANYCH DO EKSPERYMENTÓW NEUROBIOLOGICZNYCH

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    This paper presents the noise analysis of the main components of the typical recording channel dedicated to neurobiological experiments. Main noise contributors are emphasized and its noise minimization techniques are presented. Noise analysis considers the main recording channel parameters that may be crucial during multichannel recording system design. Authors also present the measurement results of the 8-channel integrated circuit dedicated to recording broad range of the neurobiological signals.W artykule opisano budowę typowego kanału odczytowego wykorzystywanego do rejestracji sygnałów neurobiologicznych. Wskazano główne źródła szumów jakie występują w tego typu układach i zwrócono szczególną uwagę na metody ich minimalizowania. Prowadzona w artykule dyskusja bierze pod uwagę kluczowe parametry wpływające na odniesione do wejścia kanału odczytowego szumy, a mianowicie moc pobieraną przez kanał pomiarowy oraz zajmowaną powierzchnię krzemu. Uwzględnia przy tym typowy kanał odczytowy składający się z przedwzmacniacza napięciowego, układu próbkująco-pamiętającego i przetwornika analogowo-cyfrowego. Pobierana moc oraz zajętość powierzchni są niezmiernie istotne w odniesieniu do budowy wielokanałowego implantowanego układu scalonego przeznaczonego do rejestracji szerokiej gamy sygnałów neurobiologicznych. Artykuł zakończony jest opisem zrealizowanego układu scalonego, którego rozbudowana funkcjonalność pozwala na wykorzystanie go do rejestracji szerokiej gamy sygnałów neurobiologicznych

    Efficient Decoding With Steady-State Kalman Filter in Neural Interface Systems

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    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics.We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5[plus-over-minus sign]0.5 s (mean[plus-over-minus sign]s.d.) . The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25[plus-over-minus sign]3 single units by a factor of 7.0[plus-over-minus sign]0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.National Institutes of Health (U.S.) (Grant R01 DC009899)National Institutes of Health (U.S.) (Grant RC1 HD063931)National Institutes of Health (U.S.) (Grant N01 HD053403)National Institutes of Health (U.S.) (Grant 5K01 NS057389)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385)United States. Dept. of Veterans Affairs (Office of Research and Development, Rehabilitation R&D Service)Massachusetts General Hospital (Deane Institute for Integrated Research on Atrial Fibrillation and Stroke)Doris Duke Charitable FoundationSpaulding Rehabilitation Hospita

    Analog Front-End Circuits for Massive Parallel 3-D Neural Microsystems.

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    Understanding dynamics of the brain has tremendously improved due to the progress in neural recording techniques over the past five decades. The number of simultaneously recorded channels has actually doubled every 7 years, which implies that a recording system with a few thousand channels should be available in the next two decades. Nonetheless, a leap in the number of simultaneous channels has remained an unmet need due to many limitations, especially in the front-end recording integrated circuits (IC). This research has focused on increasing the number of simultaneously recorded channels and providing modular design approaches to improve the integration and expansion of 3-D recording microsystems. Three analog front-ends (AFE) have been developed using extremely low-power and small-area circuit techniques on both the circuit and system levels. The three prototypes have investigated some critical circuit challenges in power, area, interface, and modularity. The first AFE (16-channels) has optimized energy efficiency using techniques such as moderate inversion, minimized asynchronous interface for data acquisition, power-scalable sampling operation, and a wide configuration range of gain and bandwidth. Circuits in this part were designed in a 0.25μm CMOS process using a 0.9-V single supply and feature a power consumption of 4μW/channel and an energy-area efficiency of 7.51x10^15 in units of J^-1Vrms^-1mm^-2. The second AFE (128-channels) provides the next level of scaling using dc-coupled analog compression techniques to reject the electrode offset and reduce the implementation area further. Signal processing techniques were also explored to transfer some computational power outside the brain. Circuits in this part were designed in a 180nm CMOS process using a 0.5-V single supply and feature a power consumption of 2.5μW/channel, and energy-area efficiency of 30.2x10^15 J^-1Vrms^-1mm^-2. The last AFE (128-channels) shows another leap in neural recording using monolithic integration of recording circuits on the shanks of neural probes. Monolithic integration may be the most effective approach to allow simultaneous recording of more than 1,024 channels. The probe and circuits in this part were designed in a 150 nm SOI CMOS process using a 0.5-V single supply and feature a power consumption of only 1.4μW/channel and energy-area efficiency of 36.4x10^15 J^-1Vrms^-1mm^-2.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98070/1/ashmouny_1.pd

    Characterization of cortico-subthalamic networks during deep brain stimulation surgery in Parkinson’s disease

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    Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established symptomatic treatment for Parkinson’s diseases (PD). However, knowledge on local electrophysiological biomarkers within the STN and their cortical connectivity profile is still scarce. Such information would be necessary for optimal positioning of the DBS leads based on PD network pathophysiology. This thesis describes the introduction and exploration of a novel technique for electrophysiological measurements during DBS surgery. Combined electroencephalography (EEG) with stepwise local field potentials recordings during insertion of the DBS lead was performed intraoperatively, thereby, allowing to capture local STN and cortico-subthalamic physiology with high speactral and spatial specificity. Our results revealed that strong beta oscillatory activity in the STN was located more dorsally than the STN-ipsilateral motor network phase coupling; the respective frequency bands were in the low and high beta-band, respectively. Moreover, the spot within the STN, where this STN-cortical phase coupling occurred, correlated highly with the STN spot where the phase of beta oscillations modulated the amplitude of high-frequency oscillations. This STN location was furthermore, characterized by information flowed from the ipsilateral motor cortex to the STN in the high beta-band suggesting a pathologically synchronized network with a direct STN-motor cortex connection via the hyperdirect pathway. Interestingly, the very same STN spot showed a resonance like responses to electrical stimulation suggesting a decoupling of pathologically synchronized STN-motor cortex connectivity during therapeutic DBS. In conclusion, this PhD thesis provides first evidence that macroelectrode recordings with the chronic electrode concurrent with EEG recordings are a reliable method for STN localization during DBS surgery. Additionally, combining LFP and EEG recordings during mapping of STN offered a new way of DBS targeting on the basis of pathological local biomarkers and network activity
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