scholarly journals FPGA Design Integration of a 32-Microelectrodes Low-Latency Spike Detector in a Commercial System for Intracortical Recordings

Digital ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 34-53
Author(s):  
Mattia Tambaro ◽  
Marta Bisio ◽  
Marta Maschietto ◽  
Alessandro Leparulo ◽  
Stefano Vassanelli
Keyword(s):  
Brain Activity ◽  
Energy Operator ◽  
Low Latency ◽  
Fpga Design ◽  
Real Time Processing ◽  
Single Action ◽  
Iir Filter ◽  
Novel Approach ◽  
Design Integration ◽  
Spike Detector

Numerous experiments require low latencies in the detection and processing of the neural brain activity to be feasible, in the order of a few milliseconds from action to reaction. In this paper, a design for sub-millisecond detection and communication of the spiking activity detected by an array of 32 intracortical microelectrodes is presented, exploiting the real-time processing provided by Field Programmable Gate Array (FPGA). The design is embedded in the commercially available RHS stimulation/recording controller from Intan Technologies, that allows recording intracortical signals and performing IntraCortical MicroStimulation (ICMS). The Spike Detector (SD) is based on the Smoothed Nonlinear Energy Operator (SNEO) and includes a novel approach to estimate an RMS-based firing-rate-independent threshold, that can be tuned to fine detect both the single Action Potential (AP) and Multi Unit Activity (MUA). A low-latency SD together with the ICMS capability, creates a powerful tool for Brain-Computer-Interface (BCI) closed-loop experiments relying on the neuronal activity-dependent stimulation. The design also includes: A third order Butterworth high-pass IIR filter and a Savitzky-Golay polynomial fitting; a privileged fast USB connection to stream the detected spikes to a host computer and a sub-milliseconds latency Universal Asynchronous Receiver-Transmitter (UART) protocol communication to send detections and receive ICMS triggers. The source code and the instruction of the project can be found on GitHub.

Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson’s disease

2020 ◽  
Vol 132 (4) ◽  
pp. 1234-1242 ◽  
Author(s):  
Paolo Belardinelli ◽  
Ramin Azodi-Avval ◽  
Erick Ortiz ◽  
Georgios Naros ◽  
Florian Grimm ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Effects ◽  
Brain Activity ◽  
Dynamic Imaging ◽  
Oscillatory Activity ◽  
Network Information ◽  
Novel Approach ◽  
Electrophysiological Recordings ◽  
Intraoperative Localization

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for symptomatic Parkinson’s disease (PD); the clinical benefit may not only mirror modulation of local STN activity but also reflect consecutive network effects on cortical oscillatory activity. Moreover, STN-DBS selectively suppresses spatially and spectrally distinct patterns of synchronous oscillatory activity within cortical-subcortical loops. These STN-cortical circuits have been described in PD patients using magnetoencephalography after surgery. This network information, however, is currently not available during surgery to inform the implantation strategy.The authors recorded spontaneous brain activity in 3 awake patients with PD (mean age 67 ± 14 years; mean disease duration 13 ± 7 years) during implantation of DBS electrodes into the STN after overnight withdrawal of dopaminergic medication. Intraoperative propofol was discontinued at least 30 minutes prior to the electrophysiological recordings. The authors used a novel approach for performing simultaneous recordings of STN local field potentials (LFPs) and multichannel electroencephalography (EEG) at rest. Coherent oscillations between LFP and EEG sensors were computed, and subsequent dynamic imaging of coherent sources was performed.The authors identified coherent activity in the upper beta range (21–35 Hz) between the STN and the ipsilateral mesial (pre)motor area. Coherence in the theta range (4–6 Hz) was detected in the ipsilateral prefrontal area.These findings demonstrate the feasibility of detecting frequency-specific and spatially distinct synchronization between the STN and cortex during DBS surgery. Mapping the STN with this technique may disentangle different functional loops relevant for refined targeting during DBS implantation.

scholarly journals A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 546
Author(s):  
Zhenni Li ◽  
Haoyi Sun ◽  
Yuliang Gao ◽  
Jiao Wang
Keyword(s):  
Real Time ◽  
Super Resolution ◽  
Depth Map ◽  
Acquisition System ◽  
Depth Image ◽  
Fpga Design ◽  
Depth Sensing ◽  
Residual Network ◽  
Real Time Processing ◽  
Depth Maps

Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).

Non-Invasive Rhythmic Musical-Electric Trigeminal Nerve Stimulation Improves Consciousness in Patients with Disorders of Consciousness

2021 ◽  
Author(s):  
Min Wu ◽  
Benyan Luo ◽  
Yamei Yu ◽  
Xiaoxia Li ◽  
Jian Gao ◽  
...  
Keyword(s):  
Trigeminal Nerve ◽  
Nerve Stimulation ◽  
Brain Activity ◽  
Minimally Conscious State ◽  
Conscious State ◽  
Disorders Of Consciousness ◽  
Non Invasive ◽  
Trigeminal Nerve Stimulation ◽  
Novel Approach ◽  
Minimally Conscious

Abstract Disorders of consciousness (DOC) are often accompanied by aberrant oscillatory neural activity in the thalamus and cerebral cortex. Patient-friendly non-invasive treatments targeting this functional anomaly are still missing. We propose and validate a novel approach that aims to restore DOC patients’ thalamocortical oscillations by combining rhythmic trigeminal-nerve stimulation (TNS) with comodulated musical stimulation. In a cluster-randomized, placebo-controlled, double-blinded, pretest-posttest clinical study, we show that application of this multisensory approach for 40 min on five consecutive days reliably leads to long-lasting improvements in DOC patients’ consciousness (assessed with Coma Recovery Scale-Revised) and oscillatory brain activity at the musical-electric TNS frequency (assessed with electroencephalography and a novel rhythmic auditory-speech paradigm). We found diagnostic improvement in 47% of patients in minimally conscious state and a positive relationship between patients’ behavioral and neural improvements. Based on this evidence we argue that non-invasive musical-electric TNS may serve as an effective patient-friendly DOC treatment and suggest frequency-specific oscillatory neural enhancement as its mode of action.

Envelope Analysis Using the Teager-Kaiser Energy Operator for Condition Monitoring of a Wind Turbine Bearing

2014 ◽  
Vol 564 ◽  
pp. 170-175 ◽  
Author(s):  
Ifigeneia Antoniadou ◽  
Thomas P. Howard ◽  
R.S. Dwyer-Joyce ◽  
Matthew B. Marshall ◽  
Jack Naumann ◽  
...  
Keyword(s):  
Energy Operator ◽  
Rolling Element Bearing ◽  
Envelope Analysis ◽  
Novel Approach ◽  
Rolling Element ◽  
Element Bearing ◽  
The Hilbert Transform ◽  
Frequency Demodulation ◽  
Signal Processing Methods ◽  
Demodulation Method

Different signal processing methods are applied to experimental data obtained from a rolling element bearing rig in order to perform damage detection. Among these methods the Teager-Kaiser energy operator is also proposed as a more novel approach. This energy operator is an amplitude-frequency demodulation method used in this paper as an alternative to the Hilbert Transform in order to perform envelope analysis on the datasets analysed.

scholarly journals Detection of Burst Suppression Patterns in EEG Using Recurrence Rate

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhenhu Liang ◽  
Yinghua Wang ◽  
Yongshao Ren ◽  
Duan Li ◽  
Logan Voss ◽  
...  
Keyword(s):  
Recurrence Rate ◽  
Brain Activity ◽  
Approximate Entropy ◽  
Energy Operator ◽  
Burst Suppression ◽  
Multiple Comparison ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Nonlinear Energy ◽  
Selection Of

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P=0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

scholarly journals Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

2020 ◽  
Author(s):  
Z. Zavecz ◽  
K. Janacsek ◽  
P. Simor ◽  
M.X. Cohen ◽  
D. Nemeth
Keyword(s):  
Functional Connectivity ◽  
Memory Consolidation ◽  
Brain Activity ◽  
Serial Order ◽  
Order Information ◽  
Novel Approach ◽  
Continuous Eeg ◽  
Induced Changes ◽  
Probability Information ◽  
The Brain

AbstractLong-term memory depends on memory consolidation that seems to rely on learning-induced changes in the brain activity. Here, we introduced a novel approach analyzing continuous EEG data to study learning-induced changes as well as trait-like characteristics in brain activity underlying consolidation. Thirty-one healthy young adults performed a learning task and their performance was retested after a short (~1h) delay, that enabled us to investigate the consolidation of serial-order and probability information simultaneously. EEG was recorded during a pre- and post-learning rest period and during learning. To investigate the brain activity associated with consolidation performance, we quantified similarities in EEG functional connectivity of learning and pre-learning rest (baseline similarity) as well as learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes in similarity from baseline to post-learning could indicate learning-induced changes, possibly spontaneous reactivation. Individuals with higher learning-induced changes in alpha frequency connectivity (8.5–9.5 Hz) showed better consolidation of serial-order information. This effect was stronger for more distant channels, highlighting the role of long-range centro-parietal networks underlying the consolidation of serial-order information. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5–3 Hz) and seemed to be dependent on more local, short-range connections. Beyond these associations with learning-induced changes, we also found substantial overlap between the baseline and post-learning similarity and their associations with consolidation performance, indicating that stable (trait-like) differences in functional connectivity networks may also be crucial for memory consolidation.Significance statementWe studied memory consolidation in humans by characterizing how similarity in neural oscillatory patterns during learning and rest periods supports consolidation. Previous studies on similarity focused on learning-induced changes (including reactivation) and neglected the stable individual characteristics that are present over resting periods and learning. Moreover, learning-induced changes are predominantly studied invasively in rodents or with neuroimaging or event-related electrophysiology techniques in humans. Here, we introduced a novel approach that enabled us 1) to reveal both learning-induced changes and trait-like individual differences in brain activity and 2) to study learning-induced changes in humans by analyzing continuous EEG. We investigated the consolidation of two types of information and revealed distinct learning-induced changes and trait-like characteristics underlying the different memory processes.

scholarly journals Feature-reweighted RSA: A method for improving the fit between computational models, brains, and behavior

2021 ◽  
Author(s):  
Philipp Kaniuth ◽  
Martin N. Hebart
Keyword(s):  
Model Selection ◽  
Computational Models ◽  
Model Comparison ◽  
Brain Activity ◽  
Activity Patterns ◽  
Behavioral Data ◽  
Novel Approach ◽  
Improve Model ◽  
And Behavior ◽  
Model Features

AbstractRepresentational Similarity Analysis (RSA) has emerged as a popular method for relating representational spaces from human brain activity, behavioral data, and computational models. RSA is based on the comparison of representational dissimilarity matrices (RDM), which characterize the pairwise dissimilarities of all conditions across all features (e.g. fMRI voxels or units of a model). However, classical RSA treats each feature as equally important. This ‘equal weights’ assumption contrasts with the flexibility of multivariate decoding, which reweights individual features for predicting a target variable. As a consequence, classical RSA may lead researchers to underestimate the correspondence between a model and a brain region and, for model comparison, it may lead to selecting the inferior model. While previous work has suggested that reweighting can improve model selection in RSA, it has remained unclear to what extent these results generalize across datasets and data modalities. To fill this gap, the aim of this work is twofold: First, utilizing a range of publicly available datasets and three popular deep neural networks (DNNs), we seek to broadly test feature-reweighted RSA (FR-RSA) applied to computational models and reveal the extent to which reweighting model features improves RDM correspondence and affects model selection. Second, we propose voxel-reweighted RSA, a novel use case of FR-RSA that reweights fMRI voxels, mirroring the rationale of multivariate decoding of optimally combining voxel activity patterns. We find that reweighting individual model units (1) markedly improves the fit between model RDMs and target RDMs derived from several fMRI and behavioral datasets and (2) affects model selection, highlighting the importance of considering FR-RSA. For voxel-reweighted RSA, improvements in RDM correspondence were even more pronounced, demonstrating the utility of this novel approach. We additionally demonstrate that classical noise ceilings can be exceeded when FR-RSA is applied and propose an updated approach for their computation. Taken together, our results broadly validate the use of FR-RSA for improving the fit between computational models, brain and behavioral data, possibly allowing us to better adjudicate between competing computational models. Further, our results suggest that FR-RSA applied to brain measurement channels could become an important new method to assess the match between representational spaces.

scholarly journals A Low Power 1024-Channels Spike Detector Using Latch-Based RAM for Real-Time Brain Silicon Interfaces

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3068
Author(s):  
Gerardo Saggese ◽  
Antonio Giuseppe Maria Strollo
Keyword(s):  
Random Access ◽  
Energy Operator ◽  
Limited Resources ◽  
The Novel ◽  
Access Memory ◽  
Low Snr ◽  
Noise Estimate ◽  
Good Trade ◽  
Spike Detector ◽  
Silicon Interface

High-density microelectrode arrays allow the neuroscientist to study a wider neurons population, however, this causes an increase of communication bandwidth. Given the limited resources available for an implantable silicon interface, an on-fly data reduction is mandatory to stay within the power/area constraints. This can be accomplished by implementing a spike detector aiming at sending only the useful information about spikes. We show that the novel non-linear energy operator called ASO in combination with a simple but robust noise estimate, achieves a good trade-off between performance and consumption. The features of the investigated technique make it a good candidate for implantable BMIs. Our proposal is tested both on synthetic and real datasets providing a good sensibility at low SNR. We also provide a 1024-channels VLSI implementation using a Random-Access Memory composed by latches to reduce as much as possible the power consumptions. The final architecture occupies an area of 2.3 mm2, dissipating 3.6 µW per channels. The comparison with the state of art shows that our proposal finds a place among other methods presented in literature, certifying its suitability for BMIs.

scholarly journals Novel Approach for EEG Signal Analysis in a Multifractal Paradigm of Motions. Epileptic and Eclamptic Seizures as Scale Transitions

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1024
Author(s):  
Stefan Andrei Irimiciuc ◽  
Andrei Zala ◽  
Dan Dimitriu ◽  
Loredana Maria Himiniuc ◽  
Maricel Agop ◽  
...  
Keyword(s):  
Brain Activity ◽  
Relativity Theory ◽  
Harmonic Mappings ◽  
Eeg Signal ◽  
Neuronal Dynamics ◽  
Novel Approach ◽  
Dynamic Methods ◽  
Time Variations ◽  
Scale Relativity ◽  
The Brain

Two different operational procedures are proposed for evaluating and predicting the onset of epileptic and eclamptic seizures. The first procedure analyzes the electrical activity of the brain (EEG signals) using nonlinear dynamic methods (the time variations of the standard deviation, the variance, the skewness and the kurtosis; the evolution in time of the spatial–temporal entropy; the variations of the Lyapunov coefficients, etc.). The second operational procedure reconstructs any type of EEG signal through harmonic mappings from the usual space to the hyperbolic one using the time homographic invariance of a multifractal-type Schrödinger equation in the framework of the scale relativity theory (i.e., in a multifractal paradigm of motions). More precisely, the explicit differential descriptions of the brain activity in the form of 2 × 2 matrices with real elements disclose, through the in-phase coherences at various scale resolutions (i.e., as scale transitions), the multitude of brain neuronal dynamics, especially sequences of epileptic and eclamptic seizures. These two operational procedures are not mutually exclusive, but rather become complementary, offering valuable information concerning epileptic and eclamptic seizures. In such context, the prediction of epileptic and eclamptic seizures becomes fundamental for patients not responding to medical treatment and also presenting an increased rate of seizure recurrence.

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