scholarly journals Towards Zero-Latency Neurofeedback

2018 ◽  
Author(s):  
Nikolai Smetanin ◽  
Mikhail A. Lebedev ◽  
Alexei Ossadtchi
Keyword(s):  
Finite Impulse Response ◽  
Brain Activity ◽  
Signal Reconstruction ◽  
Estimation Accuracy ◽  
Feedback Signal ◽  
Brain Functions ◽  
Signal Presentation ◽  
Sensory Modalities ◽  
Least Squares Optimization ◽  
Neurological Conditions

ABSTRACTNeurofeedback (NFB) is a real-time paradigm, where subjects monitor their own brain activity presented to them via one of the sensory modalities: visual, auditory or tactile. NFB has been proposed as an approach to treat neurological conditions and augment brain functions. In many applications, especially in the automatic learning scenario it is important to decrease NFB latency, so that appropriate brain mechanisms can be efficiently engaged. To this end, we propose a novel algorithm that significantly reduces feedback signal presentation in the electroencephalographic (EEG) NFB paradigm. The algorithm is based on the least squares optimization of the finite impulse response (FIR) filter weights and analytic signal reconstruction. In this approach, the trade-off between NFB latency and the accuracy of EEG envelope estimation can be achieved depending on the application needs. Moreover, the algorithm allows to implement predictive NFB by setting latency to negative values while maintaining acceptable envelope estimation accuracy. As such, our algorithm offers significant improvements in cases where subjects need to detect neural events as soon as possible and even in advance.

Mortal Body, Immortal Mind

2012 ◽  
Vol 17 (1) ◽  
pp. 5-26
Author(s):  
Hans Goller
Keyword(s):  
Brain Activity ◽  
The Body ◽  
Activity Data ◽  
Brain Functions ◽  
Body Experiences ◽  
Research Findings ◽  
The Relationship ◽  
Near Death Experiences ◽  
The Brain ◽  
Alternative Theories

Neuroscientists keep telling us that the brain produces consciousness and consciousness does not survive brain death because it ceases when brain activity ceases. Research findings on near-death-experiences during cardiac arrest contradict this widely held conviction. They raise perplexing questions with regard to our current understanding of the relationship between consciousness and brain functions. Reports on veridical perceptions during out-of-body experiences suggest that consciousness may be experienced independently of a functioning brain and that self-consciousness may continue even after the termination of brain activity. Data on studies of near-death-experiences could be an incentive to develop alternative theories of the body-mind relation as seen in contemporary neuroscience.

scholarly journals Compressive sensing-based vibration signal reconstruction using sparsity adaptive subspace pursuit

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879087 ◽  
Author(s):  
Lin Zhou ◽  
Qianxiang Yu ◽  
Daozhi Liu ◽  
Ming Li ◽  
Shukai Chi ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Data Storage ◽  
Wireless Sensors ◽  
Signal Reconstruction ◽  
Estimation Method ◽  
Vibration Signal ◽  
Estimation Accuracy ◽  
Offshore Structure ◽  
Vibration Signals ◽  
Reconstructed Signal

Wireless sensors produce large amounts of data in long-term online monitoring following the Shannon–Nyquist theorem, leading to a heavy burden on wireless communications and data storage. To address this problem, compressive sensing which allows wireless sensors to sample at a much lower rate than the Nyquist frequency has been considered. However, the lower rate sacrifices the integrity of the signal. Therefore, reconstruction from low-dimension measurement samples is necessary. Generally, the reconstruction needs the information of signal sparsity in advance, whereas it is usually unknown in practical applications. To address this issue, a sparsity adaptive subspace pursuit compressive sensing algorithm is deployed in this article. In order to balance the computational speed and estimation accuracy, a half-fold sparsity estimation method is proposed. To verify the effectiveness of this algorithm, several simulation tests were performed. First, the feasibility of subspace pursuit algorithm is verified using random sparse signals with five different sparsities. Second, the synthesized vibration signals for four different compression rates are reconstructed. The corresponding reconstruction correlation coefficient and root mean square error are demonstrated. The high correlation and low error result mean that the proposed algorithm can be applied in the vibration signal process. Third, implementation of the proposed approach for a practical vibration signal from an offshore structure is carried out. To reduce the effect of signal noise, the wavelet de-noising technique is used. Considering the randomness of the sampling, many reconstruction tests were carried out. Finally, to validate the reliability of the reconstructed signal, the structure modal parameters are calculated by the Eigensystem realization algorithm, and the result is only slightly different between original and reconstructed signal, which means that the proposed method can successfully save the modal information of vibration signals.

scholarly journals A simulation-based approach to improve decoded neurofeedback performance

2018 ◽  
Author(s):  
Ethan Oblak ◽  
James Sulzer ◽  
Jarrod Lewis-Peacock
Keyword(s):  
Real Time ◽  
Brain Activity ◽  
Processing Parameters ◽  
Orientation Tuning ◽  
Design Parameters ◽  
Visual Orientation ◽  
Brain Functions ◽  
Simulation Based ◽  
Using Data ◽  
The Right

AbstractThe neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain activity presents a unique ability for precise neuromodulation. Recent applications of this technique, known as decoded neurofeedback, have manipulated fear conditioning, visual perception, confidence judgements and facial preference. However, there has yet to be an empirical justification of the timing and data processing parameters of these experiments. Suboptimal parameter settings could impact the efficacy of neurofeedback learning and contribute to the ‘non-responder’ effect. The goal of this study was to investigate how design parameters of decoded neurofeedback experiments affect decoding accuracy and neurofeedback performance. Subjects participated in three fMRI sessions: two ‘finger localizer’ sessions to identify the fMRI patterns associated with each of the four fingers of the right hand, and one ‘finger finding’ neurofeedback session to assess neurofeedback performance. Using only the localizer data, we show that real-time decoding can be degraded by poor experiment timing or ROI selection. To set key parameters for the neurofeedback session, we used offline simulations of decoded neurofeedback using data from the localizer sessions to predict neurofeedback performance. We show that these predictions align with real neurofeedback performance at the group level and can also explain individual differences in neurofeedback success. Overall, this work demonstrates the usefulness of offline simulation to improve the success of real-time decoded neurofeedback experiments.

scholarly journals Adaptive Parameter Estimation of IIR System-Based WSN Using Multihop Diffusion in Distributed Approach

2020 ◽  
Vol 14 (4) ◽  
pp. 30-41
Author(s):  
Meera Dash ◽  
Trilochan Panigrahi ◽  
Renu Sharma ◽  
Mihir Narayan Mohanty
Keyword(s):  
Impulse Response ◽  
Sensor Node ◽  
Finite Impulse Response ◽  
Lms Algorithm ◽  
Sensor System ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Infinite Impulse Response ◽  
Communication Overhead ◽  
Least Mean Square

Distributed estimation of parameters in wireless sensor networks is taken into consideration to reduce the communication overhead of the network which makes the sensor system energy efficient. Most of the distributed approaches in literature, the sensor system is modeled with finite impulse response as it is inherently stable. Whereas in real time applications of WSN like target tracking, fast rerouting requires, infinite impulse response system (IIR) is used to model and that has been chosen in this work. It is assumed that every sensor node is equipped with IIR adaptive system. The diffusion least mean square (DLMS) algorithm is used to estimate the parameters of the IIR system where each node in the network cooperates themselves. In a sparse WSN, the performance of a DLMS algorithm reduces as the degree of the node decreases. In order to increase the estimation accuracy with a smaller number of iterations, the sensor node needs to share their information with more neighbors. This is feasible by communicating each node with multi-hop nodes instead of one-hop only. Therefore the parameters of an IIR system is estimated in distributed sparse sensor network using multihop diffusion LMS algorithm. The simulation results exhibit superior performance of the multihop diffusion LMS over non-cooperative and conventional diffusion algorithms.

scholarly journals Shifted Dynamics of Glucose Metabolism in the Hippocampus During Aging

2021 ◽  
Vol 13 ◽  
Author(s):  
Ivan Ge ◽  
Gregory Wohl Kirschen ◽  
Xinxing Wang
Keyword(s):  
Young Adult ◽  
Glucose Metabolism ◽  
Home Cage ◽  
Brain Activity ◽  
Tca Cycle ◽  
Aged Mice ◽  
Brain Functions ◽  
The Tca Cycle ◽  
Cage Condition ◽  
Energy Consuming

Aging is a process that adversely affects brain functions such as cognition. Brain activity is highly energy consuming, with glucose serving as the main energy source under normal circumstances. Whether the dynamics of glucose metabolism change with aging is not well understood. This study sought to investigate the activity-dependent changes in glucose metabolism of the mouse hippocampus during aging. In brief, after 1 h of contextual exploration in an enriched environmental condition or 1 h in a familiar home cage condition, metabolites were measured from the hippocampus of both young adult and aged mice with metabolomic profiling. Compared to the home cage context, the enriched contextual exploration condition resulted in changes in the concentration of 11 glucose metabolism-related metabolites in the young adult hippocampus. In contrast, glucose metabolism-related metabolite changes were more apparent in the aged group altered by contextual exploration when compared to those in the home cage condition. Importantly, in the aged groups, several key metabolites involved in glycolysis, the TCA cycle, and ketone body metabolism accumulated, suggesting the less efficient metabolization of glucose-based energy resources. Altogether, the analyses revealed that in the aged mice altered by enriched contextual exploration, the glucose resource seems to be unable to provide enough energy for hippocampal function.

scholarly journals Shared mushroom body circuits underlie visual and olfactory memories in Drosophila

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Katrin Vogt ◽  
Christopher Schnaitmann ◽  
Kristina V Dylla ◽  
Stephan Knapek ◽  
Yoshinori Aso ◽  
...  
Keyword(s):  
Mushroom Body ◽  
Visual Learning ◽  
Dopamine Neurons ◽  
Brain Functions ◽  
Economical Design ◽  
Sensory Modalities ◽  
Brain Center ◽  
Reinforcing Stimuli ◽  
The Brain ◽  
Intrinsic Neurons

In nature, animals form memories associating reward or punishment with stimuli from different sensory modalities, such as smells and colors. It is unclear, however, how distinct sensory memories are processed in the brain. We established appetitive and aversive visual learning assays for Drosophila that are comparable to the widely used olfactory learning assays. These assays share critical features, such as reinforcing stimuli (sugar reward and electric shock punishment), and allow direct comparison of the cellular requirements for visual and olfactory memories. We found that the same subsets of dopamine neurons drive formation of both sensory memories. Furthermore, distinct yet partially overlapping subsets of mushroom body intrinsic neurons are required for visual and olfactory memories. Thus, our results suggest that distinct sensory memories are processed in a common brain center. Such centralization of related brain functions is an economical design that avoids the repetition of similar circuit motifs.

scholarly journals Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain

2020 ◽  
Author(s):  
Sreejan Kumar ◽  
Cameron T. Ellis ◽  
Thomas O’Connell ◽  
Marvin M Chun ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Human Brain ◽  
Language Processing ◽  
Brain Function ◽  
Computational Models ◽  
Brain Activity ◽  
Functional Space ◽  
Semantic Features ◽  
Brain Functions ◽  
Auditory Features ◽  
The Brain

AbstractThe extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model are more widely distributed across the brain than previously acknowledged. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.

scholarly journals Data-driven models reveal the organization of diverse cognitive functions in the brain

2019 ◽  
Author(s):  
Tomoya Nakai ◽  
Shinji Nishimoto
Keyword(s):  
Cognitive Processes ◽  
Brain Activity ◽  
Quantitative Model ◽  
Task Type ◽  
Hierarchical Organization ◽  
Human Cognition ◽  
Cognitive Tasks ◽  
Cortical Organization ◽  
Brain Functions ◽  
Complete Representations

AbstractOur daily life is realized by the complex orchestrations of diverse brain functions including perception, decision, and action. One of the central issues in cognitive neuroscience is to reveal the complete representations underlying such diverse functions. Recent studies have revealed representations of natural perceptual experiences using encoding models1–5. However, there has been little attempt to build a quantitative model describing the cortical organization of multiple active, cognitive processes. Here, we measured brain activity using functional MRI while subjects performed over 100 cognitive tasks, and examined cortical representations with two voxel-wise encoding models6. A sparse task-type encoding model revealed a hierarchical organization of cognitive tasks, their representation in cognitive space, and their mapping onto the cortex. A cognitive factor encoding model utilizing continuous intermediate features by using metadata-based inferences7 predicted brain activation patterns for more than 80 % of the cerebral cortex and decoded more than 95 % of tasks, even under novel task conditions. This study demonstrates the usability of quantitative models of natural cognitive processes and provides a framework for the comprehensive cortical organization of human cognition.

scholarly journals NEW METHOD FOR ESTIMATION OF MULTI-HARMONIC POWER SIGNAL PARAMETERS

2021 ◽  
Vol 25 (4) ◽  
Author(s):  
Predrag Petrovic
Keyword(s):  
Fundamental Frequency ◽  
High Performance ◽  
Finite Impulse Response ◽  
Signal Reconstruction ◽  
Harmonic Signal ◽  
Estimation Procedure ◽  
Simultaneous Estimation ◽  
Spectral Processing ◽  
Unknown Parameters ◽  
Comb Filters

A systematic analytical procedure for simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves was proposed in this paper. In order to reduce complexity in the calculation of unknown parameters, a completely new reduced analytical expression is derived, which enabled fast and precise estimation with a small numerical error. Individual sinusoidal components stand out from the input complex-harmonic signal with the filter with a finite-impulse response (FIR) comb filters. The algorithm that is proposed in the operation is based on the application of partial derivate of the processed and filtered input signal, after which it is performed weighted estimation procedure to better estimate the values size of the fundamental frequency, amplitude and the multi-sinusoid signal phase. The proposed algorithm can be used in the signal reconstruction and estimation procedures, spectral processing, in procedures for the identification of the system that is observed, as well as other important signal processing areas. Through the simulation check, the effectiveness of the proposed algorithm was assessed, which confirmed its high performance.

scholarly journals Forest Height Estimation Based on Constrained Gaussian Vertical Backscatter Model Using Multi-Baseline P-Band Pol-InSAR Data

2018 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Xiaofan Sun ◽  
Bingnan Wang ◽  
Maosheng Xiang ◽  
Shuai Jiang ◽  
Xikai Fu
Keyword(s):  
Least Squares ◽  
Incidence Angle ◽  
Nonlinear Least Squares ◽  
Estimation Accuracy ◽  
Height Estimation ◽  
Low Frequencies ◽  
Function Fitting ◽  
Forest Height ◽  
Vertical Heterogeneity ◽  
Least Squares Optimization

In the case of low frequencies (e.g., P-band) radar observations, the Gaussian Vertical Backscatter (GVB) model, a model that takes into account the vertical heterogeneity of the wave-canopy interactions, can describe the forest vertical backscatter profile (VBP) more accurately. However, the GVB model is highly complex, seriously reducing the inversion efficiency because of a number of variables. Given that concern, this paper proposes a constrained Gaussian Vertical Backscatter (CGVB) model to reduce the complexity of the GVB model by establishing a constraint relationship between forest height and the backscattering vertical fluctuation (BVF) of the GVB model. The CGVB model takes into account the influence of incidence angle on scattering mechanisms. The BVF of VBP described by the CGVB model is expressed with forest height and a polynomial function of incidence angle. In order to build the CGVB model, this paper proposes the supervised learning based on RANSAC (SLBR). The proposed SLBR method used forest height as a prior knowledge to determine the function of incidence angle in the CGVB model. In this process, the Random Sample Consensus (RANSAC) method is applied to perform function fitting. Before building the CGVB model, iterative weighted complex least squares (IWCLS) is employed to extract the required volume coherence. Based on the CGVB model, forest height estimation was obtained by nonlinear least squares optimization. E-SAR P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) data acquired during the BIOSAR 2008 campaign was used to test the performance of the proposed CGVB model. It can be observed that, compared with Random Volume over Ground (RVoG) model, the proposed CGVB model improves the estimation accuracy of the areas with incidence angle less than 0.8 rad and less than 0.6 rad by 28.57 % and 40.35 % , respectively.

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