He worked as a Research Assistant Professor in the Department of Biomedical Engineering at Johns Hopkins University. He is currently a Harvey N Davis Distinguished Assistant Professor in the Department of Biomedical Engineering at Stevens Institute of Technology.
Introduction
Neural recording and stimulation
Synapses can be electrical or chemical, and the excitatory or inhibitory nature of the synapses contributes to information transmission - influx and efflux of sodium and potassium cause the membrane potential to rise and fall rapidly. Multiple layers limit information transmission from the cerebral cortex to the scalp, leading to lower amplitudes of the signal and lower spatial resolution.
Neural signal processing 1 Spike sorting
Temporal and spatial feature extraction
Electrocorticography (ECoG) Implanted electrodes placed in the upper layers of the cerebral cortex Electroencephalography (EEG) Electrodes placed on the surface of. A variety of scaled and finite-length waveforms can be selected according to the shape of the raw neural signals.
Dimensionality reduction
These important features can be extracted from a series of time signals using computational models. The model's coefficients can be viewed as neural features for the subsequent pattern recognition or classification procedure used for real-time decoding or estimation.
Machine learning algorithms
However, the recorded time series signals contain lots of noise that can be filtered using time domain filtering methods. Theoretically, these oscillations can be decomposed with a set of basis functions such as sinusoidal functions using the Fourier transform (FT) into periodic signals.
Applications of neural signal processing
Neurostimulators
Some traditional methods such as LDA, PCA and support vector machine (SVM) are also considered as machine learning algorithms.
Neuroprostheses or human-machine interfaces (HMIs)
Neurological disorders
Conclusion
Diagonal movement overview
We hypothesized that it is specifically diagonal movement planning that plays a crucial role in creating such a modulation of theta and alpha band activity. Thus, as a first step, to examine the effects of diagonal movements, in the present study we measured cerebral activity while performing diagonal and vertical movements (DM and VM, respectively), using EEG recording.
Brain oscillations, movement, and cognition
Specifically, it was observed that activity in the prefrontal cortex increases when a motor task requires maintaining movement information through execution, selecting task-relevant motor information, and inhibiting automated behavior that may compromise motor performance. They showed that this type of motor activity can improve motor skills through modulation of cerebral plasticity over frontoparietal regions, suggesting that improved motor execution is directly related to motor control in the central nervous system.
Aim of this study
From an electrophysiological point of view, pre- and post-EEG recording showed acute and chronic increase in theta and alpha power and coherence over frontal, parietal and temporal regions [9-11]. However, it is difficult to separate and isolate the contribution of diagonal from vertical and horizontal motion, and further studies in which EEG is recorded during the performance of QMT should be performed.
Methods
Participants and design
This rehabilitation protocol also includes diagonal arm movements to promote strength, coordination, functional motor skills and movement initiation [4]. Over the last few years, we studied another motor task involving diagonal movements, called Quadrato Motor Training (QMT) [6–8].
Paradigm
Depicted here are both vertical and diagonal states with the right arm in the top position. Source localization analysis was performed in specific time windows using a data-driven approach, according to what we observed in the time-frequency analysis.
Results
Time-frequency results .1 Theta (4–7 Hz)
Fisher LSD post hoc revealed that vertical and diagonal were significantly different in both Peak1 and Peak2 (p < 0.01) (see Figure 7B). Fisher LSD post hoc revealed that vertical and diagonal were significantly different in both Peak1 and Peak2 (p < 0.01) (see Figure 7C).
Source localization
For P1, significantly higher alpha was found in the posterior cingulate cortex during diagonal movement compared to vertical movement (p < 0.05). For P2, a significantly higher alpha was found in the left inferior parietal lobule during vertical movement compared to diagonal movement (p < 0.05) (see Figure 11B).
Discussion
- Theta: attention, navigation, and computation
- Alpha: internalized attention and movement
- Beta
- Source localization
In fact, we first observed a decrease in alpha power immediately after the start of the return period and a subsequent increase in the same frequency band tied to the end of the movement. The decreased alpha activity observed immediately after the end of the second movement could reflect a decrease in internalized attention.
Conclusions
Low cost sensor-based gait recording and assessment
Gait data was obtained from 20 healthy volunteers using 12 smartphone accelerometers and a software application that allowed for synchronous data collection from the devices and mapping to additional parameters including weight and age. Data collection and methods were approved by the Institutional Ethics Review Board, and participants' open consent was obtained prior to gait and EEG recordings. A total of 40 traces and 120 accelerometer data for gait cycles from the brachium (shoulder), antecubitis (elbow), carpus (wrist), coxal (hip), femur (knee), and tarsus (ankle) were taken for further analysis.
Data processing was based on the time noted by the observer during each gait phase (Figure 1A).
Estimating torque amplitude for each joint
Recent literature has shown that increased beta oscillations during dual support phases of the gait cycle (event-related synchronization, ERS) and are suppressed during the swing and single support phases (event-related desynchronization, ERD) [29-35]. In this chapter, we discuss the use of low-cost mobile phone-based accelerometer sensors to extract and analyze human gait patterns. Mean torque and lower body kinematic gait parameters during stance and swing phases were analyzed to understand how to achieve gait.
Simultaneous recording of EEG with gait and their analysis was done to interpret cortical activity during the stance and swing phases of a gait cycle.
Experimental recording of gait using EEG
Torque variations across the subjects define swing and stance gait phases Joint torques during stance and swing phases were measured using inverse
EEG was measured from four healthy subjects with four cycles per trials, and two trials were registered per test subject. Subjects were without known medical conditions and had normal or corrected-to-normal vision. The spectral bands were estimated for each stance and swing phase of the gait cycle and were averaged across all trials of the four healthy subjects.
The subjects' mean torque amplitude was analyzed and compared during the swing (Figure 2A) and Stand (Figure 2B) phases.
Variations in torque allow to differentiate male and female gait
Temporal and spectral EEG features of gait
Classifying gait sensorial data using different machine learning algorithms Since gait cadence has nonlinear and complex behaviours, extracted gait
Positive amplitude of the motor potential has been observed for the swing phase of the gait cycle in the frontal electrodes (F3), whereas negative amplitude of the motor potential has been observed e.g. the stance phase of the gait cycle in the frontal electrodes (F3). As subject weight increased, increases in joint moments were observed across subjects. By using delta and beta rhythms in the frontoparietal cortical microzones, it may be possible to classify swing and stance.
The chapter is intended for biologists and computer scientists with a keen interest in the theoretical background of the techniques used and is partly conceived as a tutorial.
Brief overview of image segmentation approaches
Such signals are obtained as discrete samples from an underlying physical process which, as an idealization, can be treated as continuous. In many cases, due to its apparent temporal irregularity, it can be considered a completely random process. Since physical measurement is a repetitive process, Gaussian noise is a very common and useful model based on the central limit theorem of probability theory.
The chapter is intended for biologists and data scientists with a keen interest in the theoretical background of the techniques used and is conceived in part as a tutorial.
Mathematical morphology
Historically, the first and simplest segmentation methods are based on global histogram thresholding. It is also important to consider the sampling of operators in the digital domain. Classically, MM theory was developed for uniform homothetic scaling of SEs, but it can be extended to inhomogeneous sets of scaling transformations.
The multi-scale aspects of the theory are due to the scaling of the structural elements.
Geometrical image features
Differential invariants
The theory will be illustrated with the Gaussian derivatives, which, in view of the duality property of Eq. 7), can be used to calculate the image derivatives. Therefore, the zero-crossing of the Laplacian operator can be used to delineate regions, which include patches. The zero crossings form the so-called null space, which can be used to identify objects.
In this regard, different neighborhoods can be considered for the blobs (4-connected, N4) and for the contours (8-connected, N8).
Scale-space theory
The Gaussian scale space
In a typical implementation of the theory, the scale parameter enumerates the space of smooth Gaussian fast-decay test kernels convolved with the digital image. An example is presented in Figure 6, where five unique components of Gaussian jet-2 space are calculated. This approach attempts to overcome some of the limitations of Gaussian scale spaces defined above.
In this way, the solution can be expressed in terms of a convolution with a very general special function - the Wright function [20].
Nonlinear scale spaces
Numerical routines for calculating the Wright function are still not readily available; therefore, the calculations are more easily achieved using fast Fourier transform (FFT) and its inverse, IFFT. Such scale spaces arise in a nonlinear partial differential equation framework, which will be outlined below. The formal properties of some types of scale space have been established by Alvarez et al.
The normal component is antiparallel to the gradient (ie in the normal direction to the isophote curve), while the tangential component is parallel to the isophote curve passing through the point.
Implementation
Discussion
Anisotropic diffusion evolved according to the orthogonal decomposition of the Laplace, s = 3, 3 steps – Tangential direction (center) and along the gradient direction (right). Note the graininess of the right image and its blurry appearance compared to the central image. 1 The installation procedure of the spatial domain filters is simple, which is why only spatial domain filters are included in the public repository.
The challenges lie in the morphological complexity of neurons and glial cells, which overlap with the heterogeneity of the extracellular matrix.
Conclusions and outlook
Appendix
Ranking operations
Some useful Fourier transforms
The main utility of the presented approaches is the construction of a multi-dimensional multi-level feature space, which is then used to learn the characteristic "fingerprints" of the objects of interest. Instead, multiple approaches should be combined and features computed to inform machine learning approaches that can adapt to cell and tissue morphology. Also called "set builder notation" in many sources. The empty set is denoted as ∅.
G~nð Þ ¼f ði2πf sÞne�2π2sf2 (23) In the Fourier domain, the fractional heat kernel is expressed as.
Convolutions and Fourier domain processing
- Empirical mode decomposition (EMD)
- Dataset
- Feature extraction
- Experiments and results
- Discussion
- Conclusions
- Statistical and mathematical background
- Statistical analysis methods
- Fourier transform and STFT
- Analysis and application
- Synaptic structure and mechanisms
- Intrasynaptic factors of the EPSP variability
- Extrasynaptic factors of the EPSP variability
- Discussion
- Conclusion
- Model equations
- Numerical calculations
Some statistical characteristics (mean value, standard deviation and energy) are also tested in this work. Another option assumes that the coding is embedded in the exact time of the jump occurrence. The regulatory effect of GABAergic (primarily GABAA type) synapses is the repolarization of Vm with the so-called inhibitory postsynaptic current (IPSC), which causes an opposite effect on membrane voltage and generates an inhibitory postsynaptic potential (IPSP).
One of the most important targets for zinc action is the N-methyl-D-aspartate (NMDA) receptor binding site with a high affinity for zinc [28]. We will refer to the concentration of the total amount of zinc in the crevice as [Zn] and to the concentration of the unbound or free zinc as [Zn]2+. Therefore, the rate of change of the total concentration of zinc in the crevice is given by.
