EEG Interchannel Causality to Identify Source/Sink Phase Connectivity Patterns in Developmental Dyslexia

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and bandlimited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels’ activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the stablished right-lateralized Theta sampling network anomaly, in line with the assumption of the temporal sampling framework of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

States and sequences of paired subspace ideals and their relationship to patterned brain function

It is found here that the state of a network of coupled ordinary differential equations is partially localizable through a pair of contractive ideal subspaces, chosen from dual complete lattices related to the synchrony and synchronization of cells within the network. The first lattice is comprised of polydiagonal subspaces, corresponding to synchronous activity patterns that arise from functional equivalences of cell receptive fields. This lattice is dual to a transdiagonal subspace lattice ordering subspaces transverse to these network-compatible synchronies. Combinatorial consideration of contracting polydiagonal and transdiagonal subspace pairs yields a rich array of dynamical possibilities for structured networks. After proving that contraction commutes with the lattice ordering, it is shown that subpopulations of cells are left at fixed potentials when pairs of contracting subspaces span the cells' local coordinates - a phenomenon named glyph formation here. Treatment of mappings between paired states then leads to a theory of network-compatible sequence generation. The theory's utility is illustrated with examples ranging from the construction of a minimal circuit for encoding a simple phoneme to a model of the primary visual cortex including high-dimensional environmental inputs, laminar speficicity, spiking discontinuities, and time delays. In this model, glyph formation and dissolution provide one account for an unexplained anomaly in electroencephalographic recordings under periodic flicker, where stimulus frequencies differing by as little as 1 Hz generate responses varying by an order of magnitude in alpha-band spectral power. Further links between coupled-cell systems and neural dynamics are drawn through a review of synchronization in the brain and its relationship to aggregate observables, focusing again on electroencephalography. Given previous theoretical work relating the geometry of visual hallucinations to symmetries in visual cortex, periodic perturbation of the visual system along a putative symmetry axis is hypothesized to lead to a greater concentration of harmonic spectral energy than asymmetric perturbations; preliminary experimental evidence affirms this hypothesis. To conclude, connections drawn between dynamics, sensation, and behavior are distilled to seven hypotheses, and the potential medical uses of the theory are illustrated with a lattice depiction of ketamine xylazine anaesthesia and a reinterpretation of hemifield neglect

A Novel Power-Efficient Wireless Multi-channel Recording System for the Telemonitoring of Electroencephalography (EEG)

This research introduces the development of a novel EEG recording system that is modular, batteryless, and wireless (untethered) with the supporting theoretical foundation in wireless communications and related design elements and circuitry. Its modular construct overcomes the EEG scaling problem and makes it easier for reconfiguring the hardware design in terms of the number and placement of electrodes and type of standard EEG system contemplated for use. In this development, portability, lightweight, and applicability to other clinical applications that rely on EEG data are sought. Due to printer tolerance, the 3D printed cap consists of 61 electrode placements. This recording capacity can however extend from 21 (as in the international 10-20 systems) up to 61 EEG channels at sample rates ranging from 250 to 1000 Hz and the transfer of the raw EEG signal using a standard allocated frequency as a data carrier. The main objectives of this dissertation are to (1) eliminate the need for heavy mounted batteries, (2) overcome the requirement for bulky power systems, and (3) avoid the use of data cables to untether the EEG system from the subject for a more practical and less restrictive setting. Unpredictability and temporal variations of the EEG input make developing a battery-free and cable-free EEG reading device challenging. Professional high-quality and high-resolution analog front ends are required to capture non-stationary EEG signals at microvolt levels. The primary components of the proposed setup are the wireless power transmission unit, which consists of a power amplifier, highly efficient resonant-inductive link, rectification, regulation, and power management units, as well as the analog front end, which consists of an analog to digital converter, pre-amplification unit, filtering unit, host microprocessor, and the wireless communication unit. These must all be compatible with the rest of the system and must use the least amount of power possible while minimizing the presence of noise and the attenuation of the recorded signal A highly efficient resonant-inductive coupling link is developed to decrease power transmission dissipation. Magnetized materials were utilized to steer electromagnetic flux and decrease route and medium loss while transmitting the required energy with low dissipation. Signal pre-amplification is handled by the front-end active electrodes. Standard bio-amplifier design approaches are combined to accomplish this purpose, and a thorough investigation of the optimum ADC, microcontroller, and transceiver units has been carried out. We can minimize overall system weight and power consumption by employing battery-less and cable-free EEG readout system designs, consequently giving patients more comfort and freedom of movement. Similarly, the solutions are designed to match the performance of medical-grade equipment. The captured electrical impulses using the proposed setup can be stored for various uses, including classification, prediction, 3D source localization, and for monitoring and diagnosing different brain disorders. All the proposed designs and supporting mathematical derivations were validated through empirical and software-simulated experiments. Many of the proposed designs, including the 3D head cap, the wireless power transmission unit, and the pre-amplification unit, are already fabricated, and the schematic circuits and simulation results were based on Spice, Altium, and high-frequency structure simulator (HFSS) software. The fully integrated head cap to be fabricated would require embedding the active electrodes into the 3D headset and applying current technological advances to miniaturize some of the design elements developed in this dissertation

Neuronal cell signal analysis: spike detection algorithm development for microelectrode array recordings

Neural signal acquisition and processing techniques are rising trends among wide scientific and commercial areas. Microelectrode array (MEA) technology makes it possible to access and record the electrical activity of neural cells. In this work, human pluripotent stem cell (hPSC) -derived neuronal populations were grown on MEA plates. The activity of the cells was recorded and the research about modern signal processing methods for the neural spike detection was performed. A list of approaches was selected for detailed investigation and the most efficient one was chosen as the new technique for permanent use in the research group. The performed laboratory activities involved cell culture plating, regular medium changes, spontaneous activity recordings and pharmacological manipulations. The data acquired from pharmacological experiments were used for the comparison between the old and new spike detection algorithms in terms of the numbers of the detected events. The Stationary Wavelet Transform-based Teager Energy Operator (SWTTEO) shows prominent performance in the tests with synthetic data. The use of the proposed algorithm in conjunction with the common amplitude-based thresholding enables to lower the threshold and to detect more spikes without an excessive number of false positives. This mode is applicable for real cell data. The detection method was considered superior and was further distributed for the processing of all neural data of the research group which include signals acquired from neuronal populations derived from human embryonic and induced pluripotent stem cells (hESCs and iPSCs) as well as rat cells

Електроенцефалографски сигнали за управљање рачунарским интерфејсом у неурорехабилитацији

Мозак-рачунар интерфејс (МоРИ) системи могу искористити карактеристичне промене мождане активности корисника као контролне сигнале уређаја (рачунара). Различити ментални задаци или спољашњи стимулуси (визуелни, аудитивни или соматосензорни) индукују промене које су кодиране у спонтаној неуралној активности. Генерисане промене се могу идентификовати мерењем можданих сигнала који представљају директну или индиректну меру електричне активности мозга...Brain Computer Interface (BCI) systems can use characteristic brain neural alterations as control signals of the device/computer. Various mental tasks or external stimulation (visual, auditory or somatosensory) induce changes which are embedded in the spontaneous neural activity. Generated changes can be extracted and identified from the brain-signal recordings that represent the (direct or indirect) measure of electrical neural activity..

Novel Tools to Investigate Cortical Activity in Paroxysmal Disorders

This PhD project is at the interface between academic research and industry, and is jointly sponsored by the BBSRC and the industrial partner– Scientifica UK. The goal of this research is the development of new instruments and approaches to monitor and manipulate neuronal network activity in disease states. Firstly, (I) I collaborated with Scientifica to develop and utilise the newly developed Laser Applied Stimulation and Uncaging (LASU) system. The combined usage of the LASU system, alongside novel spatially-targeted channelrhodopsin variants, has al- lowed me to test the limits of single-photon optogenetic stimulation in achieving specific activation of targeted neurons. The presented findings demonstrate that, al- though high-resolution stimulation is achievable in the rodent cortex, single-photon stimulation is insufficient to achieve single-cell resolution stimulation. Secondly, (II) I have combined the high temporal resolution of novel, transparent 16-channel epicortical graphene solution-gated field effect transistor (gSGFET) arrays with the large spatial coverage of bilateral widefield Ca2+ fluorescence imaging; to per- form investigations of the relationship between spreading depolarisation (SD) and cortical seizures in awake head-fixed mouse models of epilepsy. To analyse these complex datasets, I developed a bespoke, semi-automated analysis pipeline to pro- cess the data and probe the seizure-SD relationship. I present the advantages of this dual-modality approach by demonstrating the strengths and weaknesses of each recording method, and how a synergistic approach overcomes the limitations of each technique alone. I utilise widefield imaging to perform systematic classification of SD and seizures both temporally and spatially. Detailed electrophysiological anal- ysis of gSGFET data is then performed on extracted time periods of interest. This work demonstrates the complex interaction between seizures and SD, and proposes several mechanisms describing these interactions. The technological and analytical tools presented here lay the groundwork for insightful and flexible experimental paradigms; altogether, able to probe paroxysmal activity in profound detail