Dissertations / Theses: 'Brain electrical signals'

1

Khodam, Hazrati Mehrnaz [Verfasser]. "On human-machine interfaces based on electrical brain signals / Mehrnaz Khodam Hazrati." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1054365644/34.

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Yao, Bing. "ANALYSIS OF ELECTRICAL AND MAGNETIC BIO-SIGNALS ASSOCIATED WITH MOTOR PERFORMANCE AND FATIGUE." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1140813534.

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Mouradi, Rand. "Wireless Signals and Male Fertility." Cleveland State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=csu1318571631.

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Wheland, David Stanford. "Signal processing methods for brain connectivity." Thesis, University of Southern California, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3610033.

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Although the human brain has been studied for centuries, and the advent of non-invasive brain imaging modalities in the last century in particular has led to significant advances, there is much left to discover. Current neuroscientific theory likens the brain to a highly interconnected network whose behavior can be better understood by determining its network connections. Correlation, coherence, Granger causality, and blind source separation (BSS) are frequently used to infer this connectivity. Here I propose novel methods to improve their inference from neuroimaging data. Correlation and coherence suffer from being unable to differentiate between direct and indirect connectivity. While partial correlation and partial coherence can mitigate this problem, standard methods for calculating these measures result in significantly reduced statistical inference power and require greater numbers of samples. To address these drawbacks I propose novel methods based on a graph pruning algorithm that leverage the connectivity sparsity of the brain to improve the inference of partial correlation and partial coherence. These methods are demonstrated in applications. In particular, partial correlation is explored in both cortical thickness data from structural MR images and resting state data from functional MR images, and partial coherence is explored in invasive electrophysiological measurements in non-human primates. Granger causality is able to differentiate between direct and indirect connectivity by default and like partial coherence is readily applicable to time series. However unlike partial coherence, it uses the temporal ordering implied by the time series to infer a type of causality on the connectivity. Despite its differences, the inference of Granger causality can also be improved using a similar graph pruning algorithm, and I describe such an extension here. The method is also applied to explore electrophysiological interactions in non-human primate data. BSS methods seek to decompose a dataset into a linear mixture of sources such that the sources best match some target property, such as independence. The second order blind identification (SOBI) BSS method has a number of properties particularly well-suited for data on the cerebral cortex and relies on the calculation of lagged covariance matrices. However while these lagged covariance matrices are readily available in one-dimensional data, they are not straightforward to calculate on the two-dimensional cortical manifold on which certain types of neuroimaging data lie. To address this, I propose a method for calculating the covariance matrices on the cortical manifold and demonstrate its application to cortical gray matter thickness and curvature data on the cerebral cortex.

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Purdon, Patrick L. (Patrick Lee) 1974. "Signal processing in functional magnetic resonance imaging (fMRI) of the brain." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50032.

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Li, Kun. "Advanced Signal Processing Techniques for Single Trial Electroencephalography Signal Classification for Brain Computer Interface Applications." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3484.

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Brain Computer Interface (BCI) is a direct communication channel between brain and computer. It allows the users to control the environment without the need to control muscle activity [1-2]. P300-Speller is a well known and widely used BCI system that was developed by Farwell and Donchin in 1988 [3]. The accuracy level of the P300-BCI Speller as measured by the percent of communicated characters correctly identified by the system depends on the ability to detect the P300 event related potential (ERP) component among the ongoing electroencephalography (EEG) signal. Different techniques have been tested to reduce the number of trials needed to be averaged together to allow the reliable detection of the P300 response. Some of them have achieved high accuracies in multiple-trial P300 response detection. However the accuracy of single trial P300 response detection still needs to be improved. In this research, two single trial P300 response classification methods were designed. One is based on independent component analysis (ICA) with blind tracking and the other is based on variance analysis. The purpose of both methods is to detect a chosen character in real-time in the P300-BCI speller. The experimental results demonstrate that the proposed methods dramatically reduce the signal processing time, improve the data communication rate, and achieve overall accuracy of 79.1% for ICA based method and 84.8% for variance analysis based method in single trial P300 response classification task. Both methods showed better performance than that of the single trial stepwise linear discriminant analysis (SWLDA), which has been considered as the most accurate and practical technique working with P300-BCI Speller.

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Demanuele, Charmaine. "Analysis of very low frequency oscillations in electromagnetic brain signal recordings." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/159351/.

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Spontaneous very low frequency oscillations (<0.5 Hz), previously regarded as physiological noise, have of late been increasingly analysed in neuroimaging studies. These slow oscillations, which occur within widely distributed neuroanatomical systems and are unrelated to cardiac and respiratory events, are thought to arise from variations in metabolic demands in the resting brain. However, they also persist during active goal-directed processing, where they predict inter-trial variability in evoked responses and may present a potential source of attention deficit during task performance. This work presents a series of new approaches for investigating: (i) the slow waves in electromagnetic (EM) brain signal recordings, (ii) their contribution in brain function, and (iii) the changes that the slow wave mechanisms undergo during cognitive processing versus resting states. State-of-the-art blind source separation methodologies, including single-channel and spacetime independent component analysis (SC-ICA and ST-ICA), are employed for denoising and dimensionality reduction of multi-channel EM data, and to extract neurophysiologically meaningful brain sources from the recordings. Particularly, magnetoencephalographic (MEG) data of attention-deficit/hyperactivity disorder (ADHD) and control children, and electroencephalographic (EEG) data recorded from healthy adult controls, are analysed. The key analytical challenges and techniques available for the analysis of the slow waves in EM brain signal recordings are discussed, and specific solutions proposed. Core results demonstrate that the inter-trial variability in the amplitude and latency of the eventrelated fields sensory component, the M100 (in MEG), exhibits a slow wave pattern, which is indicative of the intrinsic slow waves modulating underlying brain processes. In a separate study, phase synchronisation in the slow wave band was observed between fronto-central, central and parietal brain regions, and the level of synchrony varied between rest and task conditions, and as a function of ADHD. Furthermore, a new EEG experimental framework and a multistage signal processing methodology have been designed and implemented in order to investigate brain activity during task performance in contrast with that during rest. Here, the brain has been envisaged as an oscillatory system onto which a graded load was imposed to yield a variable output response – the P300. Specifically, results show that the amplitude and phase of the brain sources in the slow wave band share essential similarities during rest and task conditions, but are distinct enough to be classified separately. This is in keeping with the view that the intrinsic slow waves are continuously influencing active brain sources and they are in turn affected by external stimulation. These slow wave variations are also significantly correlated with the level of cognitive attention assessed by performance measures (such as reaction time and error rates). Moreover, the power of the sources in the slow wave band is attenuated during task, and the level of attenuation drops as the task difficulty level is increased, whilst their phase undergoes a change in structure (measured through entropy). These new methodologies, developed for gaining insight into the neurophysiological role of the slow waves, could be used for assessing changes in the brain electrical oscillators as a function of various psychiatric and/or neurobehavioural disorders such as ADHD. This could ultimately lead towards a more scientific (and accurate) approach for the prognosis and diagnosis of these disorders.

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Renfrew, Mark E. "A Comparison of Signal Processing and Classification Methods for Brain-Computer Interface." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1246474708.

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Mountney, John M. "Particle Filtering Programmable Gate Array Architecture for Brain Machine Interfaces." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/140741.

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Electrical Engineering
Ph.D.
Decoding algorithms for brain machine interfaces map neural firing times to the underlying biological output signal through dynamic tuning functions. In order to maintain an accurate estimate of the biological signal, the state of the tuning function parameters must be tracked simultaneously. The evolution of this system state is often estimated by an adaptive filter. Recent work demonstrates that the Bayesian auxiliary particle filter (BAPF) offers improved estimates of the system state and underlying output signal over existing techniques. Performance of the BAPF is evaluated under both ideal conditions and commonly encountered spike detection errors such as missed and false detections and missorted spikes. However, this increase in neuronal signal decoding accuracy is at the expense of an increase in computational complexity. Real-time execution of the BAPF algorithm for neural signals using a sequential processor becomes prohibitive as the number of particles and neurons in the obs
Temple University--Theses

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Dharwarkar, Gireesh. "Using Temporal Evidence and Fusion of Time-Frequency Features for Brain-Computer Interfacing." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/830.

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Brain-computer interfacing (BCI) is a new method of human-machine interaction. It involves the extraction of information from the electroencephalogram (EEG) through signal processing and pattern recognition. The technology has far reaching implications for those with severe physical disabilities and has the potential to enhance machine interaction for the rest of the population. In this work we investigate time-frequency analysis in motor-imagery BCI. We consider two methods for signal analysis: adaptive autoregressive models (AAR) and wavelet transform (WAV). There are three major contributions of this research to single-trial analysis in motor-imagery BCI. First, we improve classification of AAR features over a conventional method by applying a temporal evidence accumulation (TEA) framework. Second, we compare the performance of AAR and WAV under the TEA framework for three subjects and find that WAV outperforms AAR for two subjects. The subject for whom AAR outperforms WAV has the lowest overall signal-to-noise ratio in their BCI output, an indication that the AAR model is more robust than WAV for noisier signals. Lastly, we find empirical evidence of complimentary information between AAR and WAV and propose a fusion scheme that increases the mutual information between the BCI output and classes.

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Bozorgzadeh, Bardia. "Integrated Microsystems for High-Fidelity Sensing and Manipulation of Brain Neurochemistry." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1432223568.

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Janwattanapong, Panuwat. "Connectivity Analysis of Electroencephalograms in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3906.

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This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept. For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number. The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups. By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness.

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Foldes, Stephen Thomas. "Command of a Virtual Neuroprosthesis-Arm with Noninvasive Field Potentials." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1290109568.

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14

Rajaei, Hoda. "Brain Connectivity Networks for the Study of Nonlinear Dynamics and Phase Synchrony in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3882.

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Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy. A nonlinear recurrence-based method is applied to scalp EEG recordings to obtain connectivity maps using phase synchronization attributes. The pairwise connectivity measure is obtained from time domain data without any conversion to the frequency domain. The phase coupling value, which indicates the broadband interdependence of input data, is utilized for the graph theory interpretation of local and global assessment of connectivity activities. The method is applied to the population of pediatric individuals to delineate the epileptic cases from normal controls. A probabilistic approach proved a significant difference between the two groups by successfully separating the individuals with an accuracy of 92.8%. The investigation of connectivity patterns of the interictal epileptic discharges (IED), which were originated from focal and generalized seizures, was resulted in a significant difference ( ) in connectivity matrices. It was observed that the functional connectivity maps of focal IED showed local activities while generalized cases showed global activated areas. The investigation of connectivity maps that resulted from temporal lobe epilepsy individuals has shown the temporal and frontal areas as the most affected regions. In general, functional connectivity measures are considered higher order attributes that helped the delineation of epileptic individuals in the classification process. The functional connectivity patterns of interictal activities can hence serve as indicators of the seizure type and also specify the irritated regions in focal epilepsy. These findings can indeed enhance the diagnosis process in context to the type of epilepsy and effects of relative location of the 3D source of seizure onset on other brain areas.

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15

Malý, Lukáš. "Ovládání invalidního vozíku pomocí klasifikace EEG signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221361.

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Tato diplomová práce představuje koncept elektrického invalidního vozíku ovládaného lidskou myslí. Tento koncept je určen pro osoby, které elektrický invalidní vozík nemohou ovládat klasickými způsoby, jakým je například joystick. V práci jsou popsány čtyři hlavní komponenty konceptu: elektroencefalograf, brain-computer interface (rozhraní mozek-počítač), systém sdílené kontroly a samotný elektrický invalidní vozík. V textu je představena použitá metodologie a výsledky provedených experimentů. V závěru jsou nastíněna doporučení pro budoucí vývoj.

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Ternstedt, Andreas. "Pattern recognition with spiking neural networks and the ROLLS low-power online learning neuromorphic processor." Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-63033.

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Online monitoring applications requiring advanced pattern recognition capabilities implemented in resource-constrained wireless sensor systems are challenging to construct using standard digital computers. An interesting alternative solution is to use a low-power neuromorphic processor like the ROLLS, with subthreshold mixed analog/digital circuits and online learning capabilities that approximate the behavior of real neurons and synapses. This requires that the monitoring algorithm is implemented with spiking neural networks, which in principle are efficient computational models for tasks such as pattern recognition. In this work, I investigate how spiking neural networks can be used as a pre-processing and feature learning system in a condition monitoring application where the vibration of a machine with healthy and faulty rolling-element bearings is considered. Pattern recognition with spiking neural networks is investigated using simulations with Brian -- a Python-based open source toolbox -- and an implementation is developed for the ROLLS neuromorphic processor. I analyze the learned feature-response properties of individual neurons. When pre-processing the input signals with a neuromorphic cochlea known as the AER-EAR system, the ROLLS chip learns to classify the resulting spike patterns with a training error of less than 1 %, at a combined power consumption of approximately 30 mW. Thus, the neuromorphic hardware system can potentially be realized in a resource-constrained wireless sensor for online monitoring applications.However, further work is needed for testing and cross validation of the feature learning and pattern recognition networks.i

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Chandran, Subash K. S. "Analysis of Local Field Potential and Gamma Rhythm Using Matching Pursuit Algorithm." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2771.

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Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. These signals also have transient structures related to spiking or sudden onset of a stimulus, which have a duration not exceeding tens of milliseconds. Further, brain signals are highly non-stationary because both behavioral state and external stimuli can change over a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal. In Chapter 2, we describe a multi-scale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both sharp stimulus-onset transient and sustained gamma rhythm in local field potential recorded from the primary visual cortex. Gamma rhythm (30 to 80 Hz), often associated with high-level cortical functions, has been proposed to provide a temporal reference frame (“clock”) for spiking activity, for which it should have least center frequency variation and consistent phase for extended durations. However, recent studies have proposed that gamma occurs in short bursts and it cannot act as a reference. In Chapter 3, we propose another gamma duration estimator based on matching pursuit (MP) algorithm, which is tested with synthetic brain signals and found to be estimating the gamma duration efficiently. Applying this algorithm to real data from awake monkeys, we show that the median gamma duration is more than 330 ms, which could be long enough to support some cortical computations.

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Bai-Ling, Shu, and 許百靈. "The effects of various pCO2, pO2, and electrical stimulation on functional Magnetic Resonance signals in rat brains." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/45920387071182632938.

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碩士
國立臺灣大學
物理學研究所
87
The methodologies of applying magnetic resonance imaging (MRI) to study functional effects in live animals are widely used today. One of these methodologies is to acquire functional images in rat brains while studying the increase of cerebral blood flow with elevation of CO2 concentration. The MR signals are enhanced by increasing blood flow, which causes slight difference of proton density in cerebral vessels. Another methodology is to study MR signal changes under different levels of blood oxygenation. Since the ability of hemoglobin combining with oxygen is different at various O2 concentrations and blood flow, this produces deoxyhemoglobin. Various amounts of deoxyhemoglobin in blood vessels would have different contributions to MR signal changes. Generally, various levels of blood oxygenation causing MR signal changes are called BOLD effect. Furthermore, neuronal activation would cause local oxygenation alterations in cerebral blood vessels and result in BOLD effect. Therefore the stimulation of nerves would be studied by acquiring functional images.

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Zhu, Quan. "Signal Processing for Time Series of Functional Magnetic Resonance Imaging." Diss., 2008. http://hdl.handle.net/10161/602.

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20

"Scheduling Neural Sensors to Estimate Brain Activity." Master's thesis, 2012. http://hdl.handle.net/2286/R.I.14853.

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abstract: Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might not be noticed by the human eye. An important consideration of these localization algorithms, however, is to try and minimize the overall power consumption in order to improve the study and treatment of brain disorders. This thesis considers the problem of estimating dynamic parameters of neural dipole sources while minimizing the system's overall power consumption; this is achieved by minimizing the number of EEG/MEG measurements sensors without a loss in estimation performance accuracy. As the EEG/MEG measurements models are related non-linearity to the dipole source locations and moments, these dynamic parameters can be estimated using sequential Monte Carlo methods such as particle filtering. Due to the large number of sensors required to record EEG/MEG Measurements for use in the particle filter, over long period recordings, a large amounts of power is required for storage and transmission. In order to reduce the overall power consumption, two methods are proposed. The first method used the predicted mean square estimation error as the performance metric under the constraint of a maximum power consumption. The performance metric of the second method uses the distance between the location of the sensors and the location estimate of the dipole source at the previous time step; this sensor scheduling scheme results in maximizing the overall signal-to-noise ratio. The performance of both methods is demonstrated using simulated data, and both methods show that they can provide good estimation results with significant reduction in the number of activated sensors at each time step.
Dissertation/Thesis
M.S. Electrical Engineering 2012

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Chaturvedi, Vikram. "Low Power and Low Area Techniques for Neural Recording Application." Thesis, 2012. http://hdl.handle.net/2005/3167.

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Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and to elucidate human neurophysiology. The advent of multi-channel micro-electrode arrays has driven the need for electronic store cord neural signals from many neurons. The continuous increase in demand of data from more number of neurons is challenging for the design of an efficient neural recording frontend(NRFE). Power consumption per channel and data rate minimization are two key problems which need to be addressed by next generation of neural recording systems. Area consumption per channel must be low for small implant size. Dynamic range in NRFE can vary with time due to change in electrode-neuron distance or background noise which demands adaptability. In this thesis, techniques to reduce power-per-channel and area-per-channel in a NRFE, via new circuits and architectures, are proposed. An area efficient low power neural LNA is presented in UMC 0.13 μm 1P8M CMOS technology. The amplifier can be biased adaptively from 200 nA to 2 μA , modulating input referred noise from 9.92 μV to 3.9μV . We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. Optimum sizing of the input transistors minimizes the accentuation of the input referred noise of the amplifier. It obviates the need of large input coupling capacitance in the amplifier which saves considerable amount of chip area. In vitro experiments were performed to validate the applicability of the neural LNA in neural recording systems. ADC is another important block in a NRFE. An 8-bit SAR ADC along with the input and reference buffer is implemented in 0.13 μm CMOS technology. The use of ping-pong input sampling is emphasized for multichannel input to alleviate the bandwidth requirement of the input buffer. To reduce the output data rate, the A/D process is only enabled through a proposed activity dependent A/D scheme which ensures that the background noise is not processed. Based on the dynamic range requirement, the ADC resolution is adjusted from 8 to 1 bit at 1 bit step to reduce power consumption linearly. The ADC consumes 8.8 μW from1Vsupply at1MS/s and achieves ENOB of 7.7 bit. The ADC achieves FoM of 42.3 fJ/conversion in 0.13 μm CMOS technology. Power consumption in SARADCs is greatly benefited by CMOS scaling due to its highly digital nature. However the power consumption in the capacitive DAC does not scale as well as the digital logic. In this thesis, two energy-efficient DAC switching techniques, Flip DAC and Quaternary capacitor switching, are proposed to reduce their energy consumption. Using these techniques, the energy consumption in the DAC can be reduced by 37 % and 42.5 % compared to the present state-of-the-art. A novel concept of code-independent energy consumption is introduced and emphasized. It mitigates energy consumption degradation with small input signal dynamic range.

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Dissertations / Theses on the topic 'Brain electrical signals'

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