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Relevant bibliographies by topics / Brain electrical signals

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Academic literature on the topic 'Brain electrical signals'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Brain electrical signals"

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Zhuang, Qiu Hui, Guo Jun Liu, Xiu Hua Fu, and San Qiang Wang. "Brain Electrical Signal Digital Processing System Design." Applied Mechanics and Materials 278-280 (January 2013): 958–61. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.958.

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Through the amplification system to extract the brain electrical signal, although already can be displayed, but is not clear; in addition, the analog signal into the computer to carry on the analysis, also must pass to convert analog signals to digital signals (A/D converter).Therefore the need for further use of digital processing, this paper adopts the digital way, on brain electrical analog signal digital filter, through the 40Hz low-pass filter and 50Hz filter, get clear, stable signal, to achieve the design objective.

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Thiagarajan, T. "Interpreting Electrical Signals from the Brain." Acta Physica Polonica B 49, no. 12 (2018): 2095. http://dx.doi.org/10.5506/aphyspolb.49.2095.

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Durka, P. J., J. Z. ygierewicz, and K. J. Blinowska. "Time-Frequency Analysis of Brain Electrical Activity – Adaptive Approximations." Methods of Information in Medicine 43, no. 01 (2004): 70–73. http://dx.doi.org/10.1055/s-0038-1633838.

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Summary Objectives: We present an approach to time-frequency analysis of bioelectrical signals. Methods: The method relays on the decomposition of the signal into a set of waveforms that have good localization both in time and in frequency. The waveforms belong to a highly redundant set of functions – allowing for a very accurate description of signal components. Results: Properties of the method are illustrated by simulations and applications to EEG. Conclusion: The presented method delivers a common formalism suitable for describing both gross statistical properties of structures present in bioelectrical signals, as well as microstructure of chosen phenomena.

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Charchekhandra, Barbara. "The Reading and Analyzing Of The Brain Electrical Signals To Execute a Control Command and Move an Automatic Arm." Pure Mathematics for Theoretical Computer Science 1, no. 1 (2023): 08–16. http://dx.doi.org/10.54216/pmtcs.010101.

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In this research, brain computer interface was designed to record brain signals and connecting it to labview via Bluetooth technology. Brain signals were acquired for 10 persons according to a specific protocol designed for the purpose of study then analyze those signals in order to form statistical data and study the changes of frequency and amplitude depending on the opening and closing of the eye. Accordingly a signal processing algorithm was developed to obtain the frequency and amplitude of the brain signal and compare those values during the opening and closing of the eye and determine the discrimination values. After the analysis and the classification steps, the algorithm was developed to send the command data to the Arduino chip and linked to the signal processing algorithm. As a result, the brain signal was marked at the blink and processed to execute a control command to trigger a relay and move an automatic arm or perform any action.

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Garg, Malika. "Methods for the Analysis of EEG signals: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 873–76. http://dx.doi.org/10.22214/ijraset.2021.38072.

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Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

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Bashashati, Ali, Mehrdad Fatourechi, Rabab K. Ward, and Gary E. Birch. "A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals." Journal of Neural Engineering 4, no. 2 (March 27, 2007): R32—R57. http://dx.doi.org/10.1088/1741-2560/4/2/r03.

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Naresh, B., S. Rambabu, and D. Khalandar Basha. "ARM Controller and EEG based Drowsiness Tracking and Controlling during Driving." International Journal of Reconfigurable and Embedded Systems (IJRES) 6, no. 3 (May 28, 2018): 127. http://dx.doi.org/10.11591/ijres.v6.i3.pp127-132.

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<span>This paper discussed about EEG-Based Drowsiness Tracking during Distracted Driving based on Brain computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity commands through controller device in real time. With these signals from brain in mat lab signals spectrum analyzed and estimates driver concentration and meditation conditions. If there is any nearest vehicles to this vehicle a voice alert given to driver for alert. And driver going to sleep gives voice alert for driver using voice chip. And give the information about traffic signal indication using RFID. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human feelings, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) is used to receive the raw data from brain wave sensor and it is used to extract and process the signal using Mat lab platform. The nearest vehicles information is information is taken through ultrasonic sensors and gives voice alert. And traffic signals condition is detected through RF technology.</span>

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Markovinović, Ivan, Miroslav Vrankić, and Saša Vlahinić. "Removal of eye-blink artifacts from EEG signal." Engineering review 40, no. 2 (April 1, 2020): 101–11. http://dx.doi.org/10.30765/er.40.2.11.

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Electroencephalography (EEG) is well known method of recording electrical brain activity with electrodes placed along the scalp. One of the challenging tasks in this field is the removal of electrical signals that are not related to brain activity.In this paper, an algorithm for the removal of the EEG signals corresponding to the eye blink artifacts is presented. The presented algorithm is based on ADJUST artifact removing tool, which uses independent component analysis (ICA) for signal decomposition. For every signal component returned by the ICA algorithm, temporal-spatial features are calculated, upon which every independent component is classified as artifact or non-artifact, and removed accordingly.

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Chandran, Kalyana Sundaram, and T. Kiruba Angeline. "Identification of Disease Symptoms Using Taste Disorders in Electroencephalogram Signal." Journal of Computational and Theoretical Nanoscience 17, no. 5 (May 1, 2020): 2051–56. http://dx.doi.org/10.1166/jctn.2020.8848.

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A Brain Computer Interface (BCI) is the one which converts the activity of the brain signals into useful and understandable signal. Brain computer interface is also called as Neural-Control Interface (NCI), Direct Neural Interface (DCI) or Brain Interface Machine (BMI). Electroencephalogram (EEG) based brain computer interfaces (BCI) is the technique used to measure the activity of the brain. Electroencephalography (EEG) is a brain wave monitoring and diagnosis. It is the measurement of electrical activity of the brain from the scalp. Taste sensations are important for our body to digest food. Identification of disease symptoms is based on the inhibition of different types of taste and by testing them to find the normality and abnormality of taste. The information is used in detection of disorder such as Parkinson’s disease etc. It is a source of reimbursement for better clinical diagnosis. Our brain continuously produces electrical signals when it operates. Those signals are measured with the equipment called Neurosky Mindwave Mobile headset. It is used to collect the real time brain signal samples. Neurosky is the equipment used in proposed work. Here the pre-processing technique is executed with median filtering. Feature extraction and classification is done with Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). It increases the performance accuracy. The SVM classification accuracy achieved by this work is 90%. The sensitivity achieved is higher and the specificity is about 80%. We can able to predict the taste disorders using this methodology.

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Hirai, Yasuharu, Eri Nishino, and Harunori Ohmori. "Simultaneous recording of fluorescence and electrical signals by photometric patch electrode in deep brain regions in vivo." Journal of Neurophysiology 113, no. 10 (June 2015): 3930–42. http://dx.doi.org/10.1152/jn.00005.2015.

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Despite its widespread use, high-resolution imaging with multiphoton microscopy to record neuronal signals in vivo is limited to the surface of brain tissue because of limited light penetration. Moreover, most imaging studies do not simultaneously record electrical neural activity, which is, however, crucial to understanding brain function. Accordingly, we developed a photometric patch electrode (PME) to overcome the depth limitation of optical measurements and also enable the simultaneous recording of neural electrical responses in deep brain regions. The PME recoding system uses a patch electrode to excite a fluorescent dye and to measure the fluorescence signal as a light guide, to record electrical signal, and to apply chemicals to the recorded cells locally. The optical signal was analyzed by either a spectrometer of high light sensitivity or a photomultiplier tube depending on the kinetics of the responses. We used the PME in Oregon Green BAPTA-1 AM-loaded avian auditory nuclei in vivo to monitor calcium signals and electrical responses. We demonstrated distinct response patterns in three different nuclei of the ascending auditory pathway. On acoustic stimulation, a robust calcium fluorescence response occurred in auditory cortex (field L) neurons that outlasted the electrical response. In the auditory midbrain (inferior colliculus), both responses were transient. In the brain-stem cochlear nucleus magnocellularis, calcium response seemed to be effectively suppressed by the activity of metabotropic glutamate receptors. In conclusion, the PME provides a powerful tool to study brain function in vivo at a tissue depth inaccessible to conventional imaging devices.

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Dissertations / Theses on the topic "Brain electrical signals"

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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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Books on the topic "Brain electrical signals"

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S, Gevins A., and Rémond Antoine, eds. Methods of analysis of brain electrical and magnetic signals. Amsterdam: Elsevier, 1987.

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1962-, Laguna Pablo, ed. Bioelectrical signal processing in cardiac and neurological applications. Amsterdam: Elsevier Academic Press, 2005.

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Gevins, A. S. Methods of Analysis of Brain Electrical and Magnetic Signals. Elsevier Publishing Company, 1987.

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Seeck, Margitta, L. Spinelli, Jean Gotman, and Fernando H. Lopes da Silva. Combination of Brain Functional Imaging Techniques. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0046.

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Several tools are available to map brain electrical activity. Clinical applications focus on epileptic activity, although electric source imaging (ESI) and electroencephalography-coupled functional magnetic resonance imaging (EEG–fMRI) are also used to investigate non-epileptic processes in healthy subjects. While positron-emission tomography (PET) reflects glucose metabolism, strongly linked with synaptic activity, and single-photon-emission computed tomography (SPECT) reflects blood flow, fMRI (BOLD) signals have a hemodynamic component that is a surrogate signal of neuronal (synaptic) activity. The exact interpretation of BOLD signals is not completely understood; even in unifocal epilepsy, more than one region of positive or negative BOLD is often observed. Co-registration of medical images is essential to answer clinical questions, particularly for presurgical epilepsy evaluations. Multimodal imaging can yield information about epileptic foci and underlying networks. Co-registering MRI, PET, SPECT, fMRI, and ESI (or magnetic source imaging) provides information to estimate the epileptogenic zone and can help optimize surgical results.

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Gage, Greg, and Tim Marzullo. How Your Brain Works. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/12429.001.0001.

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Discover the hidden electrical world inside your nervous system using DIY, hands-on experiments, for all ages. No MD or PhD required! The workings of the brain are mysterious: What are neural signals? What do they mean? How do our senses really sense? How does our brain control our movements? What happens when we meditate? Techniques to record signals from living brains were once thought to be the realm of advanced university labs... but not anymore! This book allows anyone to participate in the discovery of neuroscience through hands-on experiments that record the hidden electrical world beneath our skin and skulls. In How Your Brain Works, neuroscientists Greg Gage and Tim Marzullo offer a practical guide—accessible and useful to readers from middle schoolers to college undergraduates to curious adults—for learning about the brain through hands-on experiments. Armed with some DIY electrodes, readers will get to see what brain activity really looks like through simple neuroscience experiments. Written by two neuroscience researchers who invented open-source techniques to record signals from neurons, muscles, hearts, eyes, and brains, How Your Brain Works includes more than forty-five experiments to gain a deeper understanding of your brain. Using a homemade scientific instrument called a SpikerBox, readers can see how fast neural signals travel by recording electrical signals from an earthworm. Or, turning themselves into subjects, readers can strap on some electrode stickers to detect the nervous system in their own bodies. Each chapter begins by describing some phenomenology of a particular area of neuroscience, then guides readers step-by-step through an experiment, and concludes with a series of open-ended questions to inspire further investigation. Some experiments use invertebrates (such as insects), and the book provides a thoughtful framework for the ethical use of these animals in education. How Your Brain Works offers fascinating reading for students at any level, curious readers, and scientists interested in using electrophysiology in their research or teaching. Example Experiments How fast do signals travel down a neuron? The brain uses electricity. . . but do neurons communicate as fast as lightning inside our bodies? In this experiment you will make a speed trap for spikes! Can we really enhance our memories during sleep? Strap on a brainwave-reading sweatband and test the power of cueing up and strengthening memories while you dream away! Wait, that's my number! Ever feel that moment of excitement when you see your number displayed while waiting for an opening at the counter? In this experiment, you will peer into your brainwaves to see what happens when the unexpected occurs and how the brain gets your attention. Using hip hop to talk to the brain. Tired of simply “reading” the electricity from the brain? Would you like to “write” to the nervous system as well? In this experiment you will use a smartphone and hack a headphone cable to see how brain stimulators (used in treating Parkinson's disease) really work. How long does it take the brain to decide? Using simple classroom rulers and a clever technique, readers can determine how long it takes the brain to make decisions.

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Campagnola, Luke, and Paul Manis. Patch Clamp Recording in Brain Slices. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0001.

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Patch clamp recording in brain slices allows unparalleled access to neuronal membrane signals in a system that approximates the in-vivo neural substrate while affording greater control of experimental conditions. In this chapter we discuss the theory, methodology, and practical considerations of such experiments including the initial setup, techniques for preparing and handling viable brain slices, and patching and recording signals. A number of practical and technical issues faced by electrophysiologists are also considered, including maintaining slice viability, visualizing and identifying healthy cells, acquiring reliable patch seals, amplifier compensation features, hardware configuration, sources of electrical noise and table vibration, as well as basic data analysis issues and some troubleshooting tips.

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Amzica, Florin, and Fernando H. Lopes da Silva. Cellular Substrates of Brain Rhythms. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0002.

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The purpose of this chapter is to familiarize the reader with the basic electrical patterns of the electroencephalogram (EEG). Brain cells (mainly neurons and glia) are organized in multiple levels of intricate networks. The cellular membranes are semipermeable media between extracellular and intracellular solutions, populated by ions and other electrically charged molecules. This represents the basis of electrical currents flowing across cellular membranes, further generating electromagnetic fields that radiate to the scalp electrodes, which record changes in the activity of brain cells. This chapter presents these concepts together with the mechanisms of building up the EEG signal. The chapter discusses the various behavioral conditions and neurophysiological mechanisms that modulate the activity of cells leading to the most common EEG patterns, such as the cellular interactions for alpha, beta, gamma, slow, delta, and theta oscillations, DC shifts, and some particular waveforms such as sleep spindles and K-complexes and nu-complexes.

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Wadman, Wytse J., and Fernando H. Lopes da Silva. Biophysical Aspects of EEG and MEG Generation. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0004.

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This chapter reviews the essential physical principles involved in the generation of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. The general laws governing the electrophysiology of neuronal activity are analyzed within the formalism of the Maxwell equations that constitute the basis for understanding electromagnetic fields in general. Three main topics are discussed. The first is the forward problem: How can one calculate the electrical field that results from a known configuration of neuronal sources? The second is the inverse problem: Given an electrical field as a function of space and time mostly recorded at the scalp (EEG/MEG), how can one reconstruct the underlying generators at the brain level? The third is the reverse problem: How can brain activity be modulated by external electromagnetic fields with diagnostic and/or therapeutic objectives? The chapter emphasizes the importance of understanding the common biophysical framework concerning these three main topics of brain electrical and magnetic activities.

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Sutter, Raoul, Peter W. Kaplan, and Donald L. Schomer. Historical Aspects of Electroencephalography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0001.

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Electroencephalography (EEG), a dynamic real-time recording of electrical neocortical brain activity, began in the 1600s with the discovery of electrical phenomena and the concept of an “action current.” The galvanometer was introduced in the 1800s and the first bioelectrical observations of human brain signals were made in the 1900s. Certain EEG patterns were associated with brain disorders, increasing the clinical and scientific use of EEG. In the 1980s, technical advances allowed EEGs to be digitized and linked with videotape recording. In the 1990s, digital data storage increased and computer networking enabled remote real-time EEG reading, which made possible continuous EEG (cEEG) monitoring. Manual cEEG analysis became increasingly labor-intensive, calling for methods to assist this process. In the 2000s, complex algorithms enabling quantitative EEG analyses were introduced, with a new focus on shared activity between rhythms, including phase and magnitude synchrony. The automation of spectral analysis enabled studies of spectral content.

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Book chapters on the topic "Brain electrical signals"

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Başar, Erol. "Electrical Signals from the Brain." In Springer Series in Synergetics, 21–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72192-2_3.

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Ramón, Fidel, Jesús Hernández-Falcón, and Theodore H. Bullock. "Brain Electrical Signals in Unrestrained Crayfish." In Modern Approaches to the Study of Crustacea, 7–13. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0761-1_2.

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Dhiman, Rohtash, Priyanka, and J. S. Saini. "Wavelet Analysis of Electrical Signals from Brain: The Electroencephalogram." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 283–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37949-9_24.

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Gotham, Solomon, and G. Sasibushana Rao. "A Suitable Approach in Extracting Brain Source Signals from Disabled Patients." In Lecture Notes in Electrical Engineering, 721–31. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2728-1_69.

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Samarpita, Soumya, and Rabinarayan Satpathy. "Impact of EEG Signals on Human Brain Before and After Meditation." In Lecture Notes in Electrical Engineering, 331–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9090-8_29.

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Başar, Erol. "The Brain of the Sleeping Cat: Dynamics of Electrical Signals." In Springer Series in Synergetics, 75–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59893-7_6.

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Choong, Wen Yean, Wan Khairunizam, Murugappan Murugappan, Mohammad Iqbal Omar, Siao Zheng Bong, Ahmad Kadri Junoh, Zuradzman Mohamad Razlan, A. B. Shahriman, and Wan Azani Wan Mustafa. "Hurst Exponent Based Brain Behavior Analysis of Stroke Patients Using EEG Signals." In Lecture Notes in Electrical Engineering, 925–33. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5281-6_66.

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Orjuela-Cañón, Alvaro D., Osvaldo Renteria-Meza, Luis G. Hernández, Andrés F. Ruíz-Olaya, Alexander Cerquera, and Javier M. Antelis. "Self-organizing Maps for Motor Tasks Recognition from Electrical Brain Signals." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 458–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75193-1_55.

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Zhang, Wulin, Yuqiang Chen, and Jianfeng Ma. "Method of Extracting Audio-Visual Induced Brain Signals Based on Deep Neural Network." In Lecture Notes in Electrical Engineering, 1201–7. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0115-6_137.

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Mousavi, Seyed Aliakbar, Muhammad Rafie Hj Mohd Arshad, Hasimah Hj Mohamed, Putra Sumari, and Saeed Panahian Fard. "P300 Detection in Electroencephalographic Signals for Brain–Computer Interface Systems: A Neural Networks Approach." In Lecture Notes in Electrical Engineering, 355–63. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01766-2_41.

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Conference papers on the topic "Brain electrical signals"

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Wagh, Kalyani P., and K. Vasanth. "Review on Various Emotional Disorders by Analyzing Human Brain Signal Patterns (EEG Signals)." In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2019. http://dx.doi.org/10.1109/icecct.2019.8869453.

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Matsuno, Kevin, and Vidya K. Nandikolla. "Machine Learning Using Brain Computer Interface System." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23394.

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Abstract With commercially available hardware and supporting software, different electrical potential brain waves are measured via a headset with a collection of electrodes. Out of the different types of brain signals, the proposed brain-computer interface (BCI) controller utilizes non-task related signals, i.e. squeezing left/right hand or tapping left/right foot, due to their responsive behavior and general signal feature similarity among patients. In addition, motor imagery related signals, such as imagining left/right foot or hand movement are also examined. The main goal of the paper is to demonstrate the performance of machine learning algorithms based on classification accuracy. The performances are evaluated on BCI dataset of three male subjects to extract the most significant features. Each subject undergoes a 30-minute session composed of four experiments: two non-task related signals and two motor imagery signals. Each experiment records fifteen trials of two classes (i.e. left/right hand movement). The raw data is then pre-processed using a MatLab plugin, EEGLAB, where standard processes of cleaning and epoching the signals is performed. The paper discusses machine learning for robotic application and the common flaws when validating machine learning methods in the context of BCI to provide a brief overview on biologically (using brain waves) controlled devices.

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Nicolae, Irina E., and Mihai Ivanovici. "Complexity of EEG Brain Signals Triggered by Fractal Visual Stimuli." In 2021 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2021 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, 2021. http://dx.doi.org/10.1109/optim-acemp50812.2021.9590052.

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Meghdadi, Amir H., Witold Kinsner, and Reza Fazel-Rezai. "Characterization of healthy and epileptic brain EEG signals by monofractal and multifractal analysis." In 2008 Canadian Conference on Electrical and Computer Engineering - CCECE. IEEE, 2008. http://dx.doi.org/10.1109/ccece.2008.4564773.

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Jiang, Huaiguang, Jun Jason Zhang, Adam Hebb, and Mohammad H. Mahoor. "Time-frequency analysis of brain electrical Signals for behvior recognition in patients with Parkinson's disease." In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810621.

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Alrajeh, N. A., K. W. Divine, T. P. Sullivan, and N. M. Bukhari. "Controlling a Valve Actuator and the Flow of Fluids with Interpreted Brain Signals." In SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214559-ms.

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Abstract The ability to control machines and equipment with just one's thoughts is a concept that has been explored in the field of neuroscience for decades. The development of brain-computer interfaces (BCIs) has made it possible to translate electrical signals generated by the brain into machine commands. In recent years, the field of BCI has made significant advancements in non-intrusive technology, which has the potential to revolutionize the way we interact with machines and equipment in a variety of fields. The objective of this project is to merge non-intrusive BCI technology with personal protective equipment (PPE) and control the flow of fluids through human sensory and peripheral signals in field environments. A revolutionary system application was developed and tested by paper authors Kyle W. Divine, Thomas P. Sullivan, Nawaf A. Alrajeh and Nabil M. Bukhari from Saudi Aramco. This proposed system involves the use of a BCI with Electroencephalography (EEG) mounted within a traditional hardhat. The electrical signals generated from the thoughts of the wearer are used to train a Convolutional Neural Network (CNN) and modeled to recognize the wearer's imagined words, primarily open, close, push, pull, turn left, or turn right. These interpreted electrical signals are then relayed to the valve through a Micro Controller device resulting in the operation of the valve and the successful control of fluids with simple thought commands.

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Kawala-Sterniuk, Aleksandra, Jaroslaw Zygarlicki, Adam Lysiak, Barbara Grochowicz, Mariusz Pelc, Waldemar Bauer, Dawid Baczkowicz, et al. "Influence of the variables describing brain signals on the performance of the Naive Bayesian Classifier." In 2022 Progress in Applied Electrical Engineering (PAEE). IEEE, 2022. http://dx.doi.org/10.1109/paee56795.2022.9966567.

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Nguyen, Thanh An, and Yong Zeng. "Analysis of Design Activities Using EEG Signals." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28477.

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It plays a significant role in developing of design theory and methodology to understand designer’s thinking and cognitive process during design activities. The most dominant method to conduct this kind of study is protocol analysis. However, this method is prone to subjective factors. Therefore, other approaches are emerging, which can measure the brain activities directly. With the advances in technologies, brain scanner and brain recorder systems such as EEG, fMRI, PET have become more affordable. In the present research, we used EEG to record designer’s brain electrical signals when s/he was working on a design task. Six channels of the EEG signals were recorded, including Fp1, Fp2, Fz, Cz, Pz, Oz, based on which the power spectral density for each EEG band (delta, theta, alpha and beta) was calculated. The results showed that, for the given design problem, the subject spent more effort in visual thinking during the solution generation than that in solution evaluation. The preliminary success in identifying regularity underlying a single designer’s design process through EEG signals lays a foundation for further investigation of designers’ general mental efforts during the conceptual design process.

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Contreras, Stewart, and V. Sundararajan. "Visual Imagery Classification Using Shapelets of EEG Signals." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71291.

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The goal of this paper is to reconstruct three primitive shapes — rectangular cube, cone and cylinder — by analyzing electrical signals which are emitted by the brain. Three participants are asked to visualize these shapes. During visualization, a 14-channel neuroheadset is used to record electroencephalogram (EEG) signals along the scalp. The EEG recordings are then averaged to increase the signal to noise ratio which is referred to as an event related potential (ERP). Every possible subsequence of each ERP signal is analyzed in an attempt to determine a time series which is maximally representative of a particular class. These time series are referred to as shapelets and form the basis of our classification scheme. After implementing a voting technique for classification, an average classification accuracy of 60% is achieved. Compared to naive classification rate of 33%, we determine that the shapelets are in fact capturing features that are unique in the ERP representation of a unique class.

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Gonal, Jayalaxmi S., and Vinayadatt V. Kohir. "Classification of brain MR images using wavelets texture features and k-Means classfier." In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253749.

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Reports on the topic "Brain electrical signals"

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Research, Gratis. Green Light: A New Preventive Therapy for Migraine. Gratis Research, November 2020. http://dx.doi.org/10.47496/gr.blog.03.

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Manipulating the ability of green light to create the least amount of electrical signals in retina and brain cortex, green light therapy offers an excellent therapeutic role in reducing migraine pain and improves the quality of life

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