Статті в журналах з теми "Brain electrical signals"
Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Brain electrical signals.
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Brain electrical signals".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
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.
Повний текст джерелаАнотація:
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.
2
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.
Повний текст джерела
3
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.
Повний текст джерелаАнотація:
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.
4
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.
Повний текст джерелаАнотація:
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.
5
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.
Повний текст джерелаАнотація:
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
6
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.
Повний текст джерела
7
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.
Повний текст джерелаАнотація:
<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>
8
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.
Повний текст джерелаАнотація:
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.
9
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.
Повний текст джерелаАнотація:
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.
10
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.
Повний текст джерелаАнотація:
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.
11
Rekling, Jens C., and Jack L. Feldman. "Bidirectional Electrical Coupling Between Inspiratory Motoneurons in the Newborn Mouse Nucleus Ambiguus." Journal of Neurophysiology 78, no. 6 (December 1, 1997): 3508–10. http://dx.doi.org/10.1152/jn.1997.78.6.3508.
Повний текст джерелаАнотація:
Rekling, Jens C. and Jack L. Feldman. Bidirectional electrical coupling between inspiratory motoneurons in the newborn mouse nucleus ambiguus. J. Neurophysiol. 78: 3508–3510, 1997. Some spinal and brain stem motoneurons are electrically coupled in the early postnatal period. To test whether respiratory motoneurons in the brain stem are electrically coupled, we performed single and dual whole cell patch recordings from presumptive motoneurons in the nucleus ambiguus in a rhythmically active brain stem slice from newborn mice. Two of eight (25%) biocytin-injected neurons showed dye-coupling and 4 of 11 (36%) of intracellularly recorded pairs of neurons showed evidence of bidirectional electrical coupling. Impulse activity in one cell elicited small spikelets in the other and hyperpolarization of one cell led to hyperpolarization of the other with a coupling ratio (Δ V 2:Δ V 1) of 0.03–0.14. We conclude that inspiratory ambiguus motoneurons in the newborn mouse brain stem are bidirectionally electrically coupled, which may serve to transmit or coordinate signals, chemical or electrical.
12
Azhari, Ahmad, Adhi Susanto, Andri Pranolo, and Yingchi Mao. "Neural Network Classification of Brainwave Alpha Signals in Cognitive Activities." Knowledge Engineering and Data Science 2, no. 2 (December 23, 2019): 47. http://dx.doi.org/10.17977/um018v2i22019p47-57.
Повний текст джерелаАнотація:
The signal produced by human brain waves is one unique feature. Signals carry information and are represented in electrical signals generated from the brain in a typical waveform. Human brain wave activity will always be active even when sleeping. Brain waves will produce different characteristics in different individuals. Physical and behavioral characteristics can be identified from patterns of brain wave activity. This study aims to distinguish signals from each individual based on the characteristics of alpha signals from brain waves produced. Brain wave signals are generated by giving several mental perception tasks measured using an Electroencephalogram (EEG). To get different features, EEG signals are extracted using first-order extraction and are classified using the Neural Network method. The results of this study are typical of the five first-order features used, namely average, standard deviation, skewness, kurtosis, and entropy. The results of pattern recognition training show that 171 successful iterations are carried out with a period of execution of 6 seconds. Performance tests are performed using the Mean Squared Error (MSE) function. The results of the performance tests that were successfully obtained in the pattern test are in the number 0.000994.
13
Huang, Zhongwei, Lifen Cheng, and Yang Liu. "Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training." Computational Intelligence and Neuroscience 2022 (April 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/6752067.
Повний текст джерелаАнотація:
Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and further application in artificial intelligence can better improve and assist human life. Therefore, the research on emotion recognition has attracted the attention of many scholars in the field of artificial intelligence in recent years. Brain electrical signal conversion becomes critical, and it needs a brain electrical signal processing method to extract the effective signal to realize the human-computer interaction However, nonstationary nonlinear characteristics of EEG signals bring great challenge in characteristic signal extraction. At present, although there are many feature extraction methods, none of them can reflect the global feature of the signal. The following solutions are used to solve the above problems: (1) this paper proposed an ICA and sample entropy algorithm-based framework for feature extraction of EEG signals, which has not been applied for EEG and (2) simulation signals were used to verify the feasibility of this method, and experiments were carried out on two real-world data sets, to show the advantages of the new algorithm in feature extraction of EEG signals.
14
Baykara, Muhammet, and Awf Abdulrahman. "Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines." Traitement du Signal 38, no. 2 (April 30, 2021): 331–40. http://dx.doi.org/10.18280/ts.380210.
Повний текст джерелаАнотація:
Epilepsy is one of the most common chronic disorder which negatively affects the patients' life. The functionality of the brain can be obtained from brain signals and it is vital to analyze and examine the brain signals in seizure detection processes. In this study, we performed machine learning-based and signal processing methods to detect epileptic signals. To do that, we examined three different EEG signals (healthy, ictal, and interictal) with two different classes (healthy ones and epileptic ones). Our proposed method consists of three stages which are preprocessing, feature extraction, and classification. In the preprocessing phase, EEG signals normalized to scale all samples into [0,1] range. After Stockwell Transform was applied and chaotic features and Parseval's Energy collected from each EEG signal. In the last part, EEG signals were classified with ELM (Extreme Learning Machines) with different parameters. Our study shows the best classification accuracy obtained from the Sigmoid activation function with the number of 100 hidden neurons. The highlights of this study are: Stockwell Transform is used; Entropy values are selected based on the adaptive process. Threshold values are determined according to the error rates; ELM classifier algorithm is applied.
15
V, Chandana. "Controlling Wheelchair Using Brain as Biosensor." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 471–79. http://dx.doi.org/10.22214/ijraset.2021.37401.
Повний текст джерелаАнотація:
This project discusses about wheel chair controlled by brain based on Brain–computer interfaces (BCI). BCI’s 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 into commands in real time. The intention of the project is to develop a robot that can assist the disabled people in their daily life to do some work independent of others. Here, we analyse the brain wave signals. Human brain consists of millions of interconnected neurons, the pattern of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, 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 are sensed by the brain wave sensor and different patterns are used for controlling a wheel chair.
16
Siddiqui, Mohd Maroof, and Ruchin Jain. "Prediction of REM (Rapid Eye Movement) Sleep Behaviour Disorder using EEG Signal applied EMG1 and EMG2 Channel." Biomedical and Pharmacology Journal 14, no. 1 (March 30, 2021): 519–24. http://dx.doi.org/10.13005/bpj/2153.
Повний текст джерелаАнотація:
This sleep disorder is reflected as the changes in the electrical activities and chemical activities in the brain that can be observed by capturing the brain signals and the images. In this research, Short Time-frequency analysis of Power Spectrum Density (STFAPSD) approach applied on Electroencephalogram (EEG) Signals for prediction of RBD sleep disorder. Collection of Electroencephalogram (EEG) of normal subjects & different type of sleep disordered subjects & application of signal processing on EEG data for development the algorithm for detection of sleep disorder and implementation in MATLAB.
17
Siddiqui, Mohd Maroof, Ruchin Jain, Mohd Suhaib Kidwai, and Mohammad Zunnun Khan. "Recording of eeg Signals and Role in Diagnosis of Sleep Disorder." Biomedical and Pharmacology Journal 15, no. 3 (September 29, 2022): 1421–26. http://dx.doi.org/10.13005/bpj/2479.
Повний текст джерелаАнотація:
Electroencephalogram (EEG) is a recording of the electrical movement of the brain from the scalp. For sleep disorder analysis the EEG test is done while the subject is sleeping. In this paper discuss about recording of brain signal (EEG) and how these signal play major role to finding in different brain diseases. EEG data can be different when subjects are asleep or when exhausted or when some sort of action takes place. When the patient is awake standard EEG test can be taken, but it may not demonstrate any unusual electrical action. During the sleep, brainwave patterns alter and may show more unusual electrical action.
18
Zhao, Zhi-Ping, Chuang Nie, Cheng-Teng Jiang, Sheng-Hao Cao, Kai-Xi Tian, Shan Yu, and Jian-Wen Gu. "Modulating Brain Activity with Invasive Brain–Computer Interface: A Narrative Review." Brain Sciences 13, no. 1 (January 12, 2023): 134. http://dx.doi.org/10.3390/brainsci13010134.
Повний текст джерелаАнотація:
Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.
19
Mu, Zhen Dong, Jian Feng Hu, and Jing Hai Yin. "Information Granule Reduction and Cluster Based Partial Least Squares Applied in EEG Signals." Applied Mechanics and Materials 496-500 (January 2014): 2256–59. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2256.
Повний текст джерелаАнотація:
EEG is a complex signal source, feature extraction and classification algorithm was studied for the brain electrical signal is also a key point in the research of brain waves, information granule clustering algorithm is one of the main idea, at the same time, the partial least square method is an effective method of dimension reduction, this paper, the use of information granule and partial least squares analysis of visual evoked potential EEG signals, the results show that this method can effectively extract the characteristics.
20
Xu, Meng, Yuewu Zhao, Guanghui Xu, Yuehu Zhang, Shengkai Sun, Yan Sun, Jine Wang, and Renjun Pei. "Recent Development of Neural Microelectrodes with Dual-Mode Detection." Biosensors 13, no. 1 (December 30, 2022): 59. http://dx.doi.org/10.3390/bios13010059.
Повний текст джерелаАнотація:
Neurons communicate through complex chemical and electrophysiological signal patterns to develop a tight information network. A physiological or pathological event cannot be explained by signal communication mode. Therefore, dual-mode electrodes can simultaneously monitor the chemical and electrophysiological signals in the brain. They have been invented as an essential tool for brain science research and brain-computer interface (BCI) to obtain more important information and capture the characteristics of the neural network. Electrochemical sensors are the most popular methods for monitoring neurochemical levels in vivo. They are combined with neural microelectrodes to record neural electrical activity. They simultaneously detect the neurochemical and electrical activity of neurons in vivo using high spatial and temporal resolutions. This paper systematically reviews the latest development of neural microelectrodes depending on electrode materials for simultaneous in vivo electrochemical sensing and electrophysiological signal recording. This includes carbon-based microelectrodes, silicon-based microelectrode arrays (MEAs), and ceramic-based MEAs, focusing on the latest progress since 2018. In addition, the structure and interface design of various types of neural microelectrodes have been comprehensively described and compared. This could be the key to simultaneously detecting electrochemical and electrophysiological signals.
21
Wang, Jiu Hui, and Qiang Ji. "Research on Signal Acquisition Based on Wireless Sensor for Foot Compressive Characteristics on Basketball Movement." Applied Mechanics and Materials 483 (December 2013): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amm.483.401.
Повний текст джерелаАнотація:
The signal acquisition system (SAS) operated by battery is designed in this paper. SAS includes signal acquisition and statistics function based on movement joints of basketball player. SAS is a recording of the electrical activity of the brain and pulse from the scalp and the recorded waveforms provide insights into the dynamic aspects of brain activity. The amplified SAS signals are digitized by an A/D converter. The digitized signal is transmitted to PC by a wireless serial port or stored in secure digital memory card. Experimental result shows that the system could implement the acquisition and storage of the foot compressive mechanical characteristics signals efficiently. This system would be of benefit to all involved in the use of SAS for sports training.
22
Zero, Enrico, Chiara Bersani, and Roberto Sacile. "Identification of Brain Electrical Activity Related to Head Yaw Rotations." Sensors 21, no. 10 (May 11, 2021): 3345. http://dx.doi.org/10.3390/s21103345.
Повний текст джерелаАнотація:
Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.
23
Anas Fouad Ahmed. "A quick survey of EEG signal noise removal methods." Global Journal of Engineering and Technology Advances 11, no. 3 (June 30, 2022): 098–104. http://dx.doi.org/10.30574/gjeta.2022.11.3.0100.
Повний текст джерелаАнотація:
An Electroencephalogram (EEG) is produced as a consequence of the electrical voltage of neurons in the brain. The EEG signal is crucial for detecting brain activity and attitude. Because this signal has very low amplitude, it is easily corrupted by different artefacts. The study and analysis of brain signals in the presence of these artifacts is a challenging task. ECG, EOG, EMG, and motion are the popular artifacts that induce disturbance to the EEG signal. This survey paper emphasizes the artifact elimination methods with their substantial parameters that must be considered during the study of published research on this trend.
24
Yuryev, G. A., L. S. Kuravsky, and N. E. Yuryeva. "On the Experience of Developing a Mobile Complex for Recording the Brain Electrical Activity on the Meringue of Dry Electrode Technology." Моделирование и анализ данных 12, no. 3 (2022): 40–48. http://dx.doi.org/10.17759/mda.2022120303.
Повний текст джерелаАнотація:
The technology of creating a mobile complex for registration of the brain electrical activity, for recording an electroencephalographic signal with one channel, is considered. Previously, technological problems associated with insufficient sensitivity and selectivity in the sense of signal-to-noise ratio did not allow the use of electroencephalographic activity recording systems based on the so-called dry electrodes in practical applications. At the same time, even with a small number of leads, such signals, when recorded, for example, from the visual cortex localized in the occipital region of the brain can be extremely informative in the context of the operator activity analysis and other types of human activity, in which arbitrary control of attention plays an essential role. This paper considers the experience of creating a mobile autonomous complex for recording such signals for the tasks of monitoring the characteristics of the operator's activities in scientific applications. The design features of such a device, created based on Node MCU technology, which is gaining wide distribution in embedded systems, proprietary narrowband amplifiers of the electric signal and dry electrodes created by industry in the last decade, are described. Some examples of practical application of such a complex are given. The most promising directions for the development of technology are discussed.
25
Ferrari, Rosana, Aldo Ivan Cespedes Arce, Mariza Pires de Melo, and Ernane Jose Xavier Costa. "Noninvasive method to assess the electrical brain activity from rats." Ciência Rural 43, no. 10 (August 20, 2013): 1838–42. http://dx.doi.org/10.1590/s0103-84782013005000117.
Повний текст джерелаАнотація:
This research presents a noninvasive method for the acquisition of brain electrical signal in rat. Was used an electroencephalography (EEG) system developed for bovine and adapted to rats. The bipolar electrode system (needle electrodes) was glued on the surface of the head of the animal without surgical procedures and the other electrode was glued to the tail, as ground. The EEG activity was sampled at 120Hz for an hour. The accuracy and precision of the EEG measurement was performed using Fourier analysis and signal energy. For this, the digital signal was divided into sections successive of 3 seconds and was decomposed into four frequency bands: delta (0.3 to 4Hz), theta (4-8Hz), alpha (8-12Hz) and beta (12-30Hz) and energy (µV²) of the series of time filtered were calculated. The method allowed the acquisition of non-invasive electrical brain signals in conscious rats and their frequency patterns were in agreement with previous studies that used surgical procedures to acquire EEG in rats. This system showed accuracy and precision and will allow further studies on behavior and to investigate the action of drugs on the central nervous system in rats without surgical procedures.
26
Küçükakarsu, Mustafa, Ahmet Reşit Kavsaoğlu, Fayadh Alenezi, Adi Alhudhaif, Raghad Alwadie, and Kemal Polat. "A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain’s Electrical Activity Signals." Diagnostics 13, no. 3 (February 3, 2023): 575. http://dx.doi.org/10.3390/diagnostics13030575.
Повний текст джерелаАнотація:
This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.
27
Englert, Robert, Fabienne Rupp, Frank Kirchhoff, Klaus Peter Koch, and Michael Schweigmann. "Technical characterization of an 8 or 16 channel recording system to acquire electrocorticograms of mice." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 595–98. http://dx.doi.org/10.1515/cdbme-2017-0124.
Повний текст джерелаАнотація:
AbstractWhen performing electrocorticography, reliable recordings of bioelectrical signals are essential for signal processing and analysis. The acquisition of cellular electrical activity from the brain surface of mice requires a system that is able to record small signals within a low frequency range. This work presents a recording system with self-developed software and shows the result of a technical characterization in combination with self-developed electrode arrays to measure electrocorticograms of mice.
28
Hansen, Sofie Therese, Apit Hemakom, Mads Gylling Safeldt, Lærke Karen Krohne, Kristoffer Hougaard Madsen, Hartwig R. Siebner, Danilo P. Mandic, and Lars Kai Hansen. "Unmixing Oscillatory Brain Activity by EEG Source Localization and Empirical Mode Decomposition." Computational Intelligence and Neuroscience 2019 (March 14, 2019): 1–15. http://dx.doi.org/10.1155/2019/5618303.
Повний текст джерелаАнотація:
Neuronal activity is composed of synchronous and asynchronous oscillatory activity at different frequencies. The neuronal oscillations occur at time scales well matched to the temporal resolution of electroencephalography (EEG); however, to derive meaning from the electrical brain activity as measured from the scalp, it is useful to decompose the EEG signal in space and time. In this study, we elaborate on the investigations into source-based signal decomposition of EEG. Using source localization, the electrical brain signal is spatially unmixed and the neuronal dynamics from a region of interest are analyzed using empirical mode decomposition (EMD), a technique aimed at detecting periodic signals. We demonstrate, first in simulations, that the EMD is more accurate when applied to the spatially unmixed signal compared to the scalp-level signal. Furthermore, on EEG data recorded simultaneously with transcranial magnetic stimulation (TMS) over the hand area of the primary motor cortex, we observe a link between the peak to peak amplitude of the motor-evoked potential (MEP) and the phase of the decomposed localized electrical activity before TMS onset. The results thus encourage combination of source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin.
29
Gani S. F., Abd, Miskon M. F., Hamzah R. A., A. Aziz K. A., Kadmin A. F, Jidin A. Z., Md Basar M. F, Kamalrudin M., A. Razak E. N. S, and Md Ali Shah M. A. S. "Electrical Appliance Switching Controller by Brain Wave Spectrum Evaluation Using a Wireless EEG Headset." International Journal of Emerging Technology and Advanced Engineering 11, no. 10 (October 15, 2021): 109–19. http://dx.doi.org/10.46338/ijetae1021_14.
Повний текст джерелаАнотація:
Disabled people are usually unable to interact with their surroundings efficiently, and performing tasks like switching an appliance on or off can be troublesome if the user is bedridden, for example. This article discusses an electrical appliance switching controller using a wireless EEG headset that is aimed to aid elderly people and the disabled. The system comprises of a MindLink EEG headset that is Bluetooth-connected to an Arduino microcontroller board. The system permits the user to separately switch on and off the 4 electrical devices connected to the power socket. The EEG signal is obtained to investigate the brain activity throughout the experiments done. Based on the brain wave signals read, attention and meditation are determined to be the most suitable for this project and is used to trigger the relay switching of the power socket. It is found that the response time to trigger the switching is slow as some users require practice or training to control their brain wave signals effectively. The work performed provides a rudimentary insight of a BCI system functionalities and presents a brainwave-controlled hardware switching for the bedridden or disabled patients.
30
Fernandez-Chiappe, Florencia, and Nara I. Muraro. "Patch-Clamping Fly Brain Neurons." Cold Spring Harbor Protocols 2022, no. 8 (July 7, 2022): pdb.top107796. http://dx.doi.org/10.1101/pdb.top107796.
Повний текст джерелаАнотація:
The membrane potential of excitable cells, such as neurons and muscle cells, experiences a rich repertoire of dynamic changes mediated by an array of ligand- and voltage-gated ion channels. Central neurons, in particular, are fantastic computators of information, sensing, and integrating multiple subthreshold currents mediated by synaptic inputs and translating them into action potential patterns. Electrophysiology comprises a group of techniques that allow the direct measurement of electrical signals. There are many different electrophysiological approaches, but, because Drosophila neurons are small, the whole-cell patch-clamp technique is the only applicable method for recording electrical signals from individual central neurons. Here, we provide background on patch-clamp electrophysiology in Drosophila and introduce protocols for dissecting larval and adult brains, as well as for achieving whole-cell patch-clamp recordings of identified neuronal types. Patch clamping is a labor-intensive technique that requires a great deal of practice to become an expert; therefore, a steep learning curve should be anticipated. However, the instant gratification of neuronal spiking is an experience that we wish to share and disseminate, as many more Drosophila patch clampers are needed to study the electrical features of so many fly neuronal types unknown to date.
31
Hu, Jian Feng, Zhen Dong Mu, and Jing Hai Yin. "Features Extraction Method of Motor Imagery EEG Based on Information Granules." Applied Mechanics and Materials 496-500 (January 2014): 1982–85. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1982.
Повний текст джерелаАнотація:
EEG is a complex signal source, feature extraction and classification algorithm was studied for the brain electrical signal is also a key point in the research of brain waves, information granule clustering algorithm is one of the main idea, at the same time, the partial least square method is an effective method of dimension reduction, this paper, the use of information granule and partial least squares analysis of visual evoked potential EEG signals, the results show that this method can effectively extract the characteristics.
32
Caesarendra, Wahyu. "A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection." Journal of Energy, Mechanical, Material and Manufacturing Engineering 2, no. 1 (November 9, 2017): 9. http://dx.doi.org/10.22219/jemmme.v2i1.4796.
Повний текст джерелаАнотація:
The progress of today's technology is growing very quickly. This becomes the motivation for the community to be able to continue and provide innovations. One technology to be developed is the application of brain signals or called with electroencephalograph (EEG). EEG is a non-invasive measurement method that represents electrical signals from brain activity obtained by placement of multiple electrodes on the scalp in the area of the brain, thus obtaining information on electrical brain signals to be processed and analyzed. Lie is an act of covering up something so that only the person who is lying knows the truth of the statement. The hidden information from lying subjects will elicit an EEG-P300 signal response using Independent Component Analysis (ICA) in different shapes of amplitude that tends to be larger around 300 ms after stimulation. The method used in the experiment is to invite subject in a card game so that the process can be done naturally and the subject can well stimulated. After the trials there are several results almost all subjects have the same frequency on the frequency of 24-27 Hz. This is a classification of beta waves that have a frequency of 13-30 Hz where the beta wave is closely related to active thinking and attention, focusing on the outside world or solving concrete problems.
33
Djamal, Esmeralda Contessa, and Dimas Andhika Sury. "Multi-channel of electroencephalogram signal in multivariable brain-computer interface." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (June 1, 2023): 618. http://dx.doi.org/10.11591/ijai.v12.i2.pp618-626.
Повний текст джерелаАнотація:
Brain-computer interface (BCI) usually uses Electroencephalogram (EEG) signals as an intermediate device to drive external devices directly from the brain. The development of BCI capabilities is carried out by involving multivariable EEG signals as movement commands. EEG signals are recorded using multi-channel, enriching information if it uses the suitable method and architecture. This research proposed a two-dimensional convolutional neural networks (CNN) method to recognize multi-channel EEG signals. The vertical dimension is the channel, while the horizontal is the signal sequence. Hence, the signal is connected with the information time series of the same channel and between channels simultaneously. BCI was arranged with multivariable signals, specifically motor imagery and emotion. Both variables have different characteristics, and the information is from different channels. Therefore, it needs multiple CNNs to recognize the two variables in the EEG signal. The experiment showed that the accuracy of multiple 2D-CNN increased to 94.62% compared to 85.44% of single 2D CNN. Multiple 2D-CNN gave accuracy from 82.04% to 94.62% more than multiple 1D-CNN. 2D-CNN makes the channel extraction perfect into vectors to maintain the signal sequence. Signal extraction is essential, so the used Wavelet filter upgraded accuracy from 73.75% to 94.62%.
34
Ranjandish, Reza, and Alexandre Schmid. "A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control." Sensors 20, no. 19 (October 8, 2020): 5716. http://dx.doi.org/10.3390/s20195716.
Повний текст джерелаАнотація:
Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very carefully designed so as to operate in low-power and low-latency, while enhancing the probability of correct event detection. This paper reviews the electrical characteristics of the target brain signals pertaining to epilepsy detection. Commercial systems are presented and discussed. Finally, the major blocks of the signal acquisition chain are presented with a focus on the circuit architecture and a careful attention to solutions to issues related to data acquisition from multi-channel arrays of cortical sensors.
35
Dimitrov, Georgi P., Galina Panayotova, Boyan Jekov, Pavel Petrov, Iva Kostadinova, Snejana Petrova, Olexiy S. Bychkov, Vasyl Martsenyuk, and Aleksandar Parvanov. "Algorithms for Classification of Signals Derived From Human Brain." International Journal of Circuits, Systems and Signal Processing 15 (September 20, 2021): 1521–26. http://dx.doi.org/10.46300/9106.2021.15.164.
Повний текст джерелаАнотація:
Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main purpose of this paper is to provide a fair and extensive comparison of some commonly employed classification methods under the same conditions so that the assessment of different classifiers will be more convictive. In this study, we investigated the accuracy of the classification of the received signals with classifiers based on AdaBoost (AB), Decision Tree (DT), k-Nearest Neighbor (kNN), Gaussian SVM, Linear SVM, Polynomial SVM, Random Forest (RF), Random Forest Regression ( RFR ). We used only basic parameters in the classification, and we did not apply fine optimization of the classification results. The obtained results show suitable algorithms for the classification of EEG signals. This would help young researchers to achieve interesting results in this field faster.
36
Torres-Valencia, Cristian, Álvaro Orozco, David Cárdenas-Peña, Andrés Álvarez-Meza, and Mauricio Álvarez. "A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis." Applied Sciences 10, no. 19 (September 27, 2020): 6765. http://dx.doi.org/10.3390/app10196765.
Повний текст джерелаАнотація:
The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM) allows us to find and quantify the relationships between different channels. Several MOGPs are trained from different conditions grouped in bi-class problems, and the discrimination is performed based on the likelihood score of the test signals against all the models. Finally, the mean likelihood is computed to predict the correspondence of new inputs with each class’s existing models. Results show that this framework allows us to model the EEG signals adequately using generative models and allows analyzing the relationships between channels of the EEG for a particular condition. At the same time, the set of trained MOGPs is well suited to discriminate new input data.
37
Liu, Tianyi, Mingshen Shen, and Xiaohan Wang. "Difference of Brain Electrical Activity Mappings in Sleep Stages." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 568–74. http://dx.doi.org/10.54097/hset.v39i.6590.
Повний текст джерелаАнотація:
According to a survey by the World Health Organization, the proportion of people who has difficulty in sleeping is up to 27%. Detecting the cause of these sleep disorders needs an elaborate analysis of the physiological signals of different sleep stages. Analyzing and comparing the brain electrical activity mapping energy difference of normal subjects and subjects who have the disease of nocturnal frontal lobe epilepsy is introduced in this study. The brain electrical activity mapping is from the independent component analysis (ICA) of the Electroencephalograph (EEG) waveform. The EEG data set is coming from the CAP sleep database. The control group uses the data of n3, n10, and n11. The experimental group uses the data of nfle1, nfle2, and nfle3. The EEGLAB, a toolbox in MATLAB, is used to preprocess the EEG waveform and locate the area where signals are generated in the brain. The preprocessing steps include channel locations, selecting data, filtering, re-referencing the data, ICA, and artifact rejection. After the preprocessing, there are 13 electrodes retained and the energy difference of the brain electrical activity mapping will be compared between the control group and the experimental group by observation.
38
Lashkari, Saleh, Ali Moghimi, Hamid Reza Kobravi, and Mohamad Amin Younessi Heravi. "A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model." International Clinical Neuroscience Journal 8, no. 4 (October 30, 2021): 180–87. http://dx.doi.org/10.34172/icnj.2021.36.
Повний текст джерелаАнотація:
Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space. Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method. Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy. Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals.
39
Martinez-Corral, Rosa, Jintao Liu, Arthur Prindle, Gürol M. Süel, and Jordi Garcia-Ojalvo. "Metabolic basis of brain-like electrical signalling in bacterial communities." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1774 (April 22, 2019): 20180382. http://dx.doi.org/10.1098/rstb.2018.0382.
Повний текст джерелаАнотація:
Information processing in the mammalian brain relies on a careful regulation of the membrane potential dynamics of its constituent neurons, which propagates across the neuronal tissue via electrical signalling. We recently reported the existence of electrical signalling in a much simpler organism, the bacterium Bacillus subtilis . In dense bacterial communities known as biofilms, nutrient-deprived B. subtilis cells in the interior of the colony use electrical communication to transmit stress signals to the periphery, which interfere with the growth of peripheral cells and reduce nutrient consumption, thereby relieving stress from the interior. Here, we explicitly address the interplay between metabolism and electrophysiology in bacterial biofilms, by introducing a spatially extended mathematical model that combines the metabolic and electrical components of the phenomenon in a discretized reaction–diffusion scheme. The model is experimentally validated by environmental and genetic perturbations, and confirms that metabolic stress is transmitted through the bacterial population via a potassium wave. Interestingly, this behaviour is reminiscent of cortical spreading depression in the brain, characterized by a wave of electrical activity mediated by potassium diffusion that has been linked to various neurological disorders, calling for future studies on the evolutionary link between the two phenomena. This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.
40
Mansoor, Asif, Muhammad Waleed Usman, Noreen Jamil, and M. Asif Naeem. "Deep Learning Algorithm for Brain-Computer Interface." Scientific Programming 2020 (August 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/5762149.
Повний текст джерелаАнотація:
Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms.
41
Radhakrishnan, Menaka, Karthik Ramamurthy, Avantika Kothandaraman, Gauri Madaan, and Harini Machavaram. "Investigating EEG Signals of Autistic Individuals Using Detrended Fluctuation Analysis." Traitement du Signal 38, no. 5 (October 31, 2021): 1515–20. http://dx.doi.org/10.18280/ts.380528.
Повний текст джерелаАнотація:
To record all electrical activity of the human brain, an electroencephalogram (EEG) test using electrodes attached to the scalp is conducted. Analysis of EEG signals plays an important role in the diagnosis and treatment of brain diseases in the biomedical field. One of the brain diseases found in early ages include autism. Autistic behaviours are hard to distinguish, varying from mild impairments, to intensive interruption in daily life. The non-linear EEG signals arising from various lobes of the brain have been studied with the help of a robust technique called Detrended Fluctuation Analysis (DFA). Here, we study the EEG signals of Typically Developing (TD) and children with Autism Spectrum Disorder (ASD) using DFA. The Hurst exponents, which are the outputs of DFA, are used to find out the strength of self-similarity in the signals. Our analysis works towards analysing if DFA can be a helpful analysis for the early detection of ASD.
42
Sheela sobana Rani, K., S. Pravinth Raja, M. Sinthuja, B. Vidhya Banu, R. Sapna, and Kenenisa Dekeba. "Classification of EEG Signals Using Neural Network for Predicting Consumer Choices." Computational Intelligence and Neuroscience 2022 (July 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/5872401.
Повний текст джерелаАнотація:
EEG, or Electroencephalogram, is an instrument that examines the brain’s functions while it is executing any activity. EEG signals to aid in the identification of brain processes and movements and are thus useful in the detection of neurobiological illnesses. Pulses have a very weak magnitude and are recorded from peak to peak, with pulse width ranging from 0.5 to 100 V, which is around 100 times below than ECG signals. As a result, many types of noise can easily influence them. Because EEG signals are so important in detecting brain illnesses, it is critical to preprocess them for accurate assessment and detection. The crown of your head The EEG is a weighted combination of the signals generated by the different small locations beneath the electrodes on the cortical plate. The rhythm of electrical impulses is useful for evaluating a broad range of brain diseases. Hypertension, Alzheimer, and brain damage are all possibilities. We can compare and distinguish the brainwaves for different emotions and illnesses linked with the brain by studying the EEG signal. Multiple research studies and methodologies for preprocessing, extraction of features, and evaluation of EEG data have recently been created. The use of EEG in human-computer communication could be a novel and demanding field that has acquired traction in recent years. We present predictive modeling for analyzing the customer’s preference of likes and dislikes via EEG signal in our report. The impulses were obtained when clients used the Internet to seek for multiple items. The studies were carried out on a dataset that included a variety of consumer goods.
43
Mammone, Nadia, Simona De Salvo, Cosimo Ieracitano, Silvia Marino, Emanuele Cartella, Alessia Bramanti, Roberto Giorgianni, and Francesco Morabito. "Compressibility of High-Density EEG Signals in Stroke Patients." Sensors 18, no. 12 (November 23, 2018): 4107. http://dx.doi.org/10.3390/s18124107.
Повний текст джерелаАнотація:
Stroke is a critical event that causes the disruption of neural connections. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. Intra- and extra-cellular currents are involved in the communication between neurons and the macroscopic effects of such currents can be detected at the scalp through electroencephalographic (EEG) sensors. EEG can be used to study the lesions in the brain indirectly, by studying their effects on the brain electrical activity. The primary goal of the present work was to investigate possible asymmetries in the activity of the two hemispheres, in the case one of them is affected by a lesion due to stroke. In particular, the compressibility of High-Density-EEG (HD-EEG) recorded at the two hemispheres was investigated since the presence of the lesion is expected to impact on the regularity of EEG signals. The secondary objective was to evaluate if standard low density EEG is able to provide such information. Eighteen patients with unilateral stroke were recruited and underwent HD-EEG recording. Each EEG signal was compressively sensed, using Block Sparse Bayesian Learning, at increasing compression rate. The two hemispheres showed significant differences in the compressibility of EEG. Signals acquired at the electrode locations of the affected hemisphere showed a better reconstruction quality, quantified by the Structural SIMilarity index (SSIM), than the EEG signals recorded at the healthy hemisphere (p < 0.05), for each compression rate value. The presence of the lesion seems to induce an increased regularity in the electrical activity of the brain, thus an increased compressibility.
44
de Brito Guerra, Tarciana C., Taline Nóbrega, Edgard Morya, Allan de M. Martins, and Vicente A. de Sousa. "Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery." Sensors 23, no. 9 (April 26, 2023): 4277. http://dx.doi.org/10.3390/s23094277.
Повний текст джерелаАнотація:
Electroencephalography (EEG) is a fundamental tool for understanding the brain’s electrical activity related to human motor activities. Brain-Computer Interface (BCI) uses such electrical activity to develop assistive technologies, especially those directed at people with physical disabilities. However, extracting signal features and patterns is still complex, sometimes delegated to machine learning (ML) algorithms. Therefore, this work aims to develop a ML based on the Random Forest algorithm to classify EEG signals from subjects performing real and imagery motor activities. The interpretation and correct classification of EEG signals allow the development of tools controlled by cognitive processes. We evaluated our ML Random Forest algorithm using a consumer and a research-grade EEG system. Random Forest efficiently distinguishes imagery and real activities and defines the related body part, even with consumer-grade EEG. However, interpersonal variability of the EEG signals negatively affects the classification process.
45
Islam, Sheikh Md Rabiul, and Md Shakibul Islam. "Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink." Indian Journal of Signal Processing 1, no. 3 (August 10, 2021): 1–7. http://dx.doi.org/10.35940/ijsp.c1008.081321.
Повний текст джерелаАнотація:
The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.
46
Islam, Sheikh Md Rabiul, and Md Shakibul Islam. "Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink." Indian Journal of Signal Processing 1, no. 3 (August 10, 2021): 1–7. http://dx.doi.org/10.54105/ijsp.c1008.081321.
Повний текст джерелаАнотація:
The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.
47
Akgun, Omer. "Determination of the Appropriate Kernel Structure in Electroencephalography Analysis of Alcoholic Subjects." Traitement du Signal 37, no. 4 (October 10, 2020): 571–77. http://dx.doi.org/10.18280/ts.370404.
Повний текст джерелаАнотація:
Alcoholism is one of the major health problems in the world. The organ most affected by alcohol is the brain. It has been shown that alcohol causes neuronal loss in the brain and reduces brain blood flow and oxygen use. Electroencephalography is a method that measures the instantaneous electrical activity of the brain. It is known that valuable information can be obtained by observing the biological effects of alcohol through EEG. As their methods of signal processing and analysis have evolved, Electroencephalography signals have attracted the attention of researchers in this field. In this study, methods of the time-frequency analysis were applied to Electroencephalography signals obtained from normal and alcoholic subjects. For this purpose, the Cohen’s class distribution was examined. Ambiguity function analysis, which was in the structure of the distribution, was applied to the signals. Then, from the kernel structure inside the distribution, the Wigner-Ville distribution, which was very common, was reached and this distribution was examined. The inadequacy of the distribution resolution was seen and analysis of the new time-frequency distributions, which were obtained by making convolution with 4 types of kernel functions (nonseparable, separable, Doppler independent, lag independent), was performed. As a result, it was shown that the resolution of time-frequency distributions could be improved with proper kernel functions. Thus, at the end of these analyses, changes that alcohol caused in brain functions were revealed.
48
Parvez, Mohammad Zavid, and Manoranjan Paul. "NOVEL APPROACHES OF EEG SIGNAL CLASSIFICATION USING IMF BANDWIDTH AND DCT FREQUENCY." Biomedical Engineering: Applications, Basis and Communications 27, no. 03 (May 28, 2015): 1550027. http://dx.doi.org/10.4015/s1016237215500271.
Повний текст джерелаАнотація:
Electroencephalogram (EEG) is a record of ongoing electrical signal to represent the human brain activity. It has great potential for the diagnosis to treatment of mental disorder and brain diseases such as epileptic seizure. Features extraction and classification is a crucial task to detect the stage of ictal (i.e. seizure period) and interictal (i.e. period between seizures) EEG signals for the treatment and precaution of the patient. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering their non-abrupt phenomena and inconsistency in different brain locations. In this paper, we present new approaches for feature extraction using high-frequency components from discrete cosine transformation (DCT) and intrinsic mode function (IMF) extracted from empirical mode decomposition (EMD). These features are then used as an input to least square-support vector machine (LV-SVM) to classify ictal and interictal EEG signals. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity, and accuracy with greater consistence of ictal and interictal period of epilepsy for benchmark dataset from different brain locations.
49
Koudelková, Zuzana, and Martin Strmiska. "Introduction to the identification of brain waves based on their frequency." MATEC Web of Conferences 210 (2018): 05012. http://dx.doi.org/10.1051/matecconf/201821005012.
Повний текст джерелаАнотація:
A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.
50
Jurgielewicz, Paweł, Tomasz Fiutowski, Ewa Kublik, Andrzej Skoczeń, Małgorzata Szypulska, Piotr Wiącek, Paweł Hottowy, and Bartosz Mindur. "Modular Data Acquisition System for Recording Activity and Electrical Stimulation of Brain Tissue Using Dedicated Electronics." Sensors 21, no. 13 (June 28, 2021): 4423. http://dx.doi.org/10.3390/s21134423.
Повний текст джерелаАнотація:
In this paper, we present a modular Data Acquisition (DAQ) system for simultaneous electrical stimulation and recording of brain activity. The DAQ system is designed to work with custom-designed Application Specific Integrated Circuit (ASIC) called Neurostim-3 and a variety of commercially available Multi-Electrode Arrays (MEAs). The system can control simultaneously up to 512 independent bidirectional i.e., input-output channels. We present in-depth insight into both hardware and software architectures and discuss relationships between cooperating parts of that system. The particular focus of this study was the exploration of efficient software design so that it could perform all its tasks in real-time using a standard Personal Computer (PC) without the need for data precomputation even for the most demanding experiment scenarios. Not only do we show bare performance metrics, but we also used this software to characterise signal processing capabilities of Neurostim-3 (e.g., gain linearity, transmission band) so that to obtain information on how well it can handle neural signals in real-world applications. The results indicate that each Neurostim-3 channel exhibits signal gain linearity in a wide range of input signal amplitudes. Moreover, their high-pass cut-off frequency gets close to 0.6Hz making it suitable for recording both Local Field Potential (LFP) and spiking brain activity signals. Additionally, the current stimulation circuitry was checked in terms of the ability to reproduce complex patterns. Finally, we present data acquired using our system from the experiments on a living rat’s brain, which proved we obtained physiological data from non-stimulated and stimulated tissue. The presented results lead us to conclude that our hardware and software can work efficiently and effectively in tandem giving valuable insights into how information is being processed by the brain.