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Journal articles on the topic 'Electroencephalogram signal'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Nadira Mohammad Yosi, Aqila Nur, Khairul Azami Sidek, Hamwira Sakti Yaacob, Marini Othman, and Ahmad Zamani Jusoh. "Emotion recognition using electroencephalogram signal." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (2019): 786. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp786-793.

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<p class="Abstract">Emotion play an essential role in human’s life and it is not consciously controlled. Some of the emotion can be easily expressed by facial expressions, speech, behavior and gesture but some are not. This study investigates the emotion recognition using electroencephalogram (EEG) signal. Undoubtedly, EEG signals can detect human brain activity accurately with high resolution data acquisition device as compared to other biological signals. Changes in the human brain’s electrical activity occur very quickly, thus a high resolution device is required to determine the emot

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Bragin, A. D., and V. G. Spitsyn. "Motor imagery recognition in electroencephalograms using convolutional neural networks." Computer Optics 44, no. 3 (2020): 482–87. http://dx.doi.org/10.18287/2412-6179-co-669.

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Electroencephalography is a widespread method to record brain signals with the use of electrodes located on the surface of the head. This method of recording the brain activity has become popular because it is relatively cheap, compact, and does not require implanting the electrodes directly into the brain. The article is devoted to a problem of recognition of motor imagery by electroencephalogram signals. The nature of such signals is complex. Characteristics of electroencephalograms are individual for every person, also depending on their age and mental state, as well as the presence of nois

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3

Geetha, P., and S. Nagarani. "Novel Model for Automatic Classification of the Epileptic Seizures Using Fast Fourier Series-Haar Wavelet Transform." Journal of Medical Imaging and Health Informatics 11, no. 12 (2021): 3209–14. http://dx.doi.org/10.1166/jmihi.2021.3918.

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The disorder based on neurological can be considered as epilepsy that leads to the recurrent seizures in occurrence. The electronic characteristics of brain can be monitor by the electroencephalogram (EEG). It is most commonly used in the medical application. The function monitoring records can be non linear as well as non stationary functioning. The present work produce a novel methodology, it is depend on Fast Fourier series (FFS) and wavelet transform based on Haar. These methods are used for the various kinds of epileptic seizure the electroencephalogram based signal. The detection of boun

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Prendergast, Erica, Michele Grimason Mills, Jonathan Kurz, Joshua Goldstein, and Andrea C. Pardo. "Implementing Quantitative Electroencephalogram Monitoring by Nurses in a Pediatric Intensive Care Unit." Critical Care Nurse 42, no. 2 (2022): 32–40. http://dx.doi.org/10.4037/ccn2022680.

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Background Nonconvulsive seizures occur frequently in pediatric intensive care unit patients and can be impossible to detect clinically without electroencephalogram monitoring. Quantitative electroencephalography uses mathematical signal analysis to compress data, monitoring trends over time. Nonneurologists can identify seizures with quantitative electroencephalography, but data on its use in the clinical setting are limited. Local Problem Bedside quantitative electroencephalography was implemented and nurses received education on its use for seizure detection. This quality improvement projec

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Berezovchuk, L. V., and M. E. Makarchuk. "About bioelectric buffer system of the brain." Klinicheskaia khirurgiia 87, no. 7-8 (2020): 53–57. http://dx.doi.org/10.26779/2522-1396.2020.7-8.53.

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Objective. Elaboration of objective quantitative criterion of electroencephalogram for estimation of the brain functional state in man. Маterials and methods. The background electroencephalograms analysis was conducted in 6 groups of the examined patients with various diagnosis (41 patients at all). Control group consisted of 7 patients, ageing 20 - 56 yrs (average age 35 yrs). Recording of EEG was conducted, using 16-channel electroencephalograph «NeuroCom standart» (KhАI - Меdika, Ukraine) in accordance to international system of recording «10-20». There were analyzed a quantity of mea

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Melinda, Melinda, Syahrial, Yunidar, Al Bahri, and Muhammad Irhamsyah. "Finite Impulse Response Filter for Electroencephalogram Waves Detection." Green Intelligent Systems and Applications 2, no. 1 (2022): 7–19. http://dx.doi.org/10.53623/gisa.v2i1.65.

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Electroencephalographic data signals consist of electrical signal activity with several characteristics, such as non-periodic patterns and small voltage amplitudes that can mix with noise making it difficult to recognize. This study uses several types of EEG wave signals, namely Delta, Alpha, Beta, and Gamma. The method we use in this study is the application of an impulse response filter to replace the noise obtained before and after the FIR filter is applied. In addition, we also analyzed the quality of several types of electroencephalographic signal waves by looking at the addition of the s

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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 (2023): 618. http://dx.doi.org/10.11591/ijai.v12.i2.pp618-626.

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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,

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Esmeralda, Contessa Djamal, and Andhika Sury Dimas. "Multi-channel of electroencephalogram signal in multivariable brain-computer interface." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 618–26. https://doi.org/10.11591/ijai.v12.i2.pp618-626.

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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,

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9

Perumalsamy, Marichamy, and Kalyana Sundaram Chandran. "Gustatory stimulus-based electroencephalogram signal classification." International Journal of Biomedical Engineering and Technology 37, no. 3 (2021): 308. http://dx.doi.org/10.1504/ijbet.2021.10043694.

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10

Chandran, Kalyana Sundaram, and Marichamy Perumalsamy. "Gustatory stimulus-based electroencephalogram signal classification." International Journal of Biomedical Engineering and Technology 37, no. 3 (2021): 308. http://dx.doi.org/10.1504/ijbet.2021.119930.

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11

TuerxunWaili, Yousif Sa’ad Alshebly, Khairul Azami Sidek, and Md Gapar Md Johar. "Stress recognition using Electroencephalogram (EEG) signal." Journal of Physics: Conference Series 1502 (March 2020): 012052. http://dx.doi.org/10.1088/1742-6596/1502/1/012052.

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12

Dutta, A., S. Kour, and S. Taran. "Automatic drowsiness detection using electroencephalogram signal." Electronics Letters 56, no. 25 (2020): 1383–86. http://dx.doi.org/10.1049/el.2020.2697.

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13

Kamil Gatfan, Sarah. "A Review on Deep Learning For Electroencephalogram Signal Classification." Journal of Al-Qadisiyah for Computer Science and Mathematics 16, no. 1 (2024): 137–51. http://dx.doi.org/10.29304/jqcsm.2024.16.11453.

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Recently, the research on Electroencephalogram (EEG) signals have been obtained more focus at the same time the EEG signal is regarded as the basis for the prediction of diagnosis disease and the brain behavior. EEG is as significant tool for many conditions that can be recorded the brain human waves which accommodate the brain activity. In the recent decades, EEG data has been extensively applied in the approaches of data analysis such as time series analysis. With the considerable achievement of deep learning (DL) implement on the time series data, multiple studies have been began applying d

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Pathak, Shweta Suresh, and Sanjiv Vedu Bonde. "Classification of Electroencephalogram (EEG) Signals Using Linear Discriminant Analysis." International Journal of Microsystems and IoT 1, no. 5 (2023): 288–96. https://doi.org/10.5281/zenodo.10060162.

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The cognitive behavior of brain can be analyzed using EEG signals. Nowadays, EEG signals are widely used to study brain related activities and various disorders. Artificial intelligence tools are widely used to analyze these signals which are captured using network of electrodes fixed on the human scalp and transferred on mobile device for further analysis. In the present study, EEG signal analysis is performed on the online data set containing motor imagery information. Various parameters of EEG signals are pre-processed before analyzing. EEG signal pre-processing is done using Independent Co

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Kumar, B. Krishna. "Estimation of Number of Levels of Scaling the Principal Components in Denoising EEG Signals." Biomedical and Pharmacology Journal 14, no. 1 (2021): 425–33. http://dx.doi.org/10.13005/bpj/2142.

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Electroencephalogram (EEG) is basically a standard method for investigating the brain’s electrical action in diverse psychological and pathological states. Investigation of Electroencephalogram (EEG) signal is a tough task due to the occurrence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. By and large EEG signals falls in the range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex[1]. In this research paper, Princ

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Popov, A. A., A. M. Kanajkin, K. A. Roshchina, O. R. CHertov, and V. A. SHashkov. "Detection of artifacts in the EEG signal with using wavelet transform." Electronics and Communications 16, no. 4 (2011): 126–30. http://dx.doi.org/10.20535/2312-1807.2011.16.4.245547.

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The paper considers the task of cleaning up the EEG signal from artifacts. Method for identifying electrooculogram and signal recovery after its removal using discrete wavelet transform of the electroencephalogram is proposed. The developed method showed nice results on examined examples of real signals at localization and removal of artifacts

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Lee, Ren-Guey, Chun-Chang Chen, Chun-Chieh Hsiao, Hsi-Wen Wang, and Ming-Shen Wei. "SLEEP APNEA SYNDROME RECOGNITION USING THE GREYART NETWORK." Biomedical Engineering: Applications, Basis and Communications 23, no. 03 (2011): 163–72. http://dx.doi.org/10.4015/s1016237211002505.

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This study employs relational analysis and the GreyART network to identify and study the characteristics of electroencephalogram signals of sleep apnea syndrome (SAS). Seventeen raw electroencephalogram data records from the sleep database compiled by Massachusetts Institute of Technology (MIT) and Beth Israel Hospital (BIH) were used in conjunction with four wavelet decomposition steps to obtain the cD4 wavelet coefficient as input for the GreyART network. The GreyART network was then used for simulation training and testing in order to achieve the best recognition results. This study achieve

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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 (2021): 519–24. http://dx.doi.org/10.13005/bpj/2153.

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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

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Katona, Jozsef, and A. Kovari. "EEG-based Computer Control Interface for Brain-Machine Interaction." International Journal of Online Engineering (iJOE) 11, no. 6 (2015): 43. http://dx.doi.org/10.3991/ijoe.v11i6.5119.

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Recently more and more research methods are available to observe brain activity; for instance, Functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), Transcranial Magnetic Stimulation (TMS), Near Infrared Spectroscopy (NIRS), Electroencephalograph (EEG) or Magnetoencephalography (MEG), which provide new research opportunities for several applications. For example, control methods based on the evaluation of measurable signals of human brain activity. In the past few years, more mobile EEG (electroencephalogram) based brain activity biosensor and signal processing devi

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20

Agus Wirawan, I. Made, I. Gede Mahendra Darmawiguna, and Ida Bagus Nyoman Pascima. "Improving baseline reduction for emotion recognition based on electroencephalogram signals." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4263. http://dx.doi.org/10.11591/ijai.v13.i4.pp4263-4272.

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The baseline reduction method has been widely used to define electroencephalogram (EEG) signal patterns. However, because the baseline signal in this approach contains artifacts, the baseline reduction approach cannot perform optimally. As a result, decreasing artifacts in the baseline signal is critical. The mean, Gaussian, and Savitzky-Golay filters will be compared in this study to minimize artifacts in the baseline signal. Three secondary datasets are utilized to evaluate these approaches' capacity to remove artifacts. These three strategies are also tested with the convolution neural netw

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I, Made Agus Wirawan, Gede Mahendra Darmawiguna I, and Bagus Nyoman Pascima Ida. "Improving baseline reduction for emotion recognition based on electroencephalogram signals." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4263–72. https://doi.org/10.11591/ijai.v13.i4.pp4263-4272.

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The baseline reduction method has been widely used to define electroencephalogram (EEG) signal patterns. However, because the baseline signal in this approach contains artifacts, the baseline reduction approach cannot perform optimally. As a result, decreasing artifacts in the baseline signal is critical. The mean, Gaussian, and Savitzky-Golay filters will be compared in this study to minimize artifacts in the baseline signal. Three secondary datasets are utilized to evaluate these approaches' capacity to remove artifacts. These three strategies are also tested with the convolution neural netw

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22

Sim, Ji-Yeon, and Wonsik Ahn. "Electric signal composed of electroencephalogram and electromyogram." Korean Journal of Anesthesiology 55, no. 2 (2008): 263. http://dx.doi.org/10.4097/kjae.2008.55.2.263.

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23

Hellyar, M. T., E. C. Ifeachor, D. J. Mapps, E. M. Allen, and N. R. Hudson. "Expert system approach to electroencephalogram signal processing." Knowledge-Based Systems 8, no. 4 (1995): 164–73. http://dx.doi.org/10.1016/0950-7051(95)96213-b.

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NAMAZI, HAMIDREZA, TIRDAD SEIFI ALA, and VLADIMIR KULISH. "DECODING OF UPPER LIMB MOVEMENT BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL." Fractals 26, no. 05 (2018): 1850081. http://dx.doi.org/10.1142/s0218348x18500810.

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Analysis of human movements is an important category of research in biomedical engineering, especially for the rehabilitation purpose. The movement of limbs is investigated usually by analyzing the movement signals. Less efforts have been made to investigate how neural that correlate to the movements, are represented in the human brain. In this research, for the first time we decode the limb movements by fractal analysis of Electroencephalogram (EEG) signals. We investigated how the complexity of EEG signal changes in different limb movements in motor execution (ME), and motor imagination (MI)

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Liang, Wei, Liang Cheng, and Mingdong Tang. "Identity Recognition Using Biological Electroencephalogram Sensors." Journal of Sensors 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/1831742.

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Brain wave signal is a bioelectric phenomenon reflecting activities in human brain. In this paper, we firstly introduce brain wave-based identity recognition techniques and the state-of-the-art work. We then analyze important features of brain wave and present challenges confronted by its applications. Further, we evaluate the security and practicality of using brain wave in identity recognition and anticounterfeiting authentication and describe use cases of several machine learning methods in brain wave signal processing. Afterwards, we survey the critical issues of characteristic extraction,

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Zhang, Xuanpeng. "Deep Learning-Based Techniques for Electroencephalogram (EEG) Signal Denoising." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 922–27. http://dx.doi.org/10.62051/rrve8560.

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Electroencephalography (EEG) signals denoising is crucial for neural signal interpretation, particularly in complex noise conditions. Traditional methods often fail to address these conditions effectively. In contrast, deep learning-based techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Generative Adversarial Networks (GANs), and Transformers offer enhanced adaptability and robustness by tuning to diverse noise characteristics. This paper presents a systematic review and comparative analysis of updated EEG denoising models, utilizing the EEGdeno

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Kit, G. V. "ANALYSIS OF PEAK-WAVE DISCHARGES OF EEG WITH THE USE OF WAVELET TRANSFORMATIONS." Visnyk Universytetu “Ukraina”, no. 1 (28) 2020 (2020): 224–34. http://dx.doi.org/10.36994/2707-4110-2020-1-28-19.

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The method of analysis of electroencephalograms (EEG) on the basis of wavelet transformations is offered. Electroencephalogram (EEG) analysis is widely used in clinical practice for diagnosing such neurological diseases as epilepsy, Parkinson's disease and others. Traditional approaches to EEG analysis, generally accepted in the clinical diagnosis of diseases, are due to the fact that for a certain time after the stimulus, the EEG amplitudes are calculated at time intervals that depend on the frequency of signal quantization. Therefore, it is important to develop algorithms for classifying EEG

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Dalal, Virupaxi, and Satish Bhairannawar. "Efficient de-noising technique for electroencephalogram signal processing." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 603. http://dx.doi.org/10.11591/ijai.v11.i2.pp603-612.

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An electroencephalogram (EEG) is a recording of various frequencies of electrical activity in the brain. EEG signal is very useful for diagnosis of various brain related diseases at early stage to prevent severe issues which may lead to loss of life. The raw EEG signal captured through the leads contain different type of noises which is not susceptible for diagnosis. In this paper, an efficient algorithm is proposed to process the raw EEG signal to combat the noise. To obtain noiseless EEG data, the likelihood test ratio is applied to interference computation block. The likelihood ratio test c

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Virupaxi, Dalal, and Bhairannawar Satish. "Efficient de-noising technique for electroencephalogram signal processing." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 603–12. https://doi.org/10.11591/ijai.v11.i2.pp603-612.

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An electroencephalogram (EEG) is a recording of various frequencies of electrical activity in the brain. EEG signal is very useful for diagnosis of various brain related diseases at early stage to prevent severe issues which may lead to loss of life. The raw EEG signal captured through the leads contain different type of noises which is not susceptible for diagnosis. In this paper, an efficient algorithm is proposed to process the raw EEG signal to combat the noise. To obtain noiseless EEG data, the likelihood test ratio is applied to interference computation block. The likelihood ratio test c

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B.Krishna, Kumar. "Denoising of EEG Signal using Matlab and SIMULINK Techniques and Estimation of Power Spectral Density of EEG Signal using SIMULINK AR Models." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 418–22. https://doi.org/10.35940/ijeat.F8956.129219.

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The Electroencephalogram (EEG) is the standard technique for investigating the brain’s electrical activity in different psychological and pathological states. Analysis of Electroencephalogram (EEG) signal is a challenging task by reason of the presence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. Normally EEG signals falls in the frequency range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex. In thi

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Moghavvemi, M., and S. Mehrkanoon. "DETECTION OF THE ONSET OF EPILEPTIC SEIZURE SIGNAL FROM SCALP EEG USING BLIND SIGNAL SEPARATION." Biomedical Engineering: Applications, Basis and Communications 21, no. 04 (2009): 287–90. http://dx.doi.org/10.4015/s1016237209001301.

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Investigation of epileptic electroencephalogram (EEG) signal is one of the major areas of study in the field of signal processing. The ability to detect the seizure signal and its origin within the brain is of prime importance. This paper proposes a sequential blind signal separation (BSS) based system to extract the seizure signal from scalp EEG and to pinpoint the main location of seizure signal within the brain. BSS algorithm is used to demix the EEG signal into signals with independent features. Scalp time-mapping process is applied to determine the main location of the extracted seizure s

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Wirawan, I. Made Agus, Retantyo Wardoyo, Danang Lelono, and Sri Kusrohmaniah. "Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition." Emerging Science Journal 6, no. 6 (2022): 1255–73. http://dx.doi.org/10.28991/esj-2022-06-06-03.

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Participants' emotional reactions are strongly influenced by several factors such as personality traits, intellectual abilities, and gender. Several studies have examined the baseline reduction approach for emotion recognition using electroencephalogram signal patterns containing external and internal interferences, which prevented it from representing participants’ neutral state. Therefore, this study proposes two solutions to overcome this problem. Firstly, it offers a modified weighted mean filter method to eliminate the interference of the electroencephalogram baseline signal. Secondly, it

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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 (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.

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KOMAROV, P. V., and D. S. POTEKHIN. "INVESTIGATION OF A DYNAMICALLY CHANGING SIGNAL USING WAVELET TRANSFORMATIONS." Computational nanotechnology 11, no. 3 (2024): 34–42. http://dx.doi.org/10.33693/2313-223x-2024-11-3-34-42.

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In the presented work, a wavelet analysis of the patient’s electroencephalogram was performed, followed by the construction of a scalogram. This approach made it possible to identify the frequency components of the electroencephalogram and conduct a comprehensive analysis of them. The obtained results can be used to monitor the state of the patient’s brain activity. The main purpose of the study is to analyze and filter the signal to determine the main composite frequencies of the electroencephalographic signal, on the basis of which it is possible to determine the state of brain activity at c

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V, Rohith, Prajitha T.V, and Sweety Suresh. "EEG Signal Analyzing and Simulation Under Computerized Technological Support." International Journal of Engineering & Technology 7, no. 3.8 (2018): 38. http://dx.doi.org/10.14419/ijet.v7i3.8.15215.

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Electroencephalogram (EEG) is a method for acquiring the brain signals for diagnostic purposes. It tracks and records the brain wave patterns. This is a non-invasive technique. The idea behind is to categorize the EEG signal based on the frequency range. The steps include collecting EEG signals, pre-processing, feature extraction, feature selection and classification. The pre-processing eliminates the noises from the signal. EEG signal can be disintegrated by using discrete wavelet transform. The feature extraction methods are used to obtain the time-domain features of the EEG signal. Finally,

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Tong, Peiwen, Hui Xu, Yi Sun, Yongzhou Wang, Wei Wang, and Jiwei Li. "Electroencephalogram signal analysis with 1T1R arrays toward high-efficiency brain computer interface." AIP Advances 12, no. 12 (2022): 125108. http://dx.doi.org/10.1063/5.0117159.

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Brain computer interface (BCI) is a promising way for automatic driving and exploring brain functions. As the number of electrodes for electroencephalogram (EEG) acquisition continues to grow, the signal processing capabilities of BCI are facing challenges. Considering the bottlenecks of the Von Neumann architecture, it is increasingly difficult for the traditional digital computing pattern to meet the requirements of the EEG signal processing in terms of power consumption and efficiency. Here, we propose a 1T1R array-based EEG signal analysis system in which the biological likelihood of the m

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Elouaham, S., A. Dliou, N. Elkamoun, et al. "Denoising electromyogram and electroencephalogram signals using improved complete ensemble empirical mode decomposition with adaptive noise." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 829. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp829-836.

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The health of the brain and muscles depends on the proper analysis of electroencephalogram and electromyogram signals without noise. The latter blends into the recording of biomedical signals for external or internal reasons of the human body. Therefore, to obtain a more accurate signal, it is needed to select filtering techniques that minimize the noise. In this study, the techniques used are empirical mode decomposition and its variants. Among the new versions of variants is the improved complete ensemble empirical mode decomposition with adaptive noise. These methods are applied to electroe

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Elouaham, S., A. Dliou, N. Elkamoun, et al. "Denoising electromyogram and electroencephalogram signals using improved complete ensemble empirical mode decomposition with adaptive noise." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 829–36. https://doi.org/10.11591/ijeecs.v23.i2.pp829-836.

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The health of the brain and muscles depends on the proper analysis of electroencephalogram and electromyogram signals without noise. The latter blends into the recording of biomedical signals for external or internal reasons of the human body. Therefore, to obtain a more accurate signal, it is needed to select filtering techniques that minimize the noise. In this study, the techniques used are empirical mode decomposition and its variants. Among the new versions of variants is the improved complete ensemble empirical mode decomposition with adaptive noise. These methods are applied to electroe

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Lakra, Shikha, and Dr Shivangi Chandrakra. "CLASSIFYING ELECTROENCEPHALOGRAM (EEG) SIGNALS FOR ACCURATE MENTAL DISORDER ANALYSIS." International Journal of Engineering Applied Sciences and Technology 9, no. 09 (2025): 48–53. https://doi.org/10.33564/ijeast.2025.v09i09.008.

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Electroencephalogram (EEG) is a powerful tool for analysis of brain signals. The variation in pattern of EEG signals are useful in computing the mental disorders. In this presented work, the main aim is to study and analyze the techniques available for EEG signal classification. Additionally, contribute a deep learning based technique for accurately predict the mental health. This document discusses the over need of the proposed work, the problem domain and the proposed solution strategy. Additionally, the expected outcome and required tools and techniques have also been discussed.

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Zahari, Zarith Liyana, Mahfuzah Mustafa, and Rafiuddin Abdubrani. "The multimodal parameter enhancement of electroencephalogram signal for music application." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 414. http://dx.doi.org/10.11591/ijai.v11.i2.pp414-422.

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Blinding of modality has been influenced decision of multimodal in several circumstances. Sometimes, certain electroencephalogram (EEG) signal is omitted to achieve the highest accuracy of performance. Therefore, the aim for this paper is to enhance the multimodal parameters of EEG signals based on music applications. The structure of multimodal is evaluated with performance measure to ensure the implementation of parameter value is valid to apply in the multimodal equation. The modalities’ parameters proposed in this multimodal are weighted stress condition, signal features extraction, and mu

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Zarith, Liyana Zahari, Mustafa Mahfuzah, and Abdubrani Rafiuddin. "The multimodal parameter enhancement of electroencephalogram signal for music application." International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 414–22. https://doi.org/10.11591/ijai.v11.i2.pp414-422.

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Abstract:

Blinding of modality has been influenced decision of multimodal in several circumstances. Sometimes, certain electroencephalogram (EEG) signal is omitted to achieve the highest accuracy of performance. Therefore, the aim for this paper is to enhance the multimodal parameters of EEG signals based on music applications. The structure of multimodal is evaluated with performance measure to ensure the implementation of parameter value is valid to apply in the multimodal equation. The modalities’ parameters proposed in this multimodal are weighted stress condition, signal features extraction,

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Jing, Min, and Saeid Sanei. "A Novel Constrained Topographic Independent Component Analysis for Separation of Epileptic Seizure Signals." Computational Intelligence and Neuroscience 2007 (2007): 1–7. http://dx.doi.org/10.1155/2007/21315.

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Blind separation of the electroencephalogram signals (EEGs) using topographic independent component analysis (TICA) is an effective tool to group the geometrically nearby source signals. The TICA algorithm further improves the results if the desired signal sources have particular properties which can be exploited in the separation process as constraints. Here, the spatial-frequency information of the seizure signals is used to design a constrained TICA for the separation of epileptic seizure signal sources from the multichannel EEGs. The performance is compared with those from the TICA and oth

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Rodrigues, Pedro Miguel, Diamantino Rui Freitas, João Paulo Teixeira, Dílio Alves, and Carolina Garrett. "Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection." International Journal of Reliable and Quality E-Healthcare 7, no. 1 (2018): 40–59. http://dx.doi.org/10.4018/ijrqeh.2018010104.

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The World's health systems are now facing a global problem known as Alzheimer's disease (AD) that mainly affects the elderly. The goal of this work is to perform a classification methodology skilled with Artificial Neural Networks (ANN) to improve the discrimination accuracy amongst patients at AD different stages comparatively to the state-of-art. For that, several study features that characterized the Electroencephalogram (EEG) signals “slow-down” were extracted and presented to the ANN entries in order to classify the dataset. The classification results achieved in the present work are prom

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Lin, Ying, Hai-Feng Chen, Hui-Hong Chen, Zhen-Lun Yang, Ting-Cheng Chang, and Zeng-Rong Zhan. "Approximate Model for Stress Assessment Using Electroencephalogram Signal." Sensors and Materials 34, no. 2 (2022): 779. http://dx.doi.org/10.18494/sam3637.

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Kim, Dong-Jun, and Seung-Jin Woo. "Evaluation of Waist Pressure Using Electroencephalogram(EEG) Signal." Transactions of The Korean Institute of Electrical Engineers 60, no. 6 (2011): 1190–95. http://dx.doi.org/10.5370/kiee.2011.60.6.1190.

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Ghosn, Rania, Lydia Yahia-Cherif, Laurent Hugueville, et al. "Radiofrequency signal affects alpha band in resting electroencephalogram." Journal of Neurophysiology 113, no. 7 (2015): 2753–59. http://dx.doi.org/10.1152/jn.00765.2014.

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The aim of the present work was to investigate the effects of the radiofrequency (RF) electromagnetic fields (EMFs) on human resting EEG with a control of some parameters that are known to affect alpha band, such as electrode impedance, salivary cortisol, and caffeine. Eyes-open and eyes-closed resting EEG data were recorded in 26 healthy young subjects under two conditions: sham exposure and real exposure in double-blind, counterbalanced, crossover design. Spectral power of EEG rhythms was calculated for the alpha band (8–12 Hz). Saliva samples were collected before and after the study. Saliv

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Sheoran, Monika, Sanjeev Kumar, and Seema Chawla. "Methods of denoising of electroencephalogram signal: a review." International Journal of Biomedical Engineering and Technology 18, no. 4 (2015): 385. http://dx.doi.org/10.1504/ijbet.2015.071012.

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Al-Kadi, Mahmoud, Mamun Reaz, and Mohd Ali. "Evolution of Electroencephalogram Signal Analysis Techniques during Anesthesia." Sensors 13, no. 5 (2013): 6605–35. http://dx.doi.org/10.3390/s130506605.

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Ferenets, Rain, Ann Vanluchene, Tarmo Lipping, Björn Heyse, and Michel M. R. F. Struys. "Behavior of Entropy/Complexity Measures of the Electroencephalogram during Propofol-induced Sedation." Anesthesiology 106, no. 4 (2007): 696–706. http://dx.doi.org/10.1097/01.anes.0000264790.07231.2d.

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Background Several new measures based on the regularity of the electroencephalogram signal for the assessment of depth of anesthesia/sedation have been proposed recently. In this study we analyze the influence of remifentanil and electroencephalogram frequency content of the performance of a set of such measures. Methods Forty-five patients with American Society of Anesthesiologists physical status I were randomly allocated to one of three groups according to the received dose of predicted effect compartment-controlled remifentanil (0, 2, and 4 ng/ml). All 45 patients received stepwise increas

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Lara-Arellano, Edgar, Andras Takacs, Saul Tovar-Arriaga, and Juvenal Rodríguez-Reséndiz. "Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification." Eng 6, no. 4 (2025): 75. https://doi.org/10.3390/eng6040075.

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This work presents a method for classifying EEG (Electroencephalogram) signals generated when a person concentrates on specific words, defined as “Imagined Speech”. Imagined speech is essential to enhance problem-solving, memory, and language development. In addition, imagined speech is beneficial because of its applications in therapy fields like managing anxiety or improving communication skills. EEG measures the electrical activity of the brain. EEG signal classification is difficult as the machine learning (ML) algorithm has to learn how to categorize the signal linked to the imagined word

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