Journal articles: 'ELECTROENCEPHALOGRAPHY SIGNAL'

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Lee, Woonghee, Jaewoo Yang, Doyeong Park, and Younghoon Kim. "Automated Clinical Impression Generation for Medical Signal Data Searches." Applied Sciences 13, no. 15 (August 3, 2023): 8931. http://dx.doi.org/10.3390/app13158931.

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Medical retrieval systems have become significantly important in clinical settings. However, commercial retrieval systems that heavily rely on term-based indexing face challenges when handling continuous medical data, such as electroencephalography data, primarily due to the high cost associated with utilizing neurologist analyses. With the increasing affordability of data recording systems, it becomes increasingly crucial to address these challenges. Traditional procedures for annotating, classifying, and interpreting medical data are costly, time consuming, and demand specialized knowledge. While cross-modal retrieval systems have been proposed to address these challenges, most concentrate on images and text, sidelining time-series medical data like electroencephalography data. As the interpretation of electroencephalography signals, which document brain activity, requires a neurologist’s expertise, this process is often the most expensive component. Therefore, a retrieval system capable of using text to identify relevant signals, eliminating the need for expert analysis, is desirable. Our research proposes a solution to facilitate the creation of indexing systems employing electroencephalography signals for report generation in situations where reports are pending a neurologist review. We introduce a method incorporating a convolutional-neural-network-based encoder from DeepSleepNet, which extracts features from electroencephalography signals, coupled with a transformer which learns the signal’s auto-correlation and the relationship between the signal and the corresponding report. Experimental evaluation using real-world data revealed our approach surpasses baseline methods. These findings suggest potential advancements in medical data retrieval and a decrease in reliance on expert knowledge for electroencephalography signal analysis. As such, our research represents a significant stride towards making electroencephalography data more comprehensible and utilizable in clinical environments.

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He, Bryan D., Mosalam Ebrahimi, Leon Palafox, and Lakshminarayan Srinivasan. "Signal quality of endovascular electroencephalography." Journal of Neural Engineering 13, no. 1 (January 6, 2016): 016016. http://dx.doi.org/10.1088/1741-2560/13/1/016016.

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Pawar, Sanjay S., and Sangeeta R. Chougule. "Predication and Analysis of Epileptic Seizure Neurological Disorder using Intracranial Electroencephalography (iEEG)." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 16 (February 25, 2021): 197–205. http://dx.doi.org/10.37394/232014.2020.16.22.

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Epileptic seizure is one of the neurological brain disorder approximately 50 million of world’s population is affected. Diagnosis of seizure is done using medical test Electroencephalography. Electroencephalography is a technique to record brain signal by placing electrodes on scalp. Electroencephalography suffers from disadvantage such as low spatial resolution and presence of artifact. Intracranial Electroencephalography is used to record brain electrical activity by mounting strip, grid and depth electrodes on surface of brain by surgery. Online standard Intracranial Electroencephalography data is analyzed by our system for predication and analysis of Epileptic seizure. The pre-processing of Intracranial Electroencephalography signal is done and is further analyzed in wavelet domain by implementation of Daubechies Discrete Wavelet Transform. Features were extracted to classify as preictal and ictal state. Analysis of preictal state was carried out for predication of seizure. Intracranial Electroencephalography signals provide better result and accuracy in seizure detection and predication. Earlier warning can also be issued to control seizure with anti- epileptic drugs

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Yuan, Lixue, Yinyan Fan, Quanxi Gan, and Huibin Feng. "Clinical Diagnosis of Psychiatry Based on Electroencephalography." Journal of Medical Imaging and Health Informatics 11, no. 3 (March 1, 2021): 955–63. http://dx.doi.org/10.1166/jmihi.2021.3338.

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At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

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Yong, Goh Chien, Tahir Ahmad, and Normah Maan. "Spatial Interaction Image of Electroencephalography Signal during Epileptic Seizure on Flat Electroencephalography." Journal of Mathematics and Statistics 13, no. 1 (January 1, 2017): 46–56. http://dx.doi.org/10.3844/jmssp.2017.46.56.

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Tran, Yvonne. "EEG Signal Processing for Biomedical Applications." Sensors 22, no. 24 (December 13, 2022): 9754. http://dx.doi.org/10.3390/s22249754.

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Wu, J., E. C. Ifeachor, N. R. Hudson, S. K. Wimalaratna, and E. M. Allen. "Intelligent artefact identification in electroencephalography signal processing." IEE Proceedings - Science, Measurement and Technology 144, no. 5 (September 1, 1997): 193–201. http://dx.doi.org/10.1049/ip-smt:19971318.

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Sanei, Saeid, Saideh Ferdowsi, Kianoush Nazarpour, and Andrzej Cichocki. "Advances in Electroencephalography Signal Processing [Life Sciences]." IEEE Signal Processing Magazine 30, no. 1 (January 2013): 170–76. http://dx.doi.org/10.1109/msp.2012.2219675.

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Yan, Zhaokun, Xiangquan Yang, and Yu Jin. "Considerate motion imagination classification method using deep learning." PLOS ONE 17, no. 10 (October 20, 2022): e0276526. http://dx.doi.org/10.1371/journal.pone.0276526.

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In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the proposed model can accurately represent the distribution of electroencephalography electrodes in non-Euclidean space and fully consider the spatial correlation between electrodes. In addition, the spatial-spectral-temporal multi-dimensional feature information was extracted from the spatial-temporal graph representation and spatial-spectral graph representation transformed from the original electroencephalography signal using the dual branch architecture. Finally, the attention mechanism and global feature aggregation module were designed and combined with graph convolution to adaptively capture the dynamic correlation intensity and effective feature of electroencephalography signals in various dimensions. A series of contrast experiments and ablation experiments on several different public brain-computer interface datasets demonstrated that the excellence of proposed method. It is worth mentioning that, the proposed model is a general framework for the classification of electroencephalography signals, which is suitable for emotion recognition, sleep staging and other fields based on electroencephalography research. Moreover, the model has the potential to be applied in the medical field of motion imagination rehabilitation in real life.

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Redkar, Sangram. "Using Deep Learning for Human Computer Interface via Electroencephalography." IAES International Journal of Robotics and Automation (IJRA) 4, no. 4 (December 1, 2015): 292. http://dx.doi.org/10.11591/ijra.v4i4.pp292-310.

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<table class="Heading1Char" width="593" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>In this paper, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised and unsupervised machine learning models, for the EEG motor imagery classification are identified. Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, it is important to have robust signal processing and machine learning modules which operate on the EEG signals and estimate the current thought or intent of the user. Motor Imagery (imaginary hand and leg movements) signals are acquired using the Emotiv EEG headset. The signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques are applied and validated. The performances of various ML techniques are compared and some important observations are reported. Further, Deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is presented and analyzed by performing classification by using the ML techniques. It is shown that hand engineered ‘ad-hoc’ feature extraction techniques are less reliable than the automated (‘Deep Learning’) feature learning techniques. All the findings in this research, can be used by the BCI research community for building motor imagery based BCI applications such as Gaming, Robot control and autonomous vehicles.</p></td></tr></tbody></table>

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Chaddad, Ahmad, Yihang Wu, Reem Kateb, and Ahmed Bouridane. "Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques." Sensors 23, no. 14 (July 16, 2023): 6434. http://dx.doi.org/10.3390/s23146434.

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

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Ramele, Rodrigo, Ana Villar, and Juan Santos. "EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces." Brain Sciences 8, no. 11 (November 16, 2018): 199. http://dx.doi.org/10.3390/brainsci8110199.

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The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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McMillan, Rebecca, Anna Forsyth, Doug Campbell, Gemma Malpas, Elizabeth Maxwell, Juergen Dukart, Joerg F. Hipp, and Suresh Muthukumaraswamy. "Temporal dynamics of the pharmacological MRI response to subanaesthetic ketamine in healthy volunteers: A simultaneous EEG/fMRI study." Journal of Psychopharmacology 33, no. 2 (January 21, 2019): 219–29. http://dx.doi.org/10.1177/0269881118822263.

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Background: Pharmacological magnetic resonance imaging has been used to investigate the neural effects of subanaesthetic ketamine in healthy volunteers. However, the effect of ketamine has been modelled with a single time course and without consideration of physiological noise. Aims: This study aimed to investigate ketamine-induced alterations in resting neural activity using conventional pharmacological magnetic resonance imaging analysis techniques with physiological noise correction, and a novel analysis utilising simultaneously recorded electroencephalography data. Methods: Simultaneous electroencephalography/functional magnetic resonance imaging and physiological data were collected from 30 healthy male participants before and during a subanaesthetic intravenous ketamine infusion. Results: Consistent with previous literature, we show widespread cortical blood-oxygen-level dependent signal increases and decreased blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex following ketamine. However, the latter effect was attenuated by the inclusion of motion regressors and physiological correction in the model. In a novel analysis, we modelled the pharmacological magnetic resonance imaging response with the power time series of seven electroencephalography frequency bands. This showed evidence for distinct temporal time courses of neural responses to ketamine. No electroencephalography power time series correlated with decreased blood-oxygen-level dependent signal in the subgenual anterior cingulate cortex. Conclusions: We suggest the decrease in blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex typically seen in the literature is the result of physiological noise, in particular cardiac pulsatility. Furthermore, modelling the pharmacological magnetic resonance imaging response with a single temporal model does not completely capture the full spectrum of neuronal dynamics. The use of electroencephalography regressors to model the response can increase confidence that the pharmacological magnetic resonance imaging is directly related to underlying neural activity.

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Suma, K. V., D. Venkatesh, Arun Kumar, Manjula Suryabhatla, Tejaswini M. Gowda, and M. Thejashwini. "Stress Level Detection Using Electroencephalography Signals." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4223–28. http://dx.doi.org/10.1166/jctn.2020.9050.

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Stress is the pressure that is experienced by humans. The impact of stress depends upon the type of stress the individual is experiencing. A positive stress may lead to the individual to feel motivated while a negative stress may impact the individual’s professional life or relationships. In this work, the approach of detecting the level of individual stress through EEG signal is presented. The EEG signal consists of set of components like Alpha, Beta and Gamma, out of which the dominating component plays a crucial role in determining the stress level. Results shows that 93.33%, 83.33% and 90% of classification accuracy, 87.5%, 80% and 85.71% of sensitivity and 95.45%, 86.66% and 91.30% of specificity for low, medium and high stress respectively is obtained. This work can be used to analyze the region of brain that is contributing more towards the individual’s stress.

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

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

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Shin, Sung-Wook, Jung-Hyun Park, Woo-Jin Lee, Sung-Ho Kang, Hyunggun Kim, and Sung-Taek Chung. "Analysis of Electroencephalography Signals on the Contents of Cognitive Function Game: Attention and Memory." Journal of Medical Imaging and Health Informatics 10, no. 6 (June 1, 2020): 1452–58. http://dx.doi.org/10.1166/jmihi.2020.3069.

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In the current study, contents of cognitive function game were developed, and variations in Electroencephalography signals were measured and compared before performing the game tasks and during performing the game. The study sought to assess how much the game contents activated brain to see if they were suitable for cognitive functional training. For Electroencephalography signal analysis, power spectral analysis was implemented to classify signals according to frequency. To test signal variation according to the degree of brain activation before and after performing the game, variation comparison and paired t-test were conducted. Results showed that there was reduction in α wave signaling which implied that the subjects concentrate on the content, and increase in β wave signifying that they were engaged in cognitive activities such as remembering and assessing. Consequentially, the produced contents in this study are expected to be useful for cognitive functional training.

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Zhu, Hangyu, Cong Fu, Feng Shu, Huan Yu, Chen Chen, and Wei Chen. "The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods." Bioengineering 10, no. 5 (May 10, 2023): 573. http://dx.doi.org/10.3390/bioengineering10050573.

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The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intrinsic characteristics. In this paper, the effect of a coupled EEG signal in an EOG signal on automatic sleep staging is explored. The blind source separation algorithm was used to extract a clean prefrontal EEG signal. Then the raw EOG signal and clean prefrontal EEG signal were processed to obtain EOG signals coupled with different EEG signal contents. Afterwards, the coupled EOG signals were fed into a hierarchical neural network, including a convolutional neural network and recurrent neural network for automatic sleep staging. Finally, an exploration was performed using two public datasets and one clinical dataset. The results showed that using a coupled EOG signal could achieve an accuracy of 80.4%, 81.1%, and 78.9% for the three datasets, slightly better than the accuracy of sleep staging using the EOG signal without coupled EEG. Thus, an appropriate content of coupled EEG signal in an EOG signal improved the sleep staging results. This paper provides an experimental basis for sleep staging with EOG signals.

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Shelishiyah, R., M. Bharani Dharan, T. Kishore Kumar, R. Musaraf, and Thiyam Deepa Beeta. "Signal Processing for Hybrid BCI Signals." Journal of Physics: Conference Series 2318, no. 1 (August 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2318/1/012007.

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Abstract The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.

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Gupta, Nidhi, and Gyaninder Singh. "Electroencephalography-based monitors." Journal of Neuroanaesthesiology and Critical Care 02, no. 03 (December 2015): 168–78. http://dx.doi.org/10.4103/2348-0548.165030.

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AbstractAn electroencephalogram (EEG), detects changes and abnormalities in the electrical activity of the brain and thus provides a way to dynamically assess brain function. EEG may be used to diagnose and manage a number of clinical conditions such as epilepsy, convulsive and non-convulsive status epilepticus, encephalitis, barbiturate coma, brain death, etc., EEG provides a large amount of information to the anaesthesiologist for routine clinical practice as depth of anaesthesia monitors and detection of sub-clinical seizures; and also for understanding the complex mechanisms of anaesthesia-induced alteration of consciousness. In the initial years, the routine clinical applicability of EEG was hindered by the complexity of the raw EEG signal. However, with technological advancement, several EEG-derived dimensionless indices have been developed that correlate with the depth of the hypnotic component of anaesthesia and are easy to interpret. Similarly, with the development of quantitative EEG tools, the routine use of continuous EEG is ever expanding in the Intensive Care Units. This review, describe various commonly used EEG-based monitors and their clinical applicability in the field of anaesthesia and critical care.

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Ferdous, Jannatul, Sujan Ali, Ekramul Hamid, and Khademul Islam Molla. "Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform." International Journal of Advanced Robotic Systems 18, no. 1 (January 1, 2021): 172988142199226. http://dx.doi.org/10.1177/1729881421992269.

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This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.

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Alkhorshid, Daniel Rostami, Seyyedeh Fatemeh Molaeezadeh, and Mikaeil Rostami Alkhorshid. "Analysis: Electroencephalography Acquisition System: Analog Design." Biomedical Instrumentation & Technology 54, no. 5 (September 1, 2020): 346–51. http://dx.doi.org/10.2345/0899-8205-54.5.346.

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Abstract Electroencephalography (EEG) is a sensitive and weak biosignal that varies from person to person. It is easily affected by noise and artifacts. Hence, maintaining the signal integrity to design an EEG acquisition system is crucial. This article proposes an analog design for acquiring EEG signals. The proposed design consists of eight blocks: (1) a radio-frequency interference filter and electro-static discharge protection, (2) a preamplifier and second-order high-pass filter with feedback topology and an unblocking mechanism, (3) a driven right leg circuit, (4) two-stage main and variable amplifiers, (5) an eight-order anti-aliasing filter, (6) a six-order 50-Hz notch filter (optional), (7) an opto-isolator circuit, and (8) an isolated power supply. The maximum gain of the design is approximately 94 dB, and its bandwidth ranges from approximately 0.18 to 120 Hz. The depth of the 50-Hz notch filter is −35 dB. Using this filter is optional because it causes EEG integrity problems in frequencies ranging from 40 to 60 Hz.

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Zhang, Qing, Pingping Wang, Yan Liu, Bo Peng, Yufu Zhou, Zhiyong Zhou, Baotong Tong, Bensheng Qiu, Yishan Zheng, and Yakang Dai. "A real-time wireless wearable electroencephalography system based on Support Vector Machine for encephalopathy daily monitoring." International Journal of Distributed Sensor Networks 14, no. 5 (May 2018): 155014771877956. http://dx.doi.org/10.1177/1550147718779562.

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Wearable electroencephalography systems of out-of-hospital can both provide complementary recordings and offer several benefits over long-term monitoring. However, several limitations were present in these new-born systems, for example, uncomfortable for wearing, inconvenient for retrieving the recordings by patients themselves, unable to timely provide accurate classification, and early warning information. Therefore, we proposed a wireless wearable electroencephalography system for encephalopathy daily monitoring, named as Brain-Health, which focused on the following three points: (a) the monitoring device integrated with electroencephalography acquisition sensors, signal processing chip, and Bluetooth, attached to a sport hat or elastic headband; (b) the mobile terminal with dedicated application, which is not only for continuous recording and displaying electroencephalography signal but also for early warning in real time; and (c) the encephalopathy’s classification algorithm based on intelligent Support Vector Machine, which is used in a new application of wearable electroencephalography for encephalopathy daily monitoring. The results showed a high mean accuracy of 91.79% and 93.89% in two types of classification for encephalopathy. In conclusion, good performance of our Brain-Health system indicated the feasibility and effectiveness for encephalopathy daily monitoring and patients’ health self-management.

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Karaduman, Mucahit, and Ali Karci. "Deep and Statistical Features Classification Model for Electroencephalography Signals." Traitement du Signal 39, no. 5 (November 30, 2022): 1517–25. http://dx.doi.org/10.18280/ts.390508.

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People strive to make sense of the complex electroencephalography (EEG) data generated by the brain. This study uses a prepared dataset to examine how easily people with alcohol use disorder (AUD) could be distinguished from healthy people. The signals from each electrode are connected to one another and are first represented as a single signal. The signal is then denoised through variation mode decomposition (VMD) during the preprocessing stage. The statistical and deep feature extraction phases are the two subsequent phases. The crucial step in the suggested strategy is to classify data using a combination of these two unique qualities. Deep and statistical feature performance was evaluated independently. Then, using the eigenvectors created by merging all of the collected features, classification was carried out using our DSFC (Deep - Statistical Features Classification) model. Although the classification accuracy rate using only statistical features was 81.2 percent and the classification accuracy rate using only deep learning was 95.71 percent, the classification accuracy rate utilizing hybrid features created using the suggested DSFC technique was 99.2%. Therefore, it can be proven that combining statistical and deep features can produce beneficial results.

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

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

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Namazi, Hamidreza. "Investigating the Brain Development in Newborns by Information-Based Analysis of Electroencephalography (EEG) Signal." Fluctuation and Noise Letters 19, no. 04 (July 4, 2020): 2050043. http://dx.doi.org/10.1142/s0219477520500431.

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In this paper, we employ the information theory to analyze the development of brain as the newborn ages. We compute the Shannon entropy of Electroencephalography (EEG) signal during sleep for 10 groups of newborns who are aged 36 weeks to 45 weeks (first to the last group). Based on the obtained results, EEG signals for newborns in 36 weeks have the lowest information content, whereas EEG signals for newborns in 45 weeks show the greatest information content. Therefore, we concluded that the information content of EEG signal increases as the age of newborn increases. Th result of statistical analysis demonstrated that the influence of increment of age of newborn on the variations of informant content of their EEG signals was significant.

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Kim, Seonho, Jungjoon Kim, and Hong-Woo Chun. "Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease." International Journal of Environmental Research and Public Health 15, no. 8 (August 15, 2018): 1750. http://dx.doi.org/10.3390/ijerph15081750.

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Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis technique to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). Unlike the other types of data, applying deep learning to bio-signal data, which is in the form of time series of real numbers, has many issues that need to be resolved in preprocessing, learning, and analysis. Such issues include leaving feature selection, learning parts that are black boxes, difficulties in recognizing and identifying effective features, high computational complexities, etc. In this paper, to solve these issues, we provide an encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques. To demonstrate its advantages, we provide the results of three experiments conducted with EEG data of the University of California Irvine, which are a real-world benchmark bio-signal dataset. After converting the bio-signals (in the form of waves), which are a real number time series, into a sequence of symbols or a sequence of wavelet patterns that are converted into symbols, through encoding, the proposed model vectorizes the symbols by learning the sequence using deep learning-based natural language processing. The models of each class can be constructed through learning from the vectorized wavelet patterns and training data. The implemented models can be used for prediction and diagnosis of diseases by classifying the new data. The proposed method enhanced data readability and intuition of feature selection and learning processes by converting the time series of real number data into sequences of symbols. In addition, it facilitates intuitive and easy recognition, and identification of influential patterns. Furthermore, real-time large-capacity data analysis is facilitated, which is essential in the development of real-time analysis diagnosis systems, by drastically reducing the complexity of calculation without deterioration of analysis performance by data simplification through the encoding process.

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Lee, Tae-Ju, Seung-Min Park, and Kwee-Bo Sim. "Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/754539.

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This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.

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Melinda, Melinda, I. Ketut Agung Enriko, Muhammad Furqan, Muhammad Irhamsyah, Yunidar Yunidar, and Nurlida Basir. "The effect of power spectral density on the electroencephalography of autistic children based on the welch periodogram method." JURNAL INFOTEL 15, no. 1 (February 2, 2023): 111–20. http://dx.doi.org/10.20895/infotel.v15i1.874.

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Autism spectrum disorder (ASD) is a serious mental disorder affecting social behavior. Some children also face intellectual delay. In people with ASD, the signals detected have abnormalities compared to normal people. This can be a reference in diagnosing the disorder with electroencephalography (EEG). This study will analyze the effect of Power spectral density (PSD) on the EEG of autistic children and also compare it with the PSD value on the EEG of normal children using the Welch Periodogram method approach. In the preprocessing stage, the Independent Component Analysis (ICA) method will be applied to remove artifacts, and a Finite Impulse Response (FIR) filter to reduce noise in the EEG signal. The study results indicate differences in the PSD values obtained in the autistic and normal EEG signals. The PSD value obtained in the autistic EEG signal is higher than the normal EEG signal in all frequency sub-bands. From the study results, the highest PSD value obtained by the autistic EEG signal is in the delta sub-band, which is 54.06 dB/Hz, while the normal EEG signal is only 33.14 dB/Hz at the same frequency sub-band. And in the Alpha and Beta sub-bands, the normal EEG signal increases the PSD value, while in the autistic EEG signal, the PSD value decreases in the Alpha and Beta sub-bands. In addition, FIR and ICA methods can also reduce noise and artifacts contained in autistic and normal EEG signals.

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Pawar, Sanjay Shamrao, and Sangeeta Rajendra Chougule. "Classification and Severity Measurement of Epileptic Seizure using Intracranial Electroencephalogram (iEEG)." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (December 10, 2020): 36–41. http://dx.doi.org/10.35940/ijitee.b8249.1210220.

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The Epileptic seizure is one of major neurological brain disorders and about 50 million of world’s population is affected by it. Electroencephalography is medical test which records brain signal by mounting electrodes on scalp or brain cortex to diagnosis seizure. Scalp Electroencephalography has low spatial resolution and presence of external artifact as compared to Intracranial Electroencephalography. In Intracranial Electroencephalography strip, grid and depth type of electrodes are implanted on cortex of brain by surgery to measure brain signal. Analysis of brain signal was carried out in past in diagnosis of Epileptic seizure. Seizure classification and Severity measurement of Epileptic Seizure are still challenging areas of research. Seizures are classified as focal seizure, generalized and secondary generalized seizure depending upon the area of brain which it generates and how it spreads. Classification of seizure helps in treatment of seizure and during brain surgery to operate on brain part which is responsible for continuous seizures generation. Developed seizure classification algorithm classifies seizures as focal Seizure, generalized Seizure and secondary generalized seizure depending on the percentage of iEEG electrodes detecting seizure activity. Seizure severity measurement scale is developed by modification in National Hospital Seizure Severity Scale. Seizures are graded as Mild seizure, Moderate seizure and severe seizure depending on its severity. Seizure Classification and Seizure Severity Measurement improves life quality of Epileptic patients by proper drug management.

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Shankar, A., S. Muttan, and D. Vaithiyanathan. "Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface." Journal of Medical Imaging and Health Informatics 11, no. 12 (December 1, 2021): 2918–27. http://dx.doi.org/10.1166/jmihi.2021.3904.

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Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

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Lv, Chao, and Bo Song. "Classification of epileptic EEG based on improved empirical wavelet transform." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2400/1/012010.

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Abstract Electroencephalography (EEG) is the most commonly used method in the diagnosis of epilepsy diseases. In order to identify epilepsy EEG signals more effectively, an automatic identification method of epilepsy EEG signals based on improved empirical wavelet transform (EWT) is proposed. Firstly, in view of the difficulty of spectral division in the EEG signal processing of epilepsy by empirical wavelet transform, an improvement measure is proposed, that is, the average difference spectrum of the signal is obtained to replace the signal spectrum in the empirical wavelet transform, and then a number of component signal with epileptic characteristic can be obtained from the original signal. Afterward, feature extraction and classification are completed through a common spatial pattern and AdaBoost algorithm. Simulation analysis was carried out on the Bonn epilepsy EEG data set, and the EEG signals of healthy people and epilepsy patients were identified and classified in the interictal and ictal periods, and high classification accuracy was achieved.

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Luján, Miguel, María Jimeno, Jorge Mateo Sotos, Jorge Ricarte, and Alejandro Borja. "A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia." Electronics 10, no. 23 (December 5, 2021): 3037. http://dx.doi.org/10.3390/electronics10233037.

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In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

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Suwandi, G. R. F., S. N. Khotimah, and Suprijadi. "Electroencephalography Signal Power Spectral Density from Measurements in Room with and Without Faraday Cage: A Comparative Study." Journal of Physics: Conference Series 2243, no. 1 (June 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2243/1/012002.

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Abstract Electroencephalography (EEG) is a method for recording the brain’s electrical activity through electrodes placed on the scalp’s surface. This EEG has its problem, namely signal interference from outside the system or artifacts. Ways to eliminate this signal interference can be made in various ways, including handling signal sources outside the system or removing interference signals during the EEG signal processing process. One way is to isolate the measurement room from signal interference using a Faraday cage. In this study, we will compare the results of EEG signal processing in the form of power spectral density (PSD) from measurements in a room without a Faraday cage and a room with a Faraday cage. We find that the average value of the change in PSD from the measurement results in the two rooms had a level of difference that varied between 0.71%-66%. The location of the electrodes that have a high difference value is the frontal and parietal areas.

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Livint Popa, Livia, Hanna Dragos, Cristina Pantelemon, Olivia Verisezan Rosu, and Stefan Strilciuc. "The Role of Quantitative EEG in the Diagnosis of Neuropsychiatric Disorders." Journal of Medicine and Life 13, no. 1 (January 2020): 8–15. http://dx.doi.org/10.25122/jml-2019-0085.

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Quantitative electroencephalography (QEEG) is a modern type of electroencephalography (EEG) analysis that involves recording digital EEG signals which are processed, transformed, and analyzed using complex mathematical algorithms. QEEG has brought new techniques of EEG signals feature extraction: analysis of specific frequency band and signal complexity, analysis of connectivity, and network analysis. The clinical application of QEEG is extensive, including neuropsychiatric disorders, epilepsy, stroke, dementia, traumatic brain injury, mental health disorders, and many others. In this review, we talk through existing evidence on the practical applications of this clinical tool. We conclude that to date, the role of QEEG is not necessarily to pinpoint an immediate diagnosis but to provide additional insight in conjunction with other diagnostic evaluations in order to objective information necessary for obtaining a precise diagnosis, correct disease severity assessment, and specific treatment response evaluation.

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Viswanadham, Talabattula, and Rajesh Kumar P. "Artefacts Removal from ECG Signal: Dragonfly Optimization-based Learning Algorithm for Neural Network-enhanced Adaptive Filtering." Scalable Computing: Practice and Experience 21, no. 2 (June 27, 2020): 247–63. http://dx.doi.org/10.12694/scpe.v21i2.1657.

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Electrocardiogram (ECG) artefact removal is the major research topic as the pure ECG signals are an essential part of diagnosing heart-related problems. ECG signals are highly prominent to the interaction with the other signals like the Electromyography (EMG), Electroencephalography (EEG), and Electrooculography (EOG) signals and the interference mainly occurs at the time of recording. The removal of the artefacts from the ECG signal is a hectic challenge, for which, a novel algorithm is proposed in this work. The proposed method utilizes the adaptive filter termed as the (Dragonfly optimization + Levenberg Marqueret learning algorithm) DLM-based Nonlinear Autoregressive with eXogenous input (NARX) neural network for the removal of the artefacts from the ECG signals. Once the artefact signal is identified using the adaptive filter, the identified signal is subtracted from the primary signal that is composed of the ECG signal and the artefacts through an adaptive subtraction procedure. The clean signal thus obtained is used for effective diagnosis purposes, and the experimentation performed to prove the effectiveness of the proposed method proves that the proposed method obtained a maximum Signal-to-noise ratio (SNR) of 52.8789 dB, a minimum error of 0.1832, and minimum error of 0.428.

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Ndaro, Nyakuru Z., and Shu-Yi Wang. "Effects of Fatigue Based on Electroencephalography Signal during Laparoscopic Surgical Simulation." Minimally Invasive Surgery 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/2389158.

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Background. Following recent advances in technology, there is a growing interest in studying fatigue based on electrophysiological signals as a means of monitoring brain activity. While some existing works relate fatigue to performance, others consider the two as independent entities. Therefore, we must explore this intricate issue, particularly in laparoscopic training, for the sake of patient safety. Objective. This paper explores and evaluates effects of fatigue on efficiency and accuracy based on laparoscopic surgical training using Electroencephalography (EEG) signal. Materials and Methods. 20 college students performed peg transfer task on laparoscopic simulator, with real-time recording of EEG signals for each subject. To monitor degree of fatigue, a real-time fatigue monitoring system based on fatigue analysis algorithm was designed through the use of EEG in alpha (α) and theta (θ) rhythms. We designed data acquisition and fatigue analysis modules based on MATLAB platform. BrainLink was used to record EEG signals and send them to personal computer wirelessly via Bluetooth. While artifacts from the captured EEG signals were removed using Blind Source Separation (BSS), α and θ rhythms were extracted using wavelet analysis. Fatigue was evaluated based on Regression Model and Mahalanobis Distance (DC), and its threshold was determined from the experimental results using Receiver Operating Characteristic (ROC) curve analysis. Results. Completion time and number of errors behaved like a decreasing function during the first few trials while increasing afterwards with the increasing of perceived fatigue level. The results indicate that learning curve of the subjects is increasing until 13th trials when they have attained maximum learning benefits and decreases afterwards due to fatigue. Conclusion. Regression analysis shows that there are significant learning and fatigue effects when peg transfer task in the training is repeated in a series of trials. However, for the training to be effective and efficient, there should be monitoring during the training to observe where in the learning curve a trainee gains maximum learning benefits. Furthermore, fatigue is a significant indicator of efficiency and accuracy in terms of completion time and errors, respectively.

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Bruhn, Jörgen, Thomas W. Bouillon, Andreas Hoeft, and Steven L. Shafer. "Artifact Robustness, Inter- and Intraindividual Baseline Stability, and Rational EEG Parameter Selection." Anesthesiology 96, no. 1 (January 1, 2002): 54–59. http://dx.doi.org/10.1097/00000542-200201000-00015.

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Background Artifact robustness (i.e., size of deviation of an electroencephalographic parameter value from baseline caused by artifacts) and baseline stability (i.e., consistency of median baseline values) of electroencephalographic parameters profoundly influence electroencephalography-based pharmacodynamic parameter estimation and the usefulness of the processed electroencephalogram as measure of the arousal state of the central nervous system (depth of anesthesia). In this study, the authors compared the artifact robustness and the interindividual and intraindividual baseline stability of several univariate descriptors of the electroencephalogram (Shannon entropy, approximate entropy, spectral edge frequency 95, delta ratio, and canonical univariate parameter). Methods Electroencephalographic data of 16 healthy volunteers before and after administration of an intravenous bolus of propofol (2 mg/kg body weight) were analyzed. Each volunteer was studied twice. The baseline electroencephalogram was recorded for a median of 18 min before drug administration. For each electroencephalographic descriptor, the authors calculated the following: (1) baseline variability (= (median baseline - median effect) [i.e., signal]/SD baseline [i.e., noise]) without artifact rejection; (2) baseline variability with artifact rejection; and (3) baseline stability within and between individuals (= (median baseline - median effect) averaged over all volunteers/SD of all median baselines). Results Without artifact rejection, Shannon entropy and canonical univariate parameter displayed the highest signal-to-noise ratio. After artifact rejection, approximate entropy, Shannon entropy, and the canonical univariate parameter displayed the highest signal-to-noise ratio. Baseline stability within and between individuals was highest for approximate entropy. Conclusions With regard to robustness against artifacts, the electroencephalographic entropy parameters and the canonical univariate parameter were superior to spectral edge frequency 95 and delta ratio. Electroencephalographic approximate entropy displayed the best interindividual and intraindividual baseline stability.

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Darmakusuma, Reza, Ary Setijadi Prihatmanto, Adi Indrayanto, Tati Latifah Mengko, Lidwina Ayu Andarini, and Achmad Furqon Idrus. "Analysis of Arm Movement Prediction by Using the Electroencephalography Signal." Makara Journal of Technology 20, no. 1 (April 26, 2016): 38. http://dx.doi.org/10.7454/mst.v20i1.3054.

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Darmakusuma, Reza, Ary Setijadi Prihatmanto, Adi Indrayanto, Tati Latifah Mengko, Lidwina Ayu Andarini, and Achmad Furqon Idrus. "Analysis of Arm Movement Prediction by Using the Electroencephalography Signal." Makara Journal of Technology 20, no. 1 (April 26, 2016): 38. http://dx.doi.org/10.7454/mst.v20i1.3282.

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Dehuri, Satchidanada, Alok Kumar Jagadev, and Sung-Bae Cho. "Epileptic Seizure Identification from Electroencephalography Signal Using DE-RBFNs Ensemble." Procedia Computer Science 23 (2013): 84–95. http://dx.doi.org/10.1016/j.procs.2013.10.012.

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Hindarto, Hindarto, and Sumarno Sumarno. "Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform." CommIT (Communication and Information Technology) Journal 10, no. 2 (October 31, 2016): 49. http://dx.doi.org/10.21512/commit.v10i2.1548.

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This article discusses a method within the area of brain-computer interface. The proposed method is to use the features extracted from the Electroencephalograph signal and a three hidden-layer artificial neural network to map the brain signal features to the computer cursor movement. The evaluated features are the root mean square and the average power spectrum. The empirical evaluation using 200 records taken from 2003 BCI Competition dataset shows that the current approach can accurately classify a simple cursor movement within 92.5% accuracy in a short computation time.

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NAMAZI, HAMIDREZA, and SAJAD JAFARI. "DECODING OF WRIST MOVEMENTS’ DIRECTION BY FRACTAL ANALYSIS OF MAGNETOENCEPHALOGRAPHY (MEG) SIGNAL." Fractals 27, no. 02 (March 2019): 1950001. http://dx.doi.org/10.1142/s0218348x19500014.

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Analysis of human movements is an important category of research in biomedical engineering, especially for the rehabilitation purpose. The human’s different movements are usually investigated by analyzing the movement signals. Based on the literatures, fewer efforts have been made in order to investigate how human movements are represented in the brain. In this paper, we decode the movements’ directions of wrist by complexity analysis of Magnetoencephalography (MEG) signal. For this purpose, we employ fractal theory. In fact, we investigate how the complexity of MEG signal changes in case of different wrist movements’ directions. The results of our analysis showed that MEG signal has different level of complexity in response to different movement’s directions. The employed methodology in this research is not limited to the analysis of MEG signal in response to wrist movement, however, it can be applied widely to analyze the influence of different factors (stimuli) on complex structure of other brain signals such as Electroencephalography (EEG) and fMRI signals.

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Salehzadeh, Amirsaleh, Andre P. Calitz, and Jean Greyling. "Human activity recognition using deep electroencephalography learning." Biomedical Signal Processing and Control 62 (September 2020): 102094. http://dx.doi.org/10.1016/j.bspc.2020.102094.

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Blanco, Justin, Ann Vanleer, Taylor Calibo, and Samara Firebaugh. "Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography." Sensors 19, no. 3 (January 25, 2019): 499. http://dx.doi.org/10.3390/s19030499.

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This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color–word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode–feature combinations worked broadly well across subjects to distinguish stress states.

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Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (December 1, 2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.

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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.

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Ksibi, Amel, Mohammed Zakariah, Leila Jamel Menzli, Oumaima Saidani, Latifah Almuqren, and Rosy Awny Mohamed Hanafieh. "Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques." Diagnostics 13, no. 10 (May 17, 2023): 1779. http://dx.doi.org/10.3390/diagnostics13101779.

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The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals’ complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient’s status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.

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Wardoyo, Retantyo, I. Made Agus Wirawan, and I. Gede Angga Pradipta. "Oversampling Approach Using Radius-SMOTE for Imbalance Electroencephalography Datasets." Emerging Science Journal 6, no. 2 (March 9, 2022): 382–98. http://dx.doi.org/10.28991/esj-2022-06-02-013.

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Several studies related to emotion recognition based on Electroencephalogram signals have been carried out in feature extraction, feature representation, and classification. However, emotion recognition is strongly influenced by the distribution or balance of Electroencephalogram data. On the other hand, the limited data obtained significantly affects the imbalance condition of the resulting Electroencephalogram signal data. It has an impact on the low accuracy of emotion recognition. Therefore, based on these problems, the contribution of this research is to propose the Radius SMOTE method to overcome the imbalance of the DEAP dataset in the emotion recognition process. In addition to the EEG data oversampling process, there are several vital processes in emotion recognition based on EEG signals, including the feature extraction process and the emotion classification process. This study uses the Differential Entropy (DE) method in the EEG feature extraction process. The classification process in this study compares two classification methods, namely the Decision Tree method and the Convolutional Neural Network method. Based on the classification process using the Decision Tree method, the application of oversampling with the Radius SMOTE method resulted in the accuracy of recognizing arousal and valence emotions of 78.78% and 75.14%, respectively. Meanwhile, the Convolutional Neural Network method can accurately identify the arousal and valence emotions of 82.10% and 78.99%, respectively. Doi: 10.28991/ESJ-2022-06-02-013 Full Text: PDF

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Zhou, Yukai, Qingshan She, Yuliang Ma, Wanzeng Kong, and Yingchun Zhang. "Transfer of semi-supervised broad learning system in electroencephalography signal classification." Neural Computing and Applications 33, no. 16 (March 17, 2021): 10597–613. http://dx.doi.org/10.1007/s00521-021-05793-2.

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Wijayanto, Inung, Rudy Hartanto, and HanungAdi Nugroho. "Quantitative analysis of inter- and intrahemispheric coherence on epileptic electroencephalography signal." Journal of Medical Signals & Sensors 12, no. 2 (2022): 145. http://dx.doi.org/10.4103/jmss.jmss_63_20.

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Najeeb, Shaima Miqdad Mohamed, Haider Th Salim Al Rikabi, and Shaima Mohammed Ali. "Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 1 (February 1, 2021): 285. http://dx.doi.org/10.12928/telkomnika.v19i1.17884.

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Journal articles on the topic 'ELECTROENCEPHALOGRAPHY SIGNAL'

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