Готові списки джерел за темами / EEG SINGLE-CHANNEL

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Добірка наукової літератури з теми "EEG SINGLE-CHANNEL"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "EEG SINGLE-CHANNEL"

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Shellhaas, Renée A., and Robert R. Clancy. "Characterization of neonatal seizures by conventional EEG and single-channel EEG." Clinical Neurophysiology 118, no. 10 (October 2007): 2156–61. http://dx.doi.org/10.1016/j.clinph.2007.06.061.

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2

Chavez, M., F. Grosselin, A. Bussalb, F. De Vico Fallani, and X. Navarro-Sune. "Surrogate-Based Artifact Removal From Single-Channel EEG." IEEE Transactions on Neural Systems and Rehabilitation Engineering 26, no. 3 (March 2018): 540–50. http://dx.doi.org/10.1109/tnsre.2018.2794184.

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3

Bachmann, Maie, Jaanus Lass, and Hiie Hinrikus. "Single channel EEG analysis for detection of depression." Biomedical Signal Processing and Control 31 (January 2017): 391–97. http://dx.doi.org/10.1016/j.bspc.2016.09.010.

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4

Grosselin, Fanny, Xavier Navarro-Sune, Alessia Vozzi, Katerina Pandremmenou, Fabrizio De Vico Fallani, Yohan Attal, and Mario Chavez. "Quality Assessment of Single-Channel EEG for Wearable Devices." Sensors 19, no. 3 (January 31, 2019): 601. http://dx.doi.org/10.3390/s19030601.

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Анотація:
The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.
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5

Wan, Zhijiang, Hao Zhang, Jiajin Huang, Haiyan Zhou, Jie Yang, and Ning Zhong. "Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder." International Journal of Information Technology & Decision Making 18, no. 05 (September 2019): 1579–603. http://dx.doi.org/10.1142/s0219622019500342.

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Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.
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6

Zhu, Guohun, Tong Qiu, Yi Ding, Shang Gao, Nan Zhao, Feng Liu, Xujuan Zhou, and Raj Gururajan. "Detecting Depression Using Single-Channel EEG and Graph Methods." Mathematics 10, no. 22 (November 8, 2022): 4177. http://dx.doi.org/10.3390/math10224177.

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Анотація:
Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H (p < 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe.
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Nakayama, Minoru, and Hiroshi Abe. "Single-trial Classification of Viewed Characters using Single-channel EEG Waveforms." International Journal for Infonomics 3, no. 4 (December 1, 2010): 392–400. http://dx.doi.org/10.20533/iji.1742.4712.2010.0042.

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Liu, Changrui, and Chaozhu Zhang. "Remove Artifacts from a Single-Channel EEG Based on VMD and SOBI." Sensors 22, no. 17 (September 4, 2022): 6698. http://dx.doi.org/10.3390/s22176698.

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Анотація:
With the development of portable EEG acquisition systems, the collected EEG has gradually changed from being multi-channel to few-channel or single-channel, thus the removal of single-channel EEG signal artifacts is extremely significant. For the artifact removal of single-channel EEG signals, the current mainstream method is generally a combination of the decomposition method and the blind source separation (BSS) method. Between them, a combination of empirical mode decomposition (EMD) and its derivative methods and ICA has been used in single-channel EEG artifact removal. However, EMD is prone to modal mixing and it has no relevant theoretical basis, thus it is not as good as variational modal decomposition (VMD) in terms of the decomposition effect. In the ICA algorithm, the implementation method based on high-order statistics is widely used, but it is not as effective as the implementation method based on second order statistics in processing EMG artifacts. Therefore, aiming at the main artifacts in single-channel EEG signals, including EOG and EMG artifacts, this paper proposed a method of artifact removal combining variational mode decomposition (VMD) and second order blind identification (SOBI). Semi-simulation experiments show that, compared with the existing EEMD-SOBI method, this method has a better removal effect on EOG and EMG artifacts, and can preserve useful information to the greatest extent.
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Qu, Wei, Chien-Hui Kao, Hong Hong, Zheru Chi, Ron Grunstein, Christopher Gordon, and Zhiyong Wang. "Single-channel EEG based insomnia detection with domain adaptation." Computers in Biology and Medicine 139 (December 2021): 104989. http://dx.doi.org/10.1016/j.compbiomed.2021.104989.

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10

Johnstone, Stuart J., Russell Blackman, and Jason M. Bruggemann. "EEG From a Single-Channel Dry-Sensor Recording Device." Clinical EEG and Neuroscience 43, no. 2 (March 27, 2012): 112–20. http://dx.doi.org/10.1177/1550059411435857.

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Дисертації з теми "EEG SINGLE-CHANNEL"

1

Marwan, Abed Thorir. "Preamplifier Design for Active Electrodes in Single-Channel EEG Applications." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232061.

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Анотація:
The implementation of portable electroencephalography (EEG) systems has been known to be complex. During ambulation, the integrity of recorded EEG signals is often impaired by motion artifacts and the time and effort required to set up the system is excessive. The use of single-channel EEG systems with dry, active electrodes (AEs) for signal acquisition is a topic of current interest. AEs are electrodes which have integrated bioamplifier circuitry and are known to be less susceptible to motion artifacts and environmental interference. In this report, the design of an AE preamplifier for the purpose of improving single-channel EEG recordings is presented. Initially, a thorough literature review was performed, exploring the available knowledge and state-of-the-art technology. Thereafter, the design specifications were set and the appropriate topology and circuit design techniques were selected to maximize the amplifier’s performance. Ultimately, three different preamplifier topologies were designed and their performance compared with one another as well as with established medical device standards and state-of-the-art AEs. The results of one preamplifier showed comparable performance with state-of-the-art AEs. Therefore, this topology was selected for a deep analysis and physical layout design. The layout of the selected preamplifier was designed and its parasitics extracted. The post layout performance of the design proved to be comparable to the schematic level performance, with a CMRR of 153dB, IRNV of 0.89µVRMS and an electrode offset tolerance of 450mV. The preamplifier design presented in this report has proven to be comparable with state-of-the-art AE preamplifiers and demonstrates potential for the advancement of AE performance in single-channel EEG systems.
Implementeringen av bärbara elektroencefalografisystem (EEG) har varit känd för att vara komplex. Vid rörelse påverkas ofta reliabiliteten av de inspelade EEGsignalerna av rörelseartefakter samt av att tiden och det arbete som krävs för att ställa in systemet blir överdrivet lång. Användandet av singelkanals EEG-system, med torra aktiva elektroder (AE) under inspelningen, är ett aktuellt ämne. En AE är en elektrod som har en integrerad bioförstärkarkrets och är känd för att vara både mindre mottaglig för rörelseartefakter och för störning från omgivningen. I denna rapport presenteras utformningen av en AE-förförstärkare för singelkanals EEGinspelningar. Inledningsvis utfördes en grundlig litteraturöversikt där den rådande kunskapen och toppmoderna tekniken undersöktes. Därefter bestämdes designspecifikationerna, lämpliga topologier samt kretsdesigntekniker, för att maximera förstärkarens prestanda. Slutligen konstruerades tre olika förstärkares topologier och deras prestanda jämfördes med varandra liksom med etablerade medicin tekniska standarder och toppmoderna AE. Resultaten av en förförstärkare visade sig ha jämförbar prestanda med toppmoderna AE. Därför valdes denna topologi ut för en djupanalys och för fysisk layoutkonstruktion. Layouten för den valda förförstärkaren utformades och dess parasiter extraherades. Utformningen av postlayouten visade sig vara jämförbar med prestanda på en schematisk nivå, med en CMRR på 153dB, IRNV på 0.89µVRMS och en elektrodoffsettolerans på 450mV. Förförstärkarens design som presenteras i denna rapport har visat sig vara jämförbar med toppmoderna AE-förförstärkare och visar potential till framsteg för AEprestanda i singelkanals EEG-system
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Coffey, Lucas B. "Assessing Ratio-Based Fatigue Indexes Using a Single Channel EEG." UNF Digital Commons, 2018. https://digitalcommons.unf.edu/etd/805.

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Driver fatigue is a state of reduced mental alertness which impairs the performance of a range of cognitive and psychomotor tasks, including driving. According to the National Highway Traffic Safety Administration, driver fatigue was responsible for 72,000 accidents that lead to more than 800 deaths in 2015. A reliable method of driver fatigue detection is needed to prevent such accidents. There has been a great deal of research into studying driver fatigue via electroencephalography (EEG) to analyze brain wave data. These research works have produced three competing EEG data-based ratios that have the potential to detect driver fatigue. Research has shown these three ratios trend downward as fatigue increases. However, no empirical research has been conducted to determine whether drivers begin to feel fatigue at a certain Percent Change from an alert state to a fatigue state in one or more of these ratios. If a Percent Change could be identified for which drivers begin to feel fatigue, then it could be used as a method of fatigue detection in real-time system. This research focuses on answering this question by collecting brain wave data via an EEG device over a 60-minute driving session for 10 University of North Florida (UNF) students. A frequency distribution and cluster analysis was done to identify a common Percent Change for the participants who experienced fatigue. The results of the analysis were compared to a subset of users who did not experience fatigue to validate the findings. The project was approved by the UNF IRB on Nov. 1, 2016 (reference number 475514-4).
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Dietch, Jessica R. "Accuracy of Three Assessments of Sleep Timing, Duration and Efficiency Compared to a Single-Channel EEG Device." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538787/.

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Poor sleep measured across many dimensions has been linked to adverse physical and mental health outcomes including cardiovascular disease, diabetes, cancer, increased mortality, depression, and anxiety. Current research typically relies upon brief, subjective, inadequately validated methods to assess limited dimensions of sleep, resulting in inaccurate measurements and possibly faulty conclusions. Specifically, research validating objective (e.g., actigraphy) and subjective (e.g., sleep diaries, retrospective surveys) measurement methods against the gold standard of polysomnography (PSG, an overnight sleep study) is primarily limited by a) a lack of reliability based on too short (e.g., 24 or 48 hours) of an assessment period to capture night-to-night variability, b) a lack of ecological validity (e.g., full PSG in a laboratory setting), and c) a lack of generalizability due to limited or special populations (e.g., individuals with insomnia). Barriers such as prohibitive cost, extensive setup time, and personnel training requirements diminish the ability of researchers to conduct measurement comparison studies using gold standard measures like traditional PSG. These barriers can be circumvented with the use of low-cost, minimally invasive single-channel EEG devices (e.g., Zmachine), but to date few studies have employed these devices. The current study evaluated the accuracy of retrospective surveys, sleep diaries, and actigraphy compared to a single-channel EEG device for assessment of sleep timing, duration, and efficiency in participants' homes over one week using a broad community sample (N = 80). Actigraphy generally demonstrated the best agreement with Zmachine across sleep variables, followed by diary and then survey. Circadian midpoint was the most consistent across measures, followed by sleep duration and then sleep efficiency. Implications and future directions are discussed.
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Cheng, Pei-Ling, and 鄭珮綾. "Homecare sleep evaluation system based on single-channel EEG and single-channel EOG." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/r9w7d2.

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Анотація:
碩士
國立陽明大學
醫學工程研究所
97
The quality of sleep is effective to our daily life, and more and more people have been experiencing sleep disorders in recent years. However the equipment and resources for sleep diagnosis are still limited. Although many researchers have developed automatic sleep scoring system, most of the systems are built based on PSG recording. A new homecare sleep recording device, “VitalBelt”, which records only three channel bio-signals (EEG, EOG, ECG), is applied in our scoring system. Sleep features are extracted individually, then a scoring method including exact scoring, refinement and impossible rejection three aspect to score sleep stages. Thirty subjects were recorded using both polysnomnography and VitalBelt. Comparing the manual score by polysnomography and automatic score by VitalBelt, overall agreement was 73.48% (Cohen’s kappa = 0.62). The result showed that the automatic sleep staging system developed for VitalBelt was reliable and might apply in homecare sleep monitoring. In addition, the methods of single-channel EEG with or without EOG were compared to evaluate the requirements of EOG for sleep staging. Paired t-test was used to estimate the agreement increase, with the result showing that the improvement was significant (P < 0.005) after combining EOG.
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Liu, Xuan-Ming, and 劉軒銘. "Emotional Stress Determination Using Single-channel EEG Device." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7vcmz8.

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Анотація:
碩士
義守大學
電子工程學系
106
An emotional stress determination system is helpful for the modern people to reduce stress and avoid conflict, anxiety and misunderstanding. In this study, we used different multimedia to induce emotions and obtain EEG data. The raw EEG data was transformed into different EEG types by Fast Fourier Transform (FFT). However, the inducing emotion effect from subjects might be different because of the personal preferences, values and life experiences. Therefore Self-Assessment Manikin (SAM) is used as criterions for examining subjects’ emotion after induced emotion experiment. Experimental results shows that Support Vector Machine (SVM) achieves emotional determination with an average accuracy of 86.4%.The finding of this work is the high-gamma(γ) frequency feature from EEG power has the higher average accuracy rate of both positive emotion and negative emotion.
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Guo-Fu, Hu, and 胡國甫. "A Single channel EEG system design and EEG signals analysis for sleep and awake." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/19541673125630314843.

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Анотація:
碩士
元智大學
機械工程學系
91
The control system of human central nervous system (CNS) is complex. Electroencephalogram (EEG) is one tool to explore the physiological function of brain. It is widely used to diagnose clinical CNS syndromes, such as epilepsy, brain tumor, Parkinson’s disease, etc. However, mostly commercial EEG instruments belong to multi-channel, high price and too big for portable usage. Hence, our thesis tries to design a low price, small volume and single channel of EEG device for easy portable usage. It can use for medical measurements .We used the fourier transform analysis for five volunteers at each time domain in the awake and sleep EEG signals. We found significant differences in sleep and awake waves form according to the volunteers’ sleep and awake statuses. The successful results are given us to have the confidence to test this EEG device in the clinical trials in the near future.
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Dai, Zi-fei, and 戴子斐. "A Sleep Staging Method Based on Single Channel EEG Signal." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/9fvzy4.

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Анотація:
碩士
國立中山大學
機械與機電工程學系研究所
97
One of the important measures for sleep quailty is sleep structure. Normal sleep consists of awake, rapid eye movement (REM) sleep and nonrapid eye movement (NREM) sleep states. NREM sleep can be further classified into stage 1, stage 2 and slow wave sleep (SWS). These stages can be analyzed quantitatively from various electrical signals such as the electroencephalogram (EEG), electro-oculogram (EOG), and electromyogram (EMG). The goal of this research is to develop a simple four-stage process to classify sleep into wake, REM, stage 1, stage 2 and SWS by using a single EEG channel. By applying the proposed approach to 48727 distinct epochs which are acquired from 62 persons, the experimental results show that the proposed method is achieves 76.98% of accuracy. The sensitivity and PPV for wake are 85.96% and 68.35%. Furthermore, the sensitivity and PPV for REM are 82.13% and 74.11%, respectively. The sensitivity and PPV for the stage 1 are 9.02% and 39.00%. The sensitivity and PPV for the stage 2 are 84.19% and 79.36%. The sensitivity and PPV for SWS are 81.53% and 85.40%.
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8

Shu, Chen, and 舒晨. "Sleep Quality Assessment System Based on a Single-Channel EEG Device." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/kmchb3.

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Анотація:
碩士
國立陽明大學
醫學工程研究所
97
Sleep quality refers both the subjective assessment given by the subject of how restorative and undisturbed his/her sleep has been and to a series of objective measures which may be derived from polygraphic recordings. There are many factors could affect the sleep quality like personal behaviors, personal sensation, environment factors, and physiological changes. However, the most commonly used objective measure of sleep quality is an index of sleep fragmentation which may be derived from all three types of recordings. To be compared, object measures of sleep quality cost high and had not necessarily concordant results with subject ones. The aim of this study was to set up a new household sleep quality assessing system. This system was bases on one channel EEG signal to estimate the subjective measure of sleep quality. According to the signal recording, we could define the features of slow wave, alpha wave and spindles in time scale by using zero crossing method, Schimicek algorithm and RMS-amplitude for sleep staging. We used the one channel EEG data (Fpz-Cz) which was downloaded from Europe Data Format (EDF) sleep database to test the algorithm in our system. However, the results of our system had 81.67% accuracy from database hypnogram. Furthermore, we assessed more clinical sleep parameters, including total sleep time, sleep latency and percentage of light sleep, REM sleep and deep sleep to have an evaluation of household system by using sleeping quality score. . The sleeping quality score in normal human is 2.63±1.30 (n=8) evaluating form EDF database and the score in the patients with sleep syndrome is -0.15±2.54 (n=13) evaluating form MIT-BIH database. It was indeed different between these two groups. Therefore, 24 sets of EEG signal from 21 participators (29.13±9.47 years old, 15 males/ 6 females) were used in our experiments and separated into 2 groups by objective sleep stage. One was participator with good-sleep quality and another was participator with bad-sleep quality group. The average score of good-sleep group was 2.61±1.29 and these of bad-sleep group was -0.5±1.22. Therefore, our system could provide an effective index to assess the evaluation of sleep quality. According this study, we set up a household sleep quality system and help people to determine their sleep problem primarily and hope could raise living quality of human.
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9

Chiu, Hao-chih, and 邱晧智. "Detecting Slow Wave Sleep by Using a single Channel EEG Signal." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/wrj7j8.

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Анотація:
碩士
國立中山大學
機械與機電工程學系研究所
96
One of the important topics in sleep medicine is sleep structure. Normal sleep consists of rapid eye movement (REM) sleep and nonrapid eye movement (NRME) sleep states. NREM sleep can be further classified into stage 1, 2 and slow wave sleep (SWS) according to the current sleep scoring standard. Among them, SWS has been considered to be very important due to its r restorative value. The goal of this research is to detect SWS by using a single channel EEG signal. Its applications can be divided into two phases. In the first phase, a personalized SWS detector is designed for each individuals By combining these personalized SWS detectors, the second phase develops a general SWS detection method that can be applied to general population with any personalized training process. By applying the proposed method to 62 persons, the experimental results show that the proposed method, in average, achieves 90.69% classification accuracy 90.09% sensitivity and 93.97% specificity. Our experimental results also demonstrate, when applied to persons with higher AHI (apnoea-hypopnea index) values, the proposed method can still provided satisfactory results.
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Chang, Tien-Fu, and 張天福. "The study of neuro- rehabilitation using wireless single channel EEG device." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/30700074520279093871.

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Анотація:
碩士
國立交通大學
電控工程研究所
100
This research conceives a new way of self-rehabilitation which is more helpful for patients, combined with brain waves to approach a new neuro-rehabilitation system. When the user is imaging hand action, the brain will produce a special kind of brain wave, and the system is recognizing the brain wave to control machine to take user’s hand moving, with the principle of neuro-rehabilitation and the enhancement of the bio-feedback method, then this system can make a better rehabilitating effect. The study analyzes the subjects’ event related motor imagery (MI) by using independent component analysis (ICA) and many other brain wave analyzing methods, such as event related spectral perturbation (ERSP), to find the best frequencies and time to recognize, using the sensor to receive brain waves and DSP chip with some algorithms to control the motor to take targets’ hand moving. Using this method of brain wave analyzing and system architecture, the system can recognize motor imagery brain wave successfully and control the rehabilitation machine quickly, to imitate the self-behaviors of paralyzed or stroke patients. This system is hoped to shorten the rehabilitating time greatly and promote the effect, and even saving many human resources.
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Частини книг з теми "EEG SINGLE-CHANNEL"

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Palaniappan, Ramaswamy, Jenish Gosalia, Kenneth Revett, and Andrews Samraj. "PIN Generation Using Single Channel EEG Biometric." In Advances in Computing and Communications, 378–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22726-4_40.

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2

Garcia-Molina, Gary, Michele Bellesi, Sander Pastoor, Stefan Pfundtner, Brady Riedner, and Giulio Tononi. "Online Single EEG Channel Based Automatic Sleep Staging." In Lecture Notes in Computer Science, 333–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39354-9_36.

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Zhang, Tinglin, Guang Li, and Hans Liljenström. "Study on Single-Channel EEG Pattern Induced by Acupuncture." In Advances in Cognitive Neurodynamics (V), 485–91. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0207-6_66.

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Wang, Jialin, Yanchun Zhang, and Qinying Ma. "Analysis of Narcolepsy Based on Single-Channel EEG Signals." In Big Data Analytics, 295–306. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04780-1_20.

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Secerbegovic, A., S. Ibric, J. Nisic, N. Suljanovic, and A. Mujcic. "Mental workload vs. stress differentiation using single-channel EEG." In IFMBE Proceedings, 511–15. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4166-2_78.

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6

Patel, Kishan, Harit Shah, Malcolm Dcosta, and Dvijesh Shastri. "Evaluating NeuroSky’s Single-Channel EEG Sensor for Drowsiness Detection." In Communications in Computer and Information Science, 243–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58750-9_35.

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7

Zhou, Tie Hua, Wen Long Liang, Hang Yu Liu, Wei Jian Pu, and Ling Wang. "Wavelet-Based Emotion Recognition Using Single Channel EEG Device." In Intelligent Computing Methodologies, 510–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60796-8_44.

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8

Gwizdka, Jacek. "Inferring Web Page Relevance Using Pupillometry and Single Channel EEG." In Information Systems and Neuroscience, 175–83. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67431-5_20.

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9

Radhakrishnan, B. L., E. Kirubakaran, Immanuel Johnraja Jebadurai, and Kummari Gurudev. "Classifying Sleep Stages Automatically in Single-channel Against Multi-channel EEG: A Performance Analysis." In Lecture Notes in Electrical Engineering, 527–37. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2177-3_50.

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10

Dora, Chinmayee, and Pradyut Kumar Biswal. "An ELM Based Regression Model for ECG Artifact Minimization from Single Channel EEG." In Intelligent Data Engineering and Automated Learning – IDEAL 2018, 269–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03493-1_29.

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Тези доповідей конференцій з теми "EEG SINGLE-CHANNEL"

1

Raju, N. Purushotham, U. Venkatesh, and Sudha Yadhav. "Diagnosing Insomnia Using Single Channel EEG Signal." In 2019 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2019. http://dx.doi.org/10.1109/icces45898.2019.9002583.

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2

Hendrawan, Muhammad Afif, Ulla Delfana Rosiani, and Arwin Datumaya Wahyudi Sumari. "Single Channel Electroencephalogram (EEG) Based Biometric System." In 2022 IEEE 8th Information Technology International Seminar (ITIS). IEEE, 2022. http://dx.doi.org/10.1109/itis57155.2022.10010103.

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3

Zhang, Chao, Siqi Han, and Milin Zhang. "Single-channel EEG completion using Cascade Transformer." In 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2022. http://dx.doi.org/10.1109/biocas54905.2022.9948557.

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4

Oral, E. Argun, I. Yucel Ozbek, and M. Mustafa Codur. "Gender clasification based on single channel EEG signal." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090273.

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5

Liang, Zhen, Hongtao Liu, and Joseph N. Mak. "Detection of media enjoyment using single-channel EEG." In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2016. http://dx.doi.org/10.1109/biocas.2016.7833845.

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6

Song, Xiaomu, Suk-Chung Yoon, Eric Rex, Jason Nieves, and Caleb Moretz. "Driver drowsiness detection using single-channel dry EEG." In 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2017. http://dx.doi.org/10.1109/spmb.2017.8257041.

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7

Szibbo, D., An Luo, and T. J. Sullivan. "Removal of blink artifacts in single channel EEG." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346723.

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8

Jalilifard, Amir, Ednaldo Brigante Pizzolato, and Md Kafiul Islam. "Emotion classification using single-channel scalp-EEG recording." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7590833.

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9

Kouchaki, Samaneh, and Saeid Sanei. "Supervised single channel source separation of EEG signals." In 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2013. http://dx.doi.org/10.1109/mlsp.2013.6661895.

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10

Katsigiannis, Stamos, Pablo Arnau-Gonzalez, Miguel Arevalillo-Herraez, and Naeem Ramzan. "Single-channel EEG-based subject identification using visual stimuli." In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2021. http://dx.doi.org/10.1109/bhi50953.2021.9508581.

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