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Статті в журналах з теми "EEG SINGLE-CHANNEL"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

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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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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7

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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8

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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9

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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11

Zammouri, Amin, and Abdelaziz Ait Moussa. "Eye blinks artefacts detection in a single EEG channel." International Journal of Embedded Systems 9, no. 4 (2017): 321. http://dx.doi.org/10.1504/ijes.2017.086126.

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12

Ait Moussa, Abdelaziz, and Amin Zammouri. "Eye blinks artefacts detection in a single EEG channel." International Journal of Embedded Systems 9, no. 4 (2017): 321. http://dx.doi.org/10.1504/ijes.2017.10007106.

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13

Mohanchandra, Kusuma, and Snehanshu Saha. "Optimal Channel Selection for Robust EEG Single-trial Analysis." AASRI Procedia 9 (2014): 64–71. http://dx.doi.org/10.1016/j.aasri.2014.09.012.

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14

Hendrawan, Muhammad Afif, Pramana Yoga Saputra, and Cahya Rahmad. "Identification of optimum segment in single channel EEG biometric system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1847. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1847-1854.

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Анотація:

Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.

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15

Dora, Chinmayee, and Pradyut Kumar Biswal. "Efficient detection and correction of variable strength ECG artifact from single channel EEG." Biomedical Signal Processing and Control 50 (April 2019): 168–77. http://dx.doi.org/10.1016/j.bspc.2019.01.023.

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16

Maddirala, Ajay Kumar, and Kalyana C. Veluvolu. "SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals." Sensors 22, no. 3 (January 25, 2022): 931. http://dx.doi.org/10.3390/s22030931.

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Анотація:

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

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17

Zhao, Shanguang, Fangfang Long, Xin Wei, Xiaoli Ni, Hui Wang, and Bokun Wei. "Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm." International Journal of Environmental Research and Public Health 19, no. 5 (March 1, 2022): 2845. http://dx.doi.org/10.3390/ijerph19052845.

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Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring.

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18

Venkatesan, K. G. S., Kishore Kumar Mamidala, and Swaroopa Rani B. "Detecting Optimal Regions for a Single EEG Channel Biometric System." International Journal of Scientific Methods in Engineering and Management 01, no. 07 (2022): 09–20. http://dx.doi.org/10.58599/ijsmem.2023.1702.

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Innovative security systems are increasingly making use of biometric modalities as an authentication method. However, the biometric technology that is presently available on the market provides solutions to a significant number of these difficulties. The widespread use of bogus biometrics in today’s society is one of the most significant reasons for concern. The results of an electroencephalogram (EEG) can provide some interesting information on the matter. This is a highly challenging endeavour since reproduction calls for careful preparation on your part. Several different investigations have shown that the procedure may be trusted to provide accurate results. Nonetheless, the collecting of data necessitates a large expenditure of time in addition to the sensors. In this study, we provide a biometric technique that takes use of EO resting-state EEG recordings that were taken from a single-channel electrode placement on the scalp. These recordings were generated in order to determine the precision of the method. The electroencephalograms (EEGs) of all nine persons who were examined yielded a total of 45 different signals. The interval of time that passed between each EEG wave segment was under five seconds. This specific piece of study focused its attention on the

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19

Noorbasha, Sayedu Khasim, and Gnanou Florence Sudha. "Hybrid algorithm for multi artifact removal from single channel EEG." Biomedical Physics & Engineering Express 7, no. 4 (May 11, 2021): 045003. http://dx.doi.org/10.1088/2057-1976/abfd81.

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20

Molina-Cantero, Alberto J., Juan A. Castro-García, Fernando Gómez-Bravo, Rafael López-Ahumada, Raúl Jiménez-Naharro, and Santiago Berrazueta-Alvarado. "Controlling a Mouse Pointer with a Single-Channel EEG Sensor." Sensors 21, no. 16 (August 14, 2021): 5481. http://dx.doi.org/10.3390/s21165481.

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(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

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21

Lucey, Brendan P., Jennifer S. Mcleland, Cristina D. Toedebusch, Jill Boyd, John C. Morris, Eric C. Landsness, Kelvin Yamada, and David M. Holtzman. "Comparison of a single-channel EEG sleep study to polysomnography." Journal of Sleep Research 25, no. 6 (June 2, 2016): 625–35. http://dx.doi.org/10.1111/jsr.12417.

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22

Su, Bo-Lin, Yuxi Luo, Chih-Yuan Hong, Mark L. Nagurka, and Chen-Wen Yen. "Detecting slow wave sleep using a single EEG signal channel." Journal of Neuroscience Methods 243 (March 2015): 47–52. http://dx.doi.org/10.1016/j.jneumeth.2015.01.023.

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23

Rogers, Jeffrey M., Stuart J. Johnstone, Anna Aminov, James Donnelly, and Peter H. Wilson. "Test-retest reliability of a single-channel, wireless EEG system." International Journal of Psychophysiology 106 (August 2016): 87–96. http://dx.doi.org/10.1016/j.ijpsycho.2016.06.006.

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24

Roy, Vandana, Anand Prakash, and Shailja Shukla. "WAVELET FEATURES BASED SLEEP STAGES DETECTION USING SINGLE CHANNEL EEG." International Journal of Students' Research in Technology & Management 5, no. 4 (December 1, 2017): 99–102. http://dx.doi.org/10.18510/ijsrtm.2017.5414.

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The sleep stages determination is important for the identification and diagnosis of different diseases. An efficient algorithm of wavelet decomposition is used for feature extraction of single channel EEG. The Chi-Square method is applied for the selection of the best attributes from the extracted features. The classification of different staged techniques is applied with the help AdaBoost.M1 algorithm. The accuracy of 89.82% achieved in the six stage classification. The weighted sensitivity of all stages is 89.8% and kappa coefficient of 77.93% is obtained in the six stage classification.

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25

Aminov, Anna, Jeffrey M. Rogers, Stuart J. Johnstone, Sandy Middleton, and Peter H. Wilson. "Acute single channel EEG predictors of cognitive function after stroke." PLOS ONE 12, no. 10 (October 2, 2017): e0185841. http://dx.doi.org/10.1371/journal.pone.0185841.

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26

Liu, Cheng, Tongfeng Weng, and Xinhua Liu. "ResSleepNet: Automatic sleep stage classification on raw single-channel EEG." IOP Conference Series: Materials Science and Engineering 466 (December 28, 2018): 012101. http://dx.doi.org/10.1088/1757-899x/466/1/012101.

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27

Suppiah, Ravi, and Achutavarrier Prasad Vinod. "Biometric identification using single channel EEG during relaxed resting state." IET Biometrics 7, no. 4 (January 12, 2018): 342–48. http://dx.doi.org/10.1049/iet-bmt.2017.0142.

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28

Ugur, Tugce Kantar, and Aykut Erdamar. "An efficient automatic arousals detection algorithm in single channel EEG." Computer Methods and Programs in Biomedicine 173 (May 2019): 131–38. http://dx.doi.org/10.1016/j.cmpb.2019.03.013.

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29

Janjarasjitt, Suparerk. "Performance of epileptic single-channel scalp EEG classifications using single wavelet-based features." Australasian Physical & Engineering Sciences in Medicine 40, no. 1 (March 2017): 57–67. http://dx.doi.org/10.1007/s13246-016-0520-4.

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30

Gursel Ozmen, Nurhan, Levent Gumusel, and Yuan Yang. "A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification." Computational and Mathematical Methods in Medicine 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/9890132.

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Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs) of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i) resting state, (ii) mental arithmetic, (iii) imagination of left hand movement, (iv) imagination of right hand movement, and (v) imagination of letter “A.” Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification) that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%), indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.

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31

Dora, Chinmayee, and Pradyut Kumar Biswal. "Correlation-based ECG Artifact Correction from Single Channel EEG using Modified Variational Mode Decomposition." Computer Methods and Programs in Biomedicine 183 (January 2020): 105092. http://dx.doi.org/10.1016/j.cmpb.2019.105092.

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32

Balam, Venkata Phanikrishna, and Suchismitha Chinara. "Statistical Channel Selection Method for Detecting Drowsiness Through Single-Channel EEG-Based BCI System." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–9. http://dx.doi.org/10.1109/tim.2021.3094619.

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33

Chen, Xun, Chen He, and Hu Peng. "Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis." Journal of Applied Mathematics 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/261347.

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Анотація:

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications.

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34

Toma, Tabassum Islam, and Sunwoong Choi. "An End-to-End Multi-Channel Convolutional Bi-LSTM Network for Automatic Sleep Stage Detection." Sensors 23, no. 10 (May 21, 2023): 4950. http://dx.doi.org/10.3390/s23104950.

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Sleep stage detection from polysomnography (PSG) recordings is a widely used method of monitoring sleep quality. Despite significant progress in the development of machine-learning (ML)-based and deep-learning (DL)-based automatic sleep stage detection schemes focusing on single-channel PSG data, such as single-channel electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), developing a standard model is still an active subject of research. Often, the use of a single source of information suffers from data inefficiency and data-skewed problems. Instead, a multi-channel input-based classifier can mitigate the aforementioned challenges and achieve better performance. However, it requires extensive computational resources to train the model, and, hence, a tradeoff between performance and computational resources cannot be ignored. In this article, we aim to introduce a multi-channel, more specifically a four-channel, convolutional bidirectional long short-term memory (Bi-LSTM) network that can effectively exploit spatiotemporal features of data collected from multiple channels of the PSG recording (e.g., EEG Fpz-Cz, EEG Pz-Oz, EOG, and EMG) for automatic sleep stage detection. First, a dual-channel convolutional Bi-LSTM network module has been designed and pre-trained utilizing data from every two distinct channels of the PSG recording. Subsequently, we have leveraged the concept of transfer learning circuitously and have fused two dual-channel convolutional Bi-LSTM network modules to detect sleep stages. In the dual-channel convolutional Bi-LSTM module, a two-layer convolutional neural network has been utilized to extract spatial features from two channels of the PSG recordings. These extracted spatial features are subsequently coupled and given as input at every level of the Bi-LSTM network to extract and learn rich temporal correlated features. Both Sleep EDF-20 and Sleep EDF-78 (expanded version of Sleep EDF-20) datasets are used in this study to evaluate the result. The model that includes an EEG Fpz-Cz + EOG module and an EEG Fpz-Cz + EMG module can classify sleep stage with the highest value of accuracy (ACC), Kappa (Kp), and F1 score (e.g., 91.44%, 0.89, and 88.69%, respectively) on the Sleep EDF-20 dataset. On the other hand, the model consisting of an EEG Fpz-Cz + EMG module and an EEG Pz-Oz + EOG module shows the best performance (e.g., the value of ACC, Kp, and F1 score are 90.21%, 0.86, and 87.02%, respectively) compared to other combinations for the Sleep EDF-78 dataset. In addition, a comparative study with respect to other existing literature has been provided and discussed in order to exhibit the efficacy of our proposed model.

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Miyata, S., K. Iwamoto, M. Banno, Y. Ito, A. Noda, and N. Ozaki. "0575 Sleep Monitoring with a Single Channel EEG Recorder in Patients with Psychiatric Disorders." Sleep 43, Supplement_1 (April 2020): A220—A221. http://dx.doi.org/10.1093/sleep/zsaa056.572.

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Abstract Introduction The gold standard of sleep measurement has been laboratory polysomnography (PSG). However, electrodes and cables can cause discomfort, and exposure to an unfamiliar environment can cause the “first-night effect.” Difficulty falling asleep or maintaining sleep, poor sleep quality, and nightmares are some of the key clinical symptoms observed among individuals with psychiatric disorders. Those suffering from sleep disorders often present with symptoms of discontent with regard to sleep quality, timing, and quantity, and these symptoms have an adverse impact on function and quality of life. A minimally invasive technique would be preferable in patients with psychiatric disorders, who tend to be sensitive to environmental change. Accordingly, we evaluated the performance of a single-channel electroencephalography (EEG)-based sleep monitoring system in patients with psychiatric disorders. Methods Fifty-nine patients undergoing PSG were enrolled in this study. Single-channel EEG sleep monitoring was performed simultaneously with PSG. PSG and the EEG recordings were used to evaluate sleep parameters, such as total sleep time (TST), sleep efficiency, rapid eye movement (REM) sleep, light sleep (stages N1 and N2), and deep sleep (stage N3). Correlation analysis was used to evaluate the agreement on sleep parameters and attributing factors to the inaccuracies of the single-channel EEG recording. Results TST, sleep efficiency, REM sleep duration, and non-REM sleep duration of the single-channel EEG-based sleep monitoring showed a significant correlation with those of PSG. Lower sleep efficiency, a decrease in REM sleep, and increases in waking after sleep onset, arousal index, and apnea/hypopnea index were associated with the difference of sleep parameters between the two methods. Conclusion Among patients with psychiatric disorders who are sensitive to environmental change single-channel EEG sleep monitoring would be a useful technique to objectively evaluate sleep quality. Support Collaboration study with The KAITEKI Institute, Inc.

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Nazih, Waleed, Mostafa Shahin, Mohamed I. Eldesouki, and Beena Ahmed. "Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms." Sensors 23, no. 2 (January 12, 2023): 899. http://dx.doi.org/10.3390/s23020899.

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The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants’ age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1–52 weeks).

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Hagras, Shaimaa, Reham R. Mostafa, and Ahmed Abou elfetouh. "A Biometric System Based on Single-channel EEG Recording in One-second." International Journal of Intelligent Systems and Applications 12, no. 5 (October 8, 2020): 28–40. http://dx.doi.org/10.5815/ijisa.2020.05.03.

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In recent years, there are great research interests in using the Electroencephalogram (EEG) signals in biometrics applications. The strength of EEG signals as a biometric comes from its major fraud prevention capability. However, EEG signals are so sensitive, and many factors affect its usage as a biometric; two of these factors are the number of channels, and the required time for acquiring the signal; these factors affect the convenience and practicality. This study proposes a novel approach for EEG-based biometrics that optimizes the channels of acquiring data to only one channel. And the time to only one second. The results are compared against five commonly used classifiers named: KNN, Random Forest (RF), Support Vector Machine (SVM), Decision Tables (DT), and Naïve Bayes (NB). We test the approach on the public Texas data repository. The results prove the constancy of the approach for the eight minutes. The best result of the eyes-closed scenario is Average True Positive Rate (TPR) 99.1% and 98.2% for the eyes-opened. And it reaches 100% for multiple subjects.

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Raju, Padma, Padmasai Yarlagadda, and Vijaya Kumar Gurrala. "A Novel Single Channel EEG based Sleep Stage Classification using SVM." International Journal of Biomedical Engineering and Technology 36, no. 2 (2021): 1. http://dx.doi.org/10.1504/ijbet.2021.10037036.

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Frankel, Mitchell A., Mark J. Lehmkuhle, Meagan Watson, Kirsten Fetrow, Lauren Frey, Cornelia Drees, and Mark C. Spitz. "Electrographic seizure monitoring with a novel, wireless, single-channel EEG sensor." Clinical Neurophysiology Practice 6 (2021): 172–78. http://dx.doi.org/10.1016/j.cnp.2021.04.003.

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Dora, Matteo, and David Holcman. "Adaptive Single-Channel EEG Artifact Removal With Applications to Clinical Monitoring." IEEE Transactions on Neural Systems and Rehabilitation Engineering 30 (2022): 286–95. http://dx.doi.org/10.1109/tnsre.2022.3147072.

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Gurrala, Vijayakumar, Padmasai Yarlagadda, and Padmaraju Koppireddi. "A novel single channel EEG-based sleep stage classification using SVM." International Journal of Biomedical Engineering and Technology 36, no. 2 (2021): 119. http://dx.doi.org/10.1504/ijbet.2021.116112.

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MUHAMMAD UMAR SAEED, Sanay, Syed MUHAMMAD ANWAR, and Muhammad MAJID. "Quantification of Human Stress Using Commercially Available Single Channel EEG Headset." IEICE Transactions on Information and Systems E100.D, no. 9 (2017): 2241–44. http://dx.doi.org/10.1587/transinf.2016edl8248.

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Taherisadr, Mojtaba, Omid Dehzangi, and Hossein Parsaei. "Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis." Sensors 17, no. 12 (December 13, 2017): 2895. http://dx.doi.org/10.3390/s17122895.

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Swarnkar, Vinayak, and Udantha R. Abeyratne. "Bispectral analysis of single channel EEG to estimate macro-sleep-architecture." International Journal of Medical Engineering and Informatics 6, no. 1 (2014): 43. http://dx.doi.org/10.1504/ijmei.2014.058531.

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Salyers, Jacob B., Yue Dong, and Yan Gai. "Continuous Wavelet Transform for Decoding Finger Movements From Single-Channel EEG." IEEE Transactions on Biomedical Engineering 66, no. 6 (June 2019): 1588–97. http://dx.doi.org/10.1109/tbme.2018.2876068.

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Wikström, Sverre, Ingrid Hansen Pupp, Ingmar Rosén, Elisabeth Norman, Vineta Fellman, David Ley, and Lena Hellström‐Westas. "Early single‐channel aEEG/EEG predicts outcome in very preterm infants." Acta Paediatrica 101, no. 7 (April 24, 2012): 719–26. http://dx.doi.org/10.1111/j.1651-2227.2012.02677.x.

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Lin, Nan-Hung, Chung-Yao Hsu, Yuxi Luo, Mark L. Nagurka, Jia-Li Sung, Chih-Yuan Hong, and Chen-Wen Yen. "Detecting rapid eye movement sleep using a single EEG signal channel." Expert Systems with Applications 87 (November 2017): 220–27. http://dx.doi.org/10.1016/j.eswa.2017.06.017.

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Liu, Y., P. Lee, B. Ku, Y. Lin, and T. Chen. "0308 High-performance Single-channel EEG Sleep Staging Using Artificial Intelligence." Sleep 41, suppl_1 (April 2018): A118—A119. http://dx.doi.org/10.1093/sleep/zsy061.307.

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陈, 振东. "Intelligent Terminal Based on Single Channel EEG Signal LSTM Sleep Staging." Computer Science and Application 09, no. 06 (2019): 1156–68. http://dx.doi.org/10.12677/csa.2019.96131.

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Berthomier, Christian, Xavier Drouot, Maria Herman-Stoïca, Pierre Berthomier, Jacques Prado, Djibril Bokar-Thire, Odile Benoit, Jérémie Mattout, and Marie-Pia d'Ortho. "Automatic Analysis of Single-Channel Sleep EEG: Validation in Healthy Individuals." Sleep 30, no. 11 (November 2007): 1587–95. http://dx.doi.org/10.1093/sleep/30.11.1587.

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