Relevant bibliographies by topics / ELECTROENCEPHALOGRAPHY SIGNAL

Academic literature on the topic 'ELECTROENCEPHALOGRAPHY SIGNAL'

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Published: 11 September 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "ELECTROENCEPHALOGRAPHY SIGNAL"

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Sellergren, Albin, Tobias Andersson, and Jonathan Toft. "Signal processing through electroencephalography : Independent project in electrical engineering." Thesis, Uppsala universitet, Elektricitetslära, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-298771.

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This report is about a project where electroencephalography (EEG) wasused to control a two player game. The signals from the EEG-electrodeswere amplified, filtered and processed. Then the signals from the playerswere compared and an algorithm decided what would happen in the gamedepending on which signal was largest. The controls and the gaming mechanismworked as intended, however it was not possible to gather a signal fromthe brain with the method used in this project. So ultimately the goal wasnot reached.
electroencephalography, EEG
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Birch, Gary Edward. "Single trial EEG signal analysis using outlier information." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28626.

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The goal of this thesis work was to study the characteristics of the EEG signal and then, based on the insights gained from these studies, pursue an initial investigation into a processing method that would extract useful event related information from single trial EEG. The fundamental tool used to study the EEG signal characteristics was autoregressive modeling. Early investigations pointed to the need to employ robust techniques in both model parameter estimation and signal estimation applications. Pursuing robust techniques ultimately led to the development of a single trial processing method which was based on a simple neurological model that assumed an additive outlier nature of event related potentials to the ongoing EEG process. When event related potentials, such as motor related potentials, are generated by a unique additional process they are "added" into the ongoing process and hence, will appear as additive outlier content when considered from the point of view of the ongoing process. By modeling the EEG with AR models with robustly estimated (GM-estimates) parameters and by using those models in a robust signal estimator, a "cleaned" EEG signal is obtained. The outlier content, data that is extracted from the EEG during cleaning, is then processed to yield event related information. The EEG from four subjects formed the basis of the initial investigation into the viability of this single trial processing scheme. The EEG was collected under two conditions: an active task in which subjects performed a skilled thumb movement and an idle task in which subjects remained alert but did not carry out any motor activity. The outlier content was processed which provided single trial outlier waveforms. In the active case these waveforms possessed consistent features which were found to be related to events in the individual thumb movements. In the idle case the waveforms did not contain consistent features. Bayesian classification of active trials versus idle trials was carried out using a cost statistic resulting from the application of dynamic time warping to the outlier waveforms. Across the four subjects, when the decision boundary was set with the cost of misclassification equal, 93% of the active trials were classified correctly and 18% of the idle trials were incorrectly classified as active. When the cost of misclassifying an idle trial was set to be five times greater, 80% of the active trials were classified correctly and only 1.7% of the idle trials were incorrectly classified as active.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Li, Kun. "Advanced Signal Processing Techniques for Single Trial Electroencephalography Signal Classification for Brain Computer Interface Applications." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3484.

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Brain Computer Interface (BCI) is a direct communication channel between brain and computer. It allows the users to control the environment without the need to control muscle activity [1-2]. P300-Speller is a well known and widely used BCI system that was developed by Farwell and Donchin in 1988 [3]. The accuracy level of the P300-BCI Speller as measured by the percent of communicated characters correctly identified by the system depends on the ability to detect the P300 event related potential (ERP) component among the ongoing electroencephalography (EEG) signal. Different techniques have been tested to reduce the number of trials needed to be averaged together to allow the reliable detection of the P300 response. Some of them have achieved high accuracies in multiple-trial P300 response detection. However the accuracy of single trial P300 response detection still needs to be improved. In this research, two single trial P300 response classification methods were designed. One is based on independent component analysis (ICA) with blind tracking and the other is based on variance analysis. The purpose of both methods is to detect a chosen character in real-time in the P300-BCI speller. The experimental results demonstrate that the proposed methods dramatically reduce the signal processing time, improve the data communication rate, and achieve overall accuracy of 79.1% for ICA based method and 84.8% for variance analysis based method in single trial P300 response classification task. Both methods showed better performance than that of the single trial stepwise linear discriminant analysis (SWLDA), which has been considered as the most accurate and practical technique working with P300-BCI Speller.
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Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.

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Shahbaz, Askari. "Dual mode brain near infrared spectroscopy and electroencephalography hardware design and signal processing." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58418.

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Katyal, Bhavana. "Multiple current dipole estimation in a realistic head model using signal subspace methods." Online access for everyone, 2004. http://www.dissertations.wsu.edu/Thesis/Summer2004/b%5Fkatyal%5F072904.pdf.

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Mulyana, Ridwan S. "A Low Voltage, Low Power 4th Order Continuous-time Butterworth Filter for Electroencephalography Signal Recognition." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281981810.

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Hasan, Md Mahmudul. "Biomedical signal based drowsiness detection using machine learning: Singular and hybrid signal approaches." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/211388/1/Md%20Mahmudul_Hasan_Thesis.pdf.

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Drowsiness is one of the main contributors to road crashes. This research program examines the utility of drowsiness detection based on singular and hybrid approaches using physiological signals of EEG, EOG, and ECG. Four supervised machine learning models were developed to detect drowsiness levels, using physiological features known to be associated with drowsiness and performance impairment. The ground truth was subjective sleepiness responses while performing a repetitive reaction time task. The outcome of the study indicates that the selected features provided higher performance in the hybrid approaches than the singular approaches, which could be useful for future research implications.
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Salma, Nabila. "EEG Signal Analysis in Decision Making." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984237/.

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Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal processing of brain signals and how brain activity can be quantifiable through data analysis. An experiment is analyzed in this thesis to provide evidence that theta frequency band activity is associated with stress and stress is negatively correlated with concentration and problem solving, therefore hindering decision making skill. From the results of the experiment, it is seen that theta is negatively correlated to delta and beta frequency band activity, thus establishing the fact that stress affects internal focus while carrying out a task.
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Shahriari, Sheyda. "Electroencephalography (EEG) profile and sense of body ownership : a study of signal processing, proprioception and tactile illusion." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16299.

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With the ability to feel through artificial limbs, users regain more function and increasingly see the prosthetics as parts of their own bodies. So, main focus of this project was dedicated to recuperating sensation by deception both in sighted and unsighted patients, started with illusionary experiments on healthy volunteers, brain signals were captured with medical EEG headsets during these tests to have a better understanding of how the brain works during body ownership illusions. EEG results suggest that gender difference exists in the perception of body transfer illusion. Visual input can be induced to trick the brain. Using the results, a new device has been designed (sound generator system-SGS) with the principal goal to find ways to include rich sensory feedback in prosthetic devices that would aid their incorporation of the user's body representation or schema. Studying the brain is fascinating; SGS tested and was found to have an adequate level of dexterity over course of one-month multiple times. After each try, the results were more tolerable than before that proved the idea that brain can learn and understand anything and can be manipulated temporary or lasting due to influences. Different methods used to validate the results, EEG acquisition, mapping subject brain function with EEG and finally interviewing participant after each attempt. Although the results of the illusion shows that when heat applies on rubber hand, subjects behave in similar manner as if their real hand was effected, but main question is still remains. How can the conditioning apply to daily life of amputees so that illusion become permanent? This is a rapidly developing field with advancements in technology and greater interdisciplinary integration of medicine, mechatronics and control engineering with the future looking to have permanent, low power consumption, highly functional devices with a greater intuitive almost natural feel using a variety of body signals including EMG, ultrasound, and Electrocorticography.
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Books on the topic "ELECTROENCEPHALOGRAPHY SIGNAL"

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C, Handy Todd, ed. Brain signal analysis: Advances in neuroelectric and neuromagnetic methods. Cambridge, MA: The MIT Press, 2009.

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Genquan, Feng. EKG and EEG multiphase information analysis. [New York]: American Medical Publishers, 1992.

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Sanei, Saeid. EEG signal processing. Chichester: John Wiley & Sons, 2007.

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Isak, Gath, and Inbar Gideon F, eds. Advances in processing and pattern analysis of biological signals. New York: Plenum Press, 1996.

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1962-, Laguna Pablo, ed. Bioelectrical signal processing in cardiac and neurological applications. Amsterdam: Elsevier Academic Press, 2005.

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M, Dasheiff R., and Vincent D. J, eds. Continuous wave-form analysis. Amsterdam: Elsevier, 1996.

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Statistical signal processing for neuroscience and neurotechnology. Burlington, MA: Academic Press, 2011.

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Brette, Romain, and Alain Destexhe. Handbook of neural activity measurement. Cambridge: Cambridge University Press, 2012.

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Freeman, Walter J. Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. New York, NY: Springer New York, 2013.

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O, Quadens, and European Space Agency, eds. Analysis of EEG signals recorded in microgravity during parabolic flight using the method of strange attractor dimensions. Noordwijk, The Netherlands: ESA, 1999.

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Book chapters on the topic "ELECTROENCEPHALOGRAPHY SIGNAL"

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Rizzo, Cristiano. "EEG Signal Acquisition." In Clinical Electroencephalography, 53–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04573-9_5.

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Rizzo, Cristiano. "EEG Signal Analysis." In Clinical Electroencephalography, 75–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04573-9_6.

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Fathillah, Mohd Syakir, Theeban Raj Shivaraja, Khalida Azudin, and Kalaivani Chellappan. "Electroencephalography and Epileptic Discharge Identification." In Biomedical Signal Processing, 115–38. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003201137-7.

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Bucci, Paola, and Silvana Galderisi. "Physiologic Basis of the EEG Signal." In Standard Electroencephalography in Clinical Psychiatry, 7–12. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470974612.ch2.

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Al-Fraiji, Safaa S., and Dhiah Al-Shammary. "Survey for Electroencephalography EEG Signal Classification Approaches." In Mobile Computing and Sustainable Informatics, 199–214. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1866-6_14.

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Lu, Xuejing, and Li Hu. "Electroencephalography, Evoked Potentials, and Event-Related Potentials." In EEG Signal Processing and Feature Extraction, 23–42. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_3.

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Tahura, Sharaban, S. M. Hasnat Samiul, M. Shamim Kaiser, and Mufti Mahmud. "Anomaly Detection in Electroencephalography Signal Using Deep Learning Model." In Advances in Intelligent Systems and Computing, 205–17. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4673-4_18.

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Pinti, A. "Real Time Acquisition and Signal Processing on Transputers Application to Electroencephalography." In Computing with T.Node Parallel Architecture, 115–33. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3496-5_9.

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Luu, Linh, Phong Pham, and Trung Q. Le. "Feature Extraction and Electrophysiological Modeling in Personalized Deep Brain Structure Using Electroencephalography Signal." In IFMBE Proceedings, 551–56. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5859-3_95.

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Li, Jingjing, Ye Yang, Zhexin Zhang, Yinan Zhao, Vargas Meza Xanat, and Yoichi Ochiai. "Visualizing the Electroencephalography Signal Discrepancy When Maintaining Social Distancing: EEG-Based Interactive Moiré Patterns." In Lecture Notes in Computer Science, 185–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05900-1_12.

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Conference papers on the topic "ELECTROENCEPHALOGRAPHY SIGNAL"

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Sun, Yueyang. "Signal analysis in electroencephalography of epilepsy." In 4TH INTERNATIONAL CONFERENCE ON FRONTIERS OF BIOLOGICAL SCIENCES AND ENGINEERING (FBSE 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0094126.

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Ma, Xunguang, Wenkai Zheng, Zujian Peng, and Jimin Yang. "FPGA-Based Rapid Electroencephalography Signal Classification System." In 2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT). IEEE, 2019. http://dx.doi.org/10.1109/icait.2019.8935935.

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Choi, Ga-Young, Chang-Hee Han, Hyunmi Lim, Jeonghun Ku, Won-Seok Kim, and Han-Jeong Hwang. "Electroencephalography-based Motor Hotspot Detection." In 13th International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008937201950198.

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Qin, Ying-Mei, Chun-Xiao Han, Yan-Qiu Che, and Hui-Yan Li. "Efficient epileptic seizure detection based on electroencephalography signal." In 2017 36th Chinese Control Conference (CCC). IEEE, 2017. http://dx.doi.org/10.23919/chicc.2017.8028198.

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Salahuddin Morsalin, S. M., and Shin-Chi Lai. "Front-end circuit design for electroencephalography (EEG) signal." In 2020 Indo-Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN). IEEE, 2020. http://dx.doi.org/10.1109/indo-taiwanican48429.2020.9181346.

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Weinstein, Alejandro. "Imputing missing electroencephalography data using graph signal processing." In 18th International Symposium on Medical Information Processing and Analysis (SIPAIM 2022), edited by Marius G. Linguraru, Letícia Rittner, Natasha Lepore, Eduardo Romero Castro, Jorge Brieva, and Pamela Guevara. SPIE, 2023. http://dx.doi.org/10.1117/12.2669735.

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Aydemir, Onder. "Classification of electroencephalography signals recorded during smelling." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495935.

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Banville, Hubert, Isabela Albuquerque, Aapo Hyvarinen, Graeme Moffat, Denis-Alexander Engemann, and Alexandre Gramfort. "Self-Supervised Representation Learning from Electroencephalography Signals." In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019. http://dx.doi.org/10.1109/mlsp.2019.8918693.

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Khalafallah, Ayman, Aly Ibrahim, Bahieeldeen Shehab, Hisham Raslan, Omar Eltobgy, and Shady Elbaroudy. "A Pragmatic Authentication System Using Electroencephalography Signals." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461659.

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Pal, Monalisa, Saugat Bhattacharyya, Amit Konar, D. N. Tibarewala, and R. Janarthanan. "Decoding of wrist and finger movement from electroencephalography signal." In 2014 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2014. http://dx.doi.org/10.1109/conecct.2014.6740323.

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Reports on the topic "ELECTROENCEPHALOGRAPHY SIGNAL"

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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