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Automated human mind reading using EEG signals for seizure detection

Virender Ranga, Shivam Gupta, Jyoti Meena, Priyansh Agrawal
2020 Journal of Medical Engineering & Technology  
Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection.  ...  In the present paper, a model is proposed to give an accuracy of 98.33% which can be used for development of automated systems.  ...  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine, 100, 270-278. Convolutional Neural Network.  ... 
doi:10.1080/03091902.2020.1791988 pmid:32657667 fatcat:wta3ffedtzdednxrffitvptaua

Deep Learning Human Mind for Automated Visual Classification [article]

Concetto Spampinato and Simone Palazzo and Isaak Kavasidis and Daniela Giordano and Mubarak Shah and Nasim Souly
2019 arXiv   pre-print
This gives us a real hope that, indeed, human mind can be read and transferred to machines.  ...  What if we could effectively read the mind and transfer human visual capabilities to computer vision methods?  ...  Martina Platania for carrying out EEG data acquisition as well as Dr. Riccardo Ricceri for supporting experimental protocol setup.  ... 
arXiv:1609.00344v2 fatcat:6tu5t4h2qnh4rammj3euhh2ac4

Deep Learning Human Mind for Automated Visual Classification

C. Spampinato, S. Palazzo, I. Kavasidis, D. Giordano, N. Souly, M. Shah
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
What if we could effectively read the mind and transfer human visual capabilities to computer vision methods?  ...  The proposed RNN-based approach for discriminating object classes using brain signals reaches an average accuracy of about 83%, which greatly outperforms existing methods attempting to learn EEG visual  ...  Martina Platania for carrying out EEG data acquisition as well as Dr. Riccardo Ricceri for supporting experimental protocol setup.  ... 
doi:10.1109/cvpr.2017.479 dblp:conf/cvpr/SpampinatoPKGSS17 fatcat:fvsyrmzj3zdejlzucsll7mulcm

Seizure Prediction

J. Chris Sackellares
2008 Epilepsy Currents  
There is mounting evidence that seizures are preceded by characteristic changes in the EEG that are detectable minutes before seizure onset.  ...  This research has led to the development of automated seizure prediction algorithms.  ...  Some investigators have found evidence for EEG signal changes preceding seizure onset, and some have questioned the necessity of using the more complicated nonlinear methods.  ... 
doi:10.1111/j.1535-7511.2008.00236.x pmid:18488065 pmcid:PMC2384160 fatcat:7wv2b3nhnbdxza6ffsbyuldvkq

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

Baharan Kamousi, Suganya Karunakaran, Kapil Gururangan, Matthew Markert, Barbara Decker, Pouya Khankhanian, Laura Mainardi, James Quinn, Raymond Woo, Josef Parvizi
2020 Neurocritical Care  
The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10%  ...  A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures.  ...  Ethical approval and informed consent The study was classified as exempt research according to the US Department of Health and Human Services regulation 45 CFR 46.104(d)(4), and individual patient consent  ... 
doi:10.1007/s12028-020-01120-0 pmid:33025543 fatcat:qssqcwvduzb55ekdizjf56rp3u

Artificial Neural Network analysis of EEG waves in complex partial seizure patients

Shikha Saxena, Kamal Kant Gupta
2021 Nepal Journal of Neuroscience  
EEG-based epilepsy diagnosis and seizure detection is still in its infancy.  ...  This study proposes an automatic classification system for epilepsy based on neural networks and EEG signals.  ...  It is widely accepted that EEG analysis could be employed for early detection of varied dysfunctions of the human brain such as depression, epilepsy, autism and Alzheimer's disease. 5 The human mind,  ... 
doi:10.3126/njn.v18i1.31668 doaj:3e265bef94094bb7ac567379c05e08dc fatcat:5usjga4uqvb57nryfrlbgw32ra

Detection of seizure precursors from depth-EEG using a sign periodogram transform

J.J. Niederhauser, R. Esteller, J. Echauz, G. Vachtsevanos, B. Litt
2003 IEEE Transactions on Biomedical Engineering  
The JSPECT method efficiently detects these events, and may be useful as part of an automated system for predicting electrical seizure onset in appropriate patients.  ...  We present a method for detecting and displaying these events using a periodogram of the sign-limited temporal derivative of the EEG signal, denoted joint sign periodogram event characterization transform  ...  ACKNOWLEDGMENT The authors would like to thank Leif Finkel and Stephen Cranstoun for their suggestions and encouragement.  ... 
doi:10.1109/tbme.2003.809497 pmid:12723056 fatcat:yrvnv2deonezdbcgiklycvnxnm

Review on diverse approaches used for epileptic seizure detection using EEG signals

K Baskar, C Karthikeyan
2018 Bangladesh Journal of Medical Science  
<p>Epileptic seizure detection is a common diagnosis practiced by the expert clinicians through direct visual observation from the electroencephalography (EEG) signal.  ...  This study reviews different approaches, which is been designed to aid the human diagnosis using new avenues that explains the causes of epilepsy and seizures.  ...  It also provides a better perception of cognitive process, specifically in reading the signals.The efficient classification technique segments well the EEG signal, which is used for good decision making  ... 
doi:10.3329/bjms.v17i4.38307 fatcat:oppbo2aznvfjdmyvxtfarxwer4

Interictal Epileptiform Discharges and the Quality of Human Intracranial Neurophysiology Data

Simon G. Ammanuel, Jonathan K. Kleen, Matthew K. Leonard, Edward F. Chang
2020 Frontiers in Human Neuroscience  
We then describe four general strategies used when handling IEDs (manual identification, automated identification, manual-automated hybrids, and ignoring by leaving them in the data), and discuss their  ...  It is performed on patients with medically refractory epilepsy, undergoing pre-surgical seizure localization.  ...  ACKNOWLEDGMENTS We thank Maxime Baud and Han Yi for their helpful discussions and comments during the development of this manuscript.  ... 
doi:10.3389/fnhum.2020.00044 pmid:32194384 pmcid:PMC7062638 fatcat:3mxg7qdm7fcclkw5u7p7ia4vqe

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Sriram Ramgopal, Sigride Thome-Souza, Michele Jackson, Navah Ester Kadish, Iván Sánchez Fernández, Jacquelyn Klehm, William Bosl, Claus Reinsberger, Steven Schachter, Tobias Loddenkemper
2014 Epilepsy & Behavior  
Artificial neural network Automated seizure detection Closed-loop methods ECG-based seizure detection EEG-based seizure detection Fourier Higher-order spectra Markov modeling Support vector machine Nearly  ...  Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification.  ...  Acknowledgments The authors would like to thank Alexander Mylavarapu, MD, for his assistance with Fig. 2 . This study was supported by the Danny Did foundation.  ... 
doi:10.1016/j.yebeh.2014.06.023 pmid:25174001 fatcat:rtz5sxb7mvhivpmgutg7dwoja4

Statistical Wavelet Features, PCA, MLPNN, SVM and K-NN Based Approach for the Classification of EEG Physiological Signal

Manisha Chandani
2017 International Journal of Industrial and Manufacturing Systems Engineering  
So, the detection of an epileptic seizure based on DWT statistical features using NNA classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the  ...  The framework of proposed technique is an efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not.  ...  Andrzejak of University of Bonn, Germany, for providing permission to use the EEG data available in the public domain [10] . I owe a debt of gratitude to my project guide Dr.  ... 
doi:10.11648/j.ijimse.20170205.12 fatcat:s3skpu4w7vbttpqnt3xh4brx7i

A review of epileptic seizure detection using machine learning classifiers

Mohammad Khubeb Siddiqui, Ruben Morales-Menendez, Xiaodi Huang, Nasir Hussain
2020 Brain Informatics  
The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media.  ...  As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features.  ...  Mohammad Arshad, English language expert, Shoumou Investment and Trading Company, KSA for proofreading the paper.  ... 
doi:10.1186/s40708-020-00105-1 pmid:32451639 fatcat:xpsjchelv5cdtkrp5my7u3ahre

Electroencephalogram based Brain Computer Interface System Analysis

With the recent changes in technology better electrodes are being used which can catch highly sensitive signals as well.[1][2] EEG based BCI systems can change the world for many people as it holds so  ...  The electrodes read the brain signals, amplifies them in order to be studied more accurately by the machine send them to machine after converting it into digital form.  ...  Automated EEG signal anal-ysis for identification of epilepsy seizures and brain tumour. J. Med. Eng. Technol. 37 (8), 511519. 33. Sharanreddy, M., Kulkarni, P.K., 2013b.  ... 
doi:10.35940/ijitee.c1092.0193s20 fatcat:nldtc2tj4zh4jc67wr5iektjle

Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture

Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
2020 Neural Networks  
A deep learning classifier for detecting seizures in neonates is proposed.  ...  This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representation employed in traditional  ...  For an automated seizure detection algorithm to be clinically useful, its level of performance should match that of a human expert annotator i.e. it should have a comparable inter-observer agreement when  ... 
doi:10.1016/j.neunet.2019.11.023 pmid:31821947 fatcat:t4gskbbdzvb27og2gnq2itmwby

A Survey on Brain-Computer Interface and Related Applications [article]

Krishna Pai, Rakhee Kallimani, Sridhar Iyer, B.Uma Maheswari, Rajashri Khanai, Dattaprasad Torse
2022 arXiv   pre-print
For BCI systems to be widely used by people with severe disabilities, long-term studies of their real-world use are needed, along with effective and feasible dissemination models.  ...  BCI systems are able to communicate directly between the brain and computer using neural activity measurements without the involvement of muscle movements.  ...  Hence detecting the seizures using a classifier named random forest.  ... 
arXiv:2203.09164v1 fatcat:evyespeyujadnayn7c62ba5eoa
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