scholarly journals Automated Non-Invasive Identification and Localization of Focal Epileptic Activity by Exploiting Information Derived from Surface EEG Recordings

10.5772/34705 ◽  
2012 ◽  
Author(s):  
Amir Geva ◽  
Merav Ben-Asher ◽  
Dan Kerem ◽  
Mayer Aladjem ◽  
Alon Friedm
Keyword(s):  
Epileptic Activity ◽  
Non Invasive ◽  
Focal Epileptic Activity ◽  
Eeg Recordings ◽  
Surface Eeg

scholarly journals Endogenous memory reactivation during sleep in humans is clocked by slow oscillation-spindle complexes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Thomas Schreiner ◽  
Marit Petzka ◽  
Tobias Staudigl ◽  
Bernhard P. Staresina
Keyword(s):  
Memory Function ◽  
Nrem Sleep ◽  
Stimulus Category ◽  
Learning Material ◽  
Memory Reactivation ◽  
Eeg Recordings ◽  
Surface Eeg ◽  
Function Of Sleep ◽  
Prior Experiences

AbstractSleep is thought to support memory consolidation via reactivation of prior experiences, with particular electrophysiological sleep signatures (slow oscillations (SOs) and sleep spindles) gating the information flow between relevant brain areas. However, empirical evidence for a role of endogenous memory reactivation (i.e., without experimentally delivered memory cues) for consolidation in humans is lacking. Here, we devised a paradigm in which participants acquired associative memories before taking a nap. Multivariate decoding was then used to capture endogenous memory reactivation during non-rapid eye movement (NREM) sleep in surface EEG recordings. Our results reveal reactivation of learning material during SO-spindle complexes, with the precision of SO-spindle coupling predicting reactivation strength. Critically, reactivation strength (i.e. classifier evidence in favor of the previously studied stimulus category) in turn predicts the level of consolidation across participants. These results elucidate the memory function of sleep in humans and emphasize the importance of SOs and spindles in clocking endogenous consolidation processes.

A Method for Classification of Transient Events in EEG Recordings: Application to Epilepsy Diagnosis

2006 ◽  
Vol 45 (06) ◽  
pp. 610-621 ◽  
Author(s):  
A. T. Tzallas ◽  
P. S. Karvelis ◽  
C. D. Katsis ◽  
S. Giannopoulos ◽  
S. Konitsiotis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Epileptic Activity ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Epilepsy Diagnosis ◽  
Transient Events ◽  
Artificial Neural ◽  
Eeg Recordings

Summary Objectives: The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. Methods: A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. Results: The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. Conclusions: The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

scholarly journals A Novel Modular Headmount Design for non-invasive Scalp EEG Recordings in Awake Animal Models*

2018 ◽  
Author(s):  
Catherine Paulson ◽  
Daniel Chien ◽  
Francis Lin ◽  
Stephanie Seidlits ◽  
Yan Cai ◽  
...  
Keyword(s):  
Animal Models ◽  
Non Invasive ◽  
Scalp Eeg ◽  
Eeg Recordings ◽  
Awake Animal

scholarly journals A Neuromorphic Spiking Neural Network Detects Epileptic High Frequency Oscillations in the Scalp EEG

2021 ◽  
Author(s):  
Karla Burelo ◽  
Georgia Ramantani ◽  
Giacomo Indiveri ◽  
Johannes Sarnthein
Keyword(s):  
Low Power ◽  
Seizure Frequency ◽  
High Frequency ◽  
Therapy Monitoring ◽  
Non Invasive ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Eeg Recordings ◽  
Active Epilepsy ◽  
Wearable Electronic Devices

Abstract Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware. Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

scholarly journals CNN for a Connectivity Based Epilepsy Diagnosis with Resting-state EEG

2021 ◽  
Author(s):  
Berjo Rijnders ◽  
Emin Erkan Korkmaz ◽  
Funda Yildirim
Keyword(s):  
Resting State ◽  
Cognitive Abilities ◽  
Brain Connectivity ◽  
Clinical Decision ◽  
Control Group ◽  
Learning To Learn ◽  
Graph Metrics ◽  
Epilepsy Diagnosis ◽  
Eeg Recordings ◽  
Surface Eeg

Objective: This study investigates the performance of a CNN algorithm on epilepsy diagnosis. Without pathology, diagnosis involves long and costly electroencephalographic (EEG) monitoring. Novel approaches may overcome this by comparing brain connectivity using graph metrics. This study, however, uses deep learning to learn connectivity patterns directly from easily acquired EEG data. Approach: A convolutional neural network (CNN) algorithm was applied on directed Granger causality (GC) connectivity measures, derived from 50 seconds of resting-state surface EEG recordings from 30 subjects with epilepsy and a 30 subject control group. Main results: The learned CNN filters reflected reduced delta band connectivity in frontal regions and increased left lateralized frontal-posterior gamma band connectivity. A diagnosis accuracy of 85% (F1-score 85%) was achieved by an ensemble of CNN models, each trained on differently prepared data from different electrode combinations. Conclusions: Appropriate preparation of connectivity data enables generic CNN algorithms to be used for detection of multiple discriminative epileptic features. Differential patterns revealed in this study may help to shed light on underlying altered cognitive abilities in epilepsy patients. Significance: The accuracy achieved in this study shows that, in combination with other methods, this approach could prove a valuable clinical decision support system for epilepsy diagnosis.

scholarly journals With or without spikes: localization of focal epileptic activity by simultaneous electroencephalography and functional magnetic resonance imaging

Brain ◽  
2011 ◽  
Vol 134 (10) ◽  
pp. 2867-2886 ◽  
Author(s):  
Frédéric Grouiller ◽  
Rachel C. Thornton ◽  
Kristina Groening ◽  
Laurent Spinelli ◽  
John S. Duncan ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Epileptic Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Focal Epileptic Activity

Effects of electrical stimulation of the superior colliculi on focal epileptic activity in the cerebral cortex

1993 ◽  
Vol 116 (2) ◽  
pp. 900-903
Author(s):  
G. N. Kryzhanovskii ◽  
A. A. Shandra ◽  
L. S. Godlevskii ◽  
S. L. Vikhrestyuk
Keyword(s):  
Cerebral Cortex ◽  
Electrical Stimulation ◽  
Epileptic Activity ◽  
Superior Colliculi ◽  
Focal Epileptic Activity ◽  
Stimulation Of

MEG/EEG in the Study of Brain Function

2017 ◽  
pp. 304-310
Author(s):  
Riitta Hari ◽  
Aina Puce
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Social Neuroscience ◽  
Future Research ◽  
Noninvasive Methods ◽  
Advantages And Disadvantages ◽  
Non Invasive ◽  
Combined Use ◽  
Eeg Recordings ◽  
Two Sides

This chapter summarizes some relative advantages and disadvantages of MEG and EEG, most of which have been previously elaborated. MEG and EEG are the two sides of the same coin and provide complementary information about the human brain’s neurodynamics. The combined use of MEG or EEG together and with other noninvasive methods used to study human brain function is advocated to be important for future research in systems and cognitive/social neuroscience. This chapter also examines combined use and interpretation of MEG/EEG with MRI/fMRI, and performing EEG recordings during non-invasive brain stimulation.

Simultaneous VTR-Surface EEG Recordings of Simple Partial Seizures of Childhood Onset

1993 ◽  
Vol 47 (2) ◽  
pp. 278-279 ◽  
Author(s):  
Akihisa Okumura ◽  
Takashi Ohki ◽  
Norihide Maeda ◽  
Masao Kito ◽  
Yoshiko Haga ◽  
...  
Keyword(s):  
Partial Seizures ◽  
Childhood Onset ◽  
Eeg Recordings ◽  
Surface Eeg

Focal Epileptic Activity and its Spread in the Brain of Lower Vertebrates.

Epilepsia ◽  
1970 ◽  
Vol 11 (3) ◽  
pp. 227-240 ◽  
Author(s):  
Z. SERVÍT
Keyword(s):  
Epileptic Activity ◽  
Focal Epileptic Activity ◽  
Lower Vertebrates ◽  
The Brain
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