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The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localising epileptogenic tissue. However, this identification may be challenging during non-seizure…

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this…

Neurons and Cognition · Quantitative Biology 2023-04-07 Vytene Janiukstyte , Thomas W Owen , Umair J Chaudhary , Beate Diehl , Louis Lemieux , John S Duncan , Jane de Tisi , Yujiang Wang , Peter N Taylor

Aperiodic activity is a physiologically distinct component of the electrophysiological power spectrum. It is suggested to reflect the balance of excitation and inhibition in the brain, within selected frequency bands. However, the impact of…

Neocortical epilepsy surgery fails to achieve post-operative seizure freedom in 30-40% of cases. It is not fully understood why surgery in some patients is unsuccessful. Comparing interictal MEG bandpower from patients to normative maps,…

Objective: Identifying abnormalities in interictal intracranial EEG, by comparing patient data to a normative map, has shown promise for the localisation of epileptogenic tissue and prediction of outcome. The approach typically uses short…

Successful epilepsy surgery depends on localising and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially…

Intracranial EEG (iEEG) is the gold standard technique for epileptogenic zone (EZ) localisation, but requires a hypothesis of which tissue is epileptogenic, guided by qualitative analysis of seizure semiology and other imaging modalities…

Epileptic seizures are neurological disorders characterized by abnormal and excessive electrical activity in the brain, resulting in recurrent seizure events. Electroencephalogram (EEG) signals are widely used for seizure diagnosis due to…

Machine Learning · Computer Science 2026-04-02 Ferdaus Anam Jibon , Fazlul Hasan Siddiqui , F. Deeba , Gahangir Hossain

Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires expertise and experience to identify abnormalities. It is thus crucial to develop automated models for the detection of abnormalities in EEGs related…

Signal Processing · Electrical Eng. & Systems 2021-11-23 Taku Shoji , Noboru Yoshida , Toshihisa Tanaka

Normative mapping is a framework used to map population-level features of health-related variables. It is widely used in neuroscience research, but the literature lacks established protocols in modalities that do not support healthy control…

When investigating suitability for surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to…

We investigate the nature of the modifications in the temporal dynamics manifested in the high-frequency EEG spectra of the normal human brain in comparison to the diseased brain undergoing epilepsy. For this purpose, the Fourier…

Chaotic Dynamics · Physics 2025-08-19 Jyotiraj Nath , Shreya Banerjee , Bhaswati Singha Deo , Mayukha Pal , Prasanta K. Panigrahi

Timely diagnosis is important for saving the life of epileptic patients. In past few years, a lot of treatments are available for epilepsy. These treatments require use of anti-seizure drugs but are not effective in controlling frequency of…

Machine Learning · Computer Science 2021-11-08 Shivam Gupta , Jyoti Meena , O. P Gupta

The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. Hence, neurologists treating epileptic patients monitor the temporal evolution of the different…

Neurons and Cognition · Quantitative Biology 2020-06-17 Melisa Maidana Capitán , Nuria Cámpora , Claudio Sebastián , Sigvard Silvia Kochen , Inés Samengo

Epilepsy, affecting approximately 50 million people globally, is characterized by abnormal brain activity and remains challenging to treat. The diagnosis of epilepsy relies heavily on electroencephalogram (EEG) data, where specialists…

Epilepsy is one of the common neurological disorders characterized by recurrent and uncontrollable seizures, which seriously affect the life of patients. In many cases, electroencephalograms signal can provide important physiological…

Neurons and Cognition · Quantitative Biology 2023-08-15 Mohammad Reza Yousefi , Saina Golnejad , Melika Mohammad Hosseini , Amin Dehghani

The diagnosis of epilepsy generally includes a visual inspection of EEG recorded data by the Neurologist, with the purpose of checking the occurrence of transient waveforms called interictal epileptiform discharges. These waveforms have…

Computer Vision and Pattern Recognition · Computer Science 2016-06-08 Andre Rosado , Agostinho C Rosa

Epilepsy is a neurological disorder that affects normal neural activity. These electrical activities can be recorded as signals containing information about the brain known as Electroencephalography (EEG) signals. Analysis of the EEG…

Signal Processing · Electrical Eng. & Systems 2025-07-10 Fatemeh Valipour , Zahra Valipour , Mani Garousi , Ali Khadem

During the last few years, there has been growing interest in the effects induced by individual variability on activation patterns and brain connectivity. The practical implications of individual variability is of basic relevance for both…

Signal Processing · Electrical Eng. & Systems 2020-01-29 Matteo Demuru , Matteo Fraschini

Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves, i.e., disordered electrical brainwave activity in the…

Signal Processing · Electrical Eng. & Systems 2023-06-26 Junru Chen , Yang Yang , Tao Yu , Yingying Fan , Xiaolong Mo , Carl Yang
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