Normal EEG variants, especially the epileptiform variants, can be challenging to interpret because they often have sharp contours and may be confused with "epileptic" interictal activities. However, they can be recognized by the fact that "most spikes or sharp wave discharges of clinical import are followed by a slow wave or a series of slow deflections" (Maulsby, 1971). If there is no wave after the spike, electroencephalographers should be suspicious of artifacts and normal EEG variants. Most normal EEG variants display a single rhythm with the same frequency within the pattern and the morphology remains stable throughout the entire EEG recording with repetition of the same pattern. In case of doubt or difficulties with a standard EEG, it is recommended to undergo an EEG that includes sleep stages with or without sleep deprivation. Finally, epileptiform is an ambiguous term corresponding to an electroencephalographic trait. Epileptiform does not imply a pathological condition, including epilepsy. The clinical context remains the most paramount in the diagnosis of epilepsy. In this article, we propose a set of rules and guidelines to identify normal EEG variants in EEG tracings and normal variation of the background activity. It is not easy to accurately assign a specific/precise name to all EEG activity, but with an orderly approach to EEG that involves using a set of criteria, nonepileptic activity can be identified.
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http://dx.doi.org/10.1007/s00415-024-12440-y | DOI Listing |
Int J Audiol
January 2025
Department of Neurosciences, Research Group ExpORL, KU Leuven, Leuven, Belgium.
Objective: Auditory-steady state responses (ASSRs) to stimuli modulated by different frequencies may differ between children and adults. These differences in response characteristics or latency may reflect developmental changes. This study investigates age-related differences in response strength, latencies, and hemispheric laterality indices of ASSRs for different modulation frequencies.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Correct, Clinical and Imaging Anatomy, Medical University of Lublin, ul. Jaczewskiego 4, 20-090 Lublin, Poland.
As speech-related symptoms of Landau-Kleffner syndrome (LKS) are often refractory to pharmacotherapy, and resective surgery is rarely available due to the involvement of the vital cortex, multiple subpial transection (MST) was suggested to improve patient outcome and preserve cortical functions. Here, we analyze the reports about MST use in LKS, regarding its impact on seizures, language, behavior, EEG, cognition, and reported adverse effects. In conditions like LKS, surgery is not a popular treatment option and presumably should be considered sooner.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Faculty of Electronics, Communication and Computers, Pitești University Center, National University of Science and Technology POLITEHNICA Bucharest, 110040 Pitesti, Romania.
Anxiety is a widespread mental health issue, and binaural beats have been explored as a potential non-invasive treatment. EEG data reveal changes in neural oscillation and connectivity linked to anxiety reduction; however, harmonics introduced during signal acquisition and processing often distort these findings. Existing methods struggle to effectively reduce harmonics and capture the fine-grained temporal dynamics of EEG signals, leading to inaccurate feature extraction.
View Article and Find Full Text PDFBioengineering (Basel)
November 2024
Department of Physical Culture, Gdansk University of Physical Education and Sport, Kazimierza Górskiego 1, 80-336 Gdańsk, Poland.
The objective of this study is to evaluate the impact of EEG biofeedback training under normoxic and normobaric hypoxic conditions on both simple and complex reaction times in judo athletes, and to identify the optimal training frequency and environmental conditions that substantially enhance reaction times in the examined athlete groups. The study comprised 20 male judo athlete members of the Polish national judo team in the middleweight and heavyweight categories. We randomly assigned participants to an experimental group and a control group.
View Article and Find Full Text PDFBrain Sci
November 2024
Department of Neurology, Beth Isreal Deaconess Medical Center, Harvard Medical School, Harvard University, Cambridge, MA 02215, USA.
: Manually labeling sleep stages is time-consuming and labor-intensive, making automatic sleep staging methods crucial for practical sleep monitoring. While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. In this study, four public data sets-Sleep-SC, APPLES, SHHS1, and MrOS1-are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging.
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