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With the widespread application of electroencephalography (EEG) in neuroscience and clinical practice, efficiently retrieving and semantically interpreting large-scale, multi-source, heterogeneous EEG data has become a pressing challenge.…
The brain's biological age has been considered as a promising candidate for a neurologically significant biomarker. However, recent results based on longitudinal magnetic resonance imaging data have raised questions on its interpretation. A…
Ischemic brain injuries are frequent and difficult to detect reliably or early. We present the multi-modal data set containing cardiovascular (blood pressure, blood flow, electrocardiogram) and brain electrical activities to derive…
Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation,…
Event-related potentials (ERPs) are very small voltage produced by the brain in response to external stimulation. In order to detect and evaluate an ERP in an ongoing electroencephalogram (EEG), it is necessary to tag the EEG with the exact…
Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG) data which would help administer early and effective treatment for dementia patients.…
Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable for ECG interpretation, often producing plausible but clinically incorrect…
Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive process that demands highly specialized expertise, which remains relatively scarce and not…
The conversion of brain activity into text using electroencephalography (EEG) has gained significant traction in recent years. Many researchers are working to develop new models to decode EEG signals into text form. Although this area has…
The application of psychophysiology in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of an array of physiological…
Physiological fatigue, a state of reduced cognitive and physical performance resulting from prolonged mental or physical exertion, poses significant challenges in various domains, including healthcare, aviation, transportation, and…
Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making. This paper presents…
The early detection of Parkinsons disease remains a critical challenge in clinical neuroscience, with electroencephalography offering a noninvasive and scalable pathway toward population level screening. While machine learning has shown…
The present literature about possible mechanisms behind the effectivity of noninvasive electromagnetic stimulation in major depressive disorder (MDD) is not very rich. Despite extensive research in applications for clinical practice, the…
Tinnitus is a prevalent auditory condition lacking objective biomarkers, motivating the search for reliable neural signatures. EEG, being a noninvasive method of brain imaging with a high temporal resolution provides a way to investigate…
Premise. Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their…
Electroencephalography (EEG) is a neuroimaging technique that records brain neural activity with high temporal resolution. Unlike other methods, EEG does not require prohibitively expensive equipment and can be easily set up using…
Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders and neurological abnormalities. Enhanced analysis and classification of EEG signals can help improve detection performance. A new approach is examined…
Electroencephalography(EEG) classification is a crucial task in neuroscience, neural engineering, and several commercial applications. Traditional EEG classification models, however, have often overlooked or inadequately leveraged the…
The purpose of this document is to help individuals use the "Essential Motor Cortex Signal Processing MATLAB Toolbox". The toolbox implements various methods for three major aspects of investigating human motor cortex from Neuroscience view…