Biophysiological Signals
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder affecting children and adolescents, characterized by inattention, hyperactivity, and impulsivity. Current diagnostic methods primarily rely on subjective clinical evaluations, which are prone to bias. Advances in neurophysiological assessment, particularly through electroencephalography (EEG), eye tracking, and electrodermal activity (EDA), offer promising avenues for objective diagnosis and monitoring of ADHD.
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This dataset was collected to support research on the screening and diagnosis of Diabetic Peripheral Neuropathy (DPN) and Cardiac Autonomic Neuropathy (CAN) using wearable sensor technology. It includes synchronized data from gait analysis and physiological signals such as electrocardiogram (ECG), heart rate variability (HRV), and inertial measurement units (IMUs) obtained from individuals with and without DPN and CAN.
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This study investigated neural mechanisms underlying working memory by employing a visual n-back task with graded cognitive load (0-back to 3-back). Ten healthy volunteers (6 males, 4 females; mean age 23.3 ± 0.9 years) participated, performing a spatial matching task where they judged whether the current position of a displayed square matched the position presented n trials earlier, responding via keypress ("V" for match, "N" for non-match).
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This database, collected at the Neural Engineering Laboratory, Iran University of Science and Technology, comprises iEEG recordings from Wistar rats during healthy and epileptic conditions. Recordings were collected from 5 rats (3 males, 2 females, weighing 260-378 g and aged 4-5 months). iEEG signals were recorded from 3 brain sites: motor cortex (left M1), thalamus (left ANT), and hippocampus (right CA1) of freely moving rats. As a result, for each rat, a matrix with 3 columns (representing the 3 signals) is available in this dataset.
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Small intestinal serosa voltage is defined as a potential in the serosa relative to the mesentery, which provides good visualization of the neural activity and the health of the electrical activity of the Interstitial Cells of Cajal that control contractions. This dataset consists of a .csv format of porcine small intestinal serosa voltage measured during ex vivo blood reperfusion, which mimics the damage to the organ during transplantation.
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A collection of Python pickles objects containing a Pandas DataFrame. Each Dataframe corresponds to the postprocessed firing rate (fr) in Hz and mean amplitude of the spikes (AMP) in microV/s of the vagus nerve recordings obtained from 12 adult female Sprague-Dawley rats. Additionally, the blood-glucose level in mg/dL is included. The fr and AMP signals have 0.1 miliseconds of resolution, whereas the glucose level was measured approximately every 5 minutes. Temporal variations are due to experimental factors. The number of available glucose samples changes across recordings.
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Gait Event Detection (GED) plays a pivotal role in understanding human locomotion, with applications spanning rehabilitation, prosthesis design, sports science, and biomechanics. Accurate identification of key gait events—such as Heel Strike (HS), Loading Response (LS), Mid-Stance (MS), and Heel Off (HO)—during the stance phase of the gait cycle is essential for analyzing movement patterns, diagnosing gait abnormalities, and developing assistive technologies.
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The Clarkson University Affective Data Set (CUADS) is a multi-modal affective dataset designed to assist in machine learning model development for automated emotion recognition. CUADS provides electrocardiogram, photoplethysmogram, and galvanic skin response data from 38 participants, captured under controlled conditions using Shimmer3 ECG and GSR sensors. ECG, GSR and PPG signals were recorded while each participant viewed and rated 20 affective movie clips. CUADS also provides big five personality traits for each participant.
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