Biophysiological Signals

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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Respiratory diseases are major global killers, demanding early diagnosis for effective management. Digital stethoscopes offer promise, but face limitations in storage and transmission. A compressive sensing-based compression algorithm is needed to address these constraints. Meanwhile, fast-reconstruction CS algorithms are sought to balance speed and fidelity. Sound event detection algorithms are crucial for identifying abnormal lung sounds and augmenting diagnostic accuracy. Integrating these technologies can revolutionize respiratory disease management, enhancing patient outcomes.

Last Updated On: 
Mon, 03/10/2025 - 02:20

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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With the continuous advancement of technology, small and portable physiological sensors that can be worn on the body are quietly integrating into our daily lives, and are expected to greatly enhance our quality of life. In order to further enrich and expand the emotional physiological signals captured by portable wearable devices, we utilized the 14-channel portable EEG acquisition device Emotiv EPOC X, and with emotional video clips as the stimulus source, we collected two sets of emotional EEG signals from two groups of 10 participants each, named EmoX1 and EmoX2.

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Stress became a common factor of individuals in this competitive work environment, especially in academics. To address and assess this issue, this MUSEI-EEG dataset provides the Electroencephalogram (EEG) data of 20 undergraduate individuals in the 18-24 years age group (both male and female). Raag Darbari's music-based three-stage paradigm is designed for the subjects for cognitive stress assessment. Through this paradigm, physiological signal-based monitoring of stress level reduction can be observed in reference to stress and anxiety forms filled by the individual.

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This study introduces a multimodal dataset collected to investigate the psychophysiological relationship between perceived fear and muscle activity in climbers. The dataset includes physiological, motion, and subjective data from 19 climbers during \textit{lead} and \textit{top rope climbing} ascent styles, which differ in perceived risk due to varying fall distances. Physiological data were recorded using EMG, ECG, and IMU sensors. Additionally, subjective fear ratings were collected at distinct phases of the climb.

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