Many stroke survivors are unable to effectively control brain-computer interface (BCI) devices due to insufficient sensorimotor activity generated during motor imagery. Previous studies focused on upregulating motor cortex excitability and overlooked the important role that motor imagery plays on BCI control. Dorsolateral prefrontal cortex (DLPFC), an important region for motor imagery, may serve as an effective target for improving BCI performance.
This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds.
The data set used here is a part of the Bialystok PLUS cohort study, where residents of Białystok were randomly selected from the Municipal Office database and invited to participate in the study. In this part of study they were between 30 and 70 years old and constituted a representative sample of the local population. Data contains proteomics and atherosclerosis risk factors.
This dataset has been compiled and derived from publicly available dermatological image collections, including the ISIC 2018 Skin Lesion Dataset and the Atlas Dermatology archive. It comprises 49,100 high-resolution, anonymized images categorized into 32 classes, including 31 dermatological diseases and an additional “Unknown” class to improve real-world generalization. Each image is labeled based on expert classification standards and curated for deep learning applications.
The dataset corresponding to the paper "A Time-Frequency Disentanglement Framework with Liquid Neural Networks for Biometric Identification and Physiological Interference Analysis from PPG Sensor Data"
We developed IIST BCI Dataset-9, a novel EEG-based Brain-Computer Interface (BCI)
dataset to improve wheelchair control systems using Malayalam dialect variations. BCI
systems help people with motor disabilities by allowing them to control devices using brain
signals. The limited number of BCI datasets in Indian languages makes it harder for native
speakers to use these systems. To address this, we created a dataset with 15 Malayalam
words related to basic wheelchair commands like Forward, Backward, Go, Stop, Reverse,
Hamstring Injuries (HSIs) are common among athletes and necessitate extended rehabilitation before Return to Sport (RTS). Post-injury, athletes undergo physical examinations, which often fall short in assessing injury severity or guiding rehabilitation. Therefore, imaging techniques such as Magnetic Resonance Imaging (MRI) are used to evaluate the injury more comprehensively, aiding in the assessment of optimal rehabilitation and RTS timelines. Given the significant impact of HSIs on athletic careers, early prediction is essential.
