Brain
Autism spectrum disorder (ASD) is characterized by qualitative impairment in social reciprocity, and by repetitive, restricted, and stereotyped behaviors/interests. Previously considered rare, ASD is now recognized to occur in more than 1% of children. Despite continuing research advances, their pace and clinical impact have not kept up with the urgency to identify ways of determining the diagnosis at earlier ages, selecting optimal treatments, and predicting outcomes. For the most part this is due to the complexity and heterogeneity of ASD.
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This study used datasets from two hospitals. These data were collaborated by physician diagnosis. Before using the data obtained from the two hospitals, the data were processed in such a way that no personal data such as names, addresses or phone numbers were stored in the dataset. Therefore, third parties cannot identify personal data in the dataset. Consent was also obtained from the hospitals where the data were collected and from the individuals participating in this study.
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This is an auditory attention decoding dataset including EEG recordings of 21 subjects when they were instructed to attend to one of the two competing speakers at two different locations.
Unlike previous datasets (such as the KUL dataset), the locations of the two speakers are randomly drawn from fifteen alternatives.
All subjects have given formal written consent approved by the Nanjing University ethical committee before the experiment and received financial compensation upon completion.
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595 Views
Accurate detection and segmentation of brain tumors is critical for medical diagnosis. We propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor segmentation. TSGM was trained on the BraTs2020 brain tumor dataset.
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Dataset I: This is the original EEG data of twelve healthy subjects for driver fatigue detection. Due to personal privacy, the digital number represents different participants. The .cnt files were created by a 40-channel Neuroscan amplifier, including the EEG data in two states in the process of driving.
Dataset II: This project adopted an event-related lane-departure paradigm in a virtual-reality (VR) dynamic driving simulator to quantitatively measure brain EEG dynamics along with the fluctuation of task performance throughout the experiment.
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2343 Views
Features extracted from EEG when subjects imagined the musical pitch from C4 to B4. The feature extraction method is introduced in "Decoding Imagined Musical Pitch from Human Scalp Electroencephalograms".
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291 Views
We have utilized Ultrasound (US) B-mode imaging to record single agents and collective swarms of microrobots in controlled experimental conditions.
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384 Views
This dataset consists of electroencephalography (EEG) data from 10 healthy participants aged between 24 and 38 years with a mean age of 30 years (standard deviation 5 years). Five participants are male, and all the participants are right-handed.
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Design of EEG-TMS experiment. The figure shows the timeline of the experimental session, the illustration of the typical sequence of the visual cues and the structure of one trial, and the illustration of the time intervals of interest within a trial: Pre is the baseline pre-que interval [-4.5 -0.5] s; Post is the post-cue interval [0 0.5] s; Img is the interval [1 3] s of motor imagery execution; here, t=0 corresponds to the moment of the appearance of the visual cue to start the movement.
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188 Views