Brain
Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies. Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.
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This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/
The DataPort Repository contains the data used primarily for generating Figure 1.
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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 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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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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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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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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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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This is just a preliminary collation of the relevant TCGA datasets collated and used in our methodology. We will continue to upload the full dataset later for your reference and use. We hope to make a small contribution to the study of automatic 3D MRI classification of gliomas and the problem of domain adaptation on medical images.
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Synaptic vesicle glycoprotein 2A (SV2A) is the most widely distributed transmembrane glycoprotein present on secretory vesicles in the pre-synaptic terminal of neurons throughout the central nervous system (Bajjalieh et al., 1994). SV2A can be used as a marker to visualize pre-synaptic density distribution in vivo using positron emission tomography (PET) imaging thanks to the SV2A radioligands available, including [11C]UCB-J (Nabulsi et al., 2016). Given the brain-wide distribution of SV2A, regional analysis of SV2A PET data may be limiting the amount of information that can be obtained.
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