45 machine learning datasets
45 dataset results
This dataset is a BIDS compatible version of the Siena Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect:
The database consists of EEG recordings of 14 patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena. Subjects include 9 males (ages 25-71) and 5 females (ages 20-58). Subjects were monitored with a Video-EEG with a sampling rate of 512 Hz, with electrodes arranged on the basis of the international 10-20 System. Most of the recordings also contain 1 or 2 EKG signals. The diagnosis of epilepsy and the classification of seizures according to the criteria of the International League Against Epilepsy were performed by an expert clinician after a careful review of the clinical and electrophysiological data of each patient.
This dataset is obtained during an ICON project (2017-2018) in collaboration with KU Leuven (ESAT-STADIUS), UZ Leuven, UCB, Byteflies and Pilipili. The goal of this project was to design a system using Behind the ear (bhE) EEG electrodes for monitoring the patient in a home environment. This way, a nice balance can be found between sufficient accuracy of seizure detection algorithms (because EEG is used) and wearability (bhe EEG is relatively subtle, similar to a hear-aid device). The dataset acquired in the hospital during presurgical evaluation. During such presurgical evaluation, neurologists try to see if a specific part of the brain is causing the seizures, and if so, if that part of the brain can be removed during surgery. During the presurgical evaluation, patients are monitored using the vEEG for multiple days (typically a week). Patients are however restricted to move within their room because of the wiring and video analysis. In this dataset, following data is available per p
Characterising multimedia content with relevant, reliable and discriminating tags is vital for multimedia information retrieval. With the rapid expansion of digital multimedia content, alternative methods to the existing explicit tagging are needed to enrich the pool of tagged content. Currently, social media websites encourage users to tag their content. However, the users’ intent when tagging multimedia content does not always match the information retrieval goals. A large portion of user defined tags are either motivated by increasing the popularity and reputation of a user in an online com-munity or based on individual and egoistic judgments. Moreover, users do not evaluate media content on the same criteria. Some might tag multimedia content with words to express their emotion while others might use tags to describe the content. For example, a picture receive different tags based on the objects in the image, the camera by which the picture was taken or the emotion a user felt look
Temporal Dataset for Indoor and In-Vehicle Thermal Comfort Estimation Abstract Thermal comfort estimation is essential for enhancing user experience in static indoor environments and dynamic in-vehicle scenarios. While traditional datasets focus on buildings, their application to fast-changing conditions, such as in vehicles, remains unexplored. We address this gap by introducing two temporal datasets collected from (1) a self-built climatic chamber with 31 sensor signals and user-labeled ratings from 18 participants and (2) in-vehicle studies with 20 participants in a BMW 3 Series.
Dataset Description This dataset consists of Electroencephalography (EEG) data recorded from 15 healthy subjects using a 64-channel EEG headset during spoken and imagined speech interaction with a simulated robot.
Please refer to the Zenodo page for a detailed description: https://zenodo.org/records/15665101
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Alex Motor Imagery dataset. Dataset summary Motor imagery dataset from the PhD dissertation of A. Barachant.
BNCI 2014-001 Motor Imagery dataset Dataset IIa from BCI Competition 4 [1].
Dataset description
Dataset Description
Physionet MI dataset: https://physionet.org/pn4/eegmmidb/ This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers [2]_.
Data Acquisition EEG and NIRS data was collected in an ordinary bright room. EEG data was recorded by a multichannel BrainAmp EEG amplifier with thirty active electrodes (Brain Products GmbH, Gilching, Germany) with linked mastoids reference at 1000 Hz sampling rate. The EEG amplifier was also used to measure the electrooculogram (EOG), electrocardiogram (ECG) and respiration with a piezo based breathing belt. Thirty EEG electrodes were placed on a custom-made stretchy fabric cap (EASYCAP GmbH, Herrsching am Ammersee, Germany) and placed according to the international 10-5 system (AFp1, AFp2, AFF1h, AFF2h, AFF5h, AFF6h, F3, F4, F7, F8, FCC3h, FCC4h, FCC5h, FCC6h, T7, T8, Cz, CCP3h, CCP4h, CCP5h, CCP6h, Pz, P3, P4, P7, P8, PPO1h, PPO2h, POO1, POO2 and Fz for ground electrode).
Data Acquisition
Dataset from the article Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery [1]_.
Dataset from the article A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface [1]_. This dataset contains data recorded on 4 subjects performing 3 type of motor imagery: left hand, right hand and feet.
Dataset Description
Dataset description
Dataset description