45 machine learning datasets
45 dataset results
The SEED dataset contains subjects' EEG signals when they were watching films clips. The film clips are carefully selected so as to induce different types of emotion, which are positive, negative, and neutral ones.
The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature. Corresponding hypnograms (sleep patterns) were manually scored by well-trained technicians according to the Rechtschaffen and Kales manual, and are also available.
We present a database for research on affect, personality traits and mood by means of neuro-physiological signals. Different to other databases, we elicited affect using both short and long videos in two configurations, one with individual viewers and one with groups of viewers. The database allows the multimodal study of the affective responses of individuals in relation to their personality and mood, and the analysis of how these responses are affected by (i) the individual/group configuration, and (ii) the duration of the videos (short vs long). The data is collected in two experimental settings. In the first one, 40 participants watched 16 short emotional videos while they were alone. In the second one, the same participants watched 4 long videos, some of them alone and the rest in groups. In both settings, the participants' signals, namely, Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR), were recorded using wearable sensors. Frontal, full-bod
The Montreal Archive of Sleep Studies (MASS) is an open-access and collaborative database of laboratory-based polysomnography (PSG) recordings O’Reilly, C., et al. (2014) J Seep Res, 23(6):628-635. Its goal is to provide a standard and easily accessible source of data for benchmarking the various systems developed to help the automation of sleep analysis. It also provides a readily available source of data for fast validation of experimental results and for exploratory analyses. Finally, it is a shared resource that can be used to foster large-scale collaborations in sleep studies.
The DEAP dataset consists of two parts:
EEEyeNet is a dataset and benchmark with the goal of advancing research in the intersection of brain activities and eye movements. It consists of simultaneous Electroencephalography (EEG) and Eye-tracking (ET) recordings from 356 different subjects collected from three different experimental paradigms.
The CHB-MIT dataset is a dataset of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure mediation in order to characterize their seizures and assess their candidacy for surgical intervention. The dataset contains 23 patients divided among 24 cases (a patient has 2 recordings, 1.5 years apart). The dataset consists of 969 Hours of scalp EEG recordings with 173 seizures. There exist various types of seizures in the dataset (clonic, atonic, tonic). The diversity of patients (Male, Female, 10-22 years old) and different types of seizures contained in the datasets are ideal for assessing the performance of automatic seizure detection methods in realistic settings.
The dataset contains a collection of physiological signals (EEG, GSR, PPG) obtained from an experiment of the auditory attention on natural speech. Ethical Approval was acquired for the experiment. Details of the experiment can be found here https://phyaat.github.io/experiment
The eSports Sensors dataset contains sensor data collected from 10 players in 22 matches in League of Legends. The sensor data collected includes:
EEG/fMRI Data from 8 subject doing a simple eyes open/eyes closed task is provided on this webpage.
This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers [2].
High-gamma dataset discribed in Schirrmeister et al. 2017
The SEED-VIG dataset is composed of four parts. EEG features include: EEG_Feature_2Hz: EEG features (power spectral density: PSD, differential entropy: DE) from the total frequency band (1~50 Hz) with a 2 Hz frequency resolution. The fields "psd_movingAve", "psd_LDS", "de_movingAve", and "de_LDS" indicate PSD with a moving average, PSD with a linear dynamic system, DE with a moving average, and DE with a linear dynamic system, respectively. The data format is channelsample_numberfrequency_bands (1788525). The first 1–5 in the first dimension 'channel' correspond to temporal brain areas, and the last 7–17 correspond to posterior brain areas. EEG_Feature_5Bands: This part is similar to the EEG_feature_2Hz file except that EEG features (PSD, DE) are extracted from five frequency bands: delta (1~4 Hz), theta (4~8 Hz), alpha (8~14 Hz), beta (14~31 Hz), and gamma (31~50 Hz). The data format is channelsample numberfrequency bands (178855). Forehead EEG feature files have a similar architectur
Maintenance of Wakefulness Test (MWT) is a dataset of recordings with microsleep episodes and drowsiness.
The provided dataset consists of high-quality realistic head models and combined EEG/MEG data which can be used for state-of-the-art methods in brain research, such as modern finite element methods (FEM) to compute the EEG/MEG forward problems using the software toolbox DUNEuro (http://duneuro.org).
A new subset of the popular open source electroencephalogram (EEG) corpus – TUH EEG: - The Temple University Artifact Corpus (TUAR) consists of high yield artifact files annotated using a five-way classification system: 1. Chewing (CHEW): An artifact resulting from the tensing and relaxing of the jaw muscles. 2. Electrode (ELEC): An artifact that encompasses various electrode related phenomena. 3. Eye Movement (EYEM): A spike-like waveform created during patient eye movement. 4. Muscle (MUSC): A common artifact with high frequency, sharp waves corresponding to patient movement. 5. Shiver (SHIV): A specific and sustained sharp wave artifact that occurs when a patient shivers. - EEG artifacts are waveforms that are not of cerebral origin and may have been affected by several external and physiological factors. - These artifacts cause false alarms in seizure prediction machine learning systems. This corpus was developed to support research and evaluation of artifact suppression technology
This dataset consists of raw EEG data from 48 subjects who participated in a multitasking workload experiment utilizing the SIMKAP multitasking test. The subjects’ brain activity at rest was also recorded before the test and is included as well. The Emotiv EPOC device, with sampling frequency of 128Hz and 14 channels was used to obtain the data, with 2.5 minutes of EEG recording for each case. Subjects were also asked to rate their perceived mental workload after each stage on a rating scale of 1 to 9 and the ratings are provided in a separate file.
EEG signals from 60 users have been recorded whose age range lies between 6 and 55 years. Among all, there were 25 females and 35 male users. In general, all the participants were either school children or belonged to the socioeconomic cross section of the population with no medical history. The EEG recordings were acquired from all 14 electrodes operating at a sampling rate of 128 Hz. During recording, the participants were asked to comfortably sit on the chair with clear thoughts and a relaxed state.
MODA is a large open-source dataset of high quality, human-scored sleep spindles (5342 spindles, from 180 subjects) that was produced by the Massive Online Data Annotation project. Sleep spindles were detected as a consensus of a number of human-expert scorers. With a median number of 5 experts scoring every EEG segment, MODA offers sleep spindle annotations of a quality unseen in previous datasets.
This dataset is a BIDS-compatible version of the CHB-MIT Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect: