Ten volunteers were trained through a series of twelve daily lessons to type in a computer using the Colemak keyboard layout. During the fourth-, eight-, and eleventh-session, electroencephalography (EEG) measurements were acquired for the five trials each subject performed in the corresponding lesson. Electrocardiography (ECG) data at each of those trials were acquired as well. The purpose of this experiment is to aim in the development of different methods to assess the process of learning a new task.


*Experimental setup

Ten volunteers were trained through a series of twelve daily lessons to type in a computer using the Colemak keyboard layout, which is an alternative to the QWERTY and Dvorak layouts, and it is designed for efficient and ergonomic touch typing in English. Six of our volunteers were female, four male, all of them were right-handed, and their mean age was 29.3 years old with an standard deviation of 5.7 years. The lessons used during our experiment are available on-line at colemak.com/Typing_lessons. In our case, we asked the volunteers to repeat each of them five times (with resting intervals of 2 min in between). We
chose Colemak touch typing as the ability to learn because most people are unaware of its existence, then it is a good candidate for a truly new ability to learn. The training process always took place in a sound-proof cubicle in which the volunteers were isolated from distractions. Hence, the volunteers were sitting in front of the computer and were engaged entirely in the typing lesson. All the experiments were carried at the same hour of the day, and all volunteers were asked to refrain of doing any additional training anywhere else. For more details, see [1].

*Data arrangement

A Matlab-compatible file is provided for each subject. Each .mat file contains a cell array (named Cn) of size 15x10, which corresponds to the 15 trials and 10 channels, respectively. Trials are organized as follows: rows 1-5 correspond to the measurements during the fourth Colemak lesson, rows 6-10 during the eighth, and rows 11-15 during the eleventh. Channels are organized by columns in the following order: (1) ECG, (2) F3, (3) Fz, (4) F4, (5) C3, (6) Cz, (7) C4, (8) P3, (9) POz, and (10) P4. Each of the elements of Cn correspond to a vector containing the output (time samples acquired at 256 Hz sampling frequency) of each of those channels. The lenght of each of those vectors differ between subjects, as well as for each trial depending on the time it took the corresponging subject to complete the Colemak lesson. The units of all output signals are microVolts.


All data has been preprocessed with the automatic decontamination algorithms provided by the B-Alert Live Software (BLS): raw signals are processed to eliminate known artifacts. Particularly, the following actions are taken for different type of artifacts:

• Excursions and amplifier saturation – contaminated periods are replaced with zero values, starting and ending at zero crossing before and after each event.
• Spikes caused by artifact are identified and signal value is interpolated.
• Eye Blinks (EOG) – wavelet transforms deconstruct the signal and a regression equation is used to identify the EEG regions contaminated with eye blinks. Representative EEG preceding the eye blink is inserted in the contaminated region.

Aditionally, all data were detrended using Matlab's command detrend.

*How to acknowledge

We encourage researchers to use the published dataset freely and we ask that they cite the respective data sources as well as this paper:

[1] D. Gutiérrez y M. A. Ramírez-Moreno, “Assessing a Learning Process with Functional ANOVA Estimators of EEG Power Spectral Densities,” Cognitive Neurodynamics, vol. 10, no. 2, pp. 175-183, 2016. DOI: 10.1007/s11571-015-9368-7


All data were acquired in the Laboratory of Biomedical Signal Processing, Cinvestav Monterrey, in the context of M. A. Ramírez-Moreno's MSc thesis work under the advice of D. Gutiérrez.


This Dataset contains EEG recordings from epileptic rats. The genetic absence epilepsy rats (GAERS) are one of the best-established rodent models for generalized epilepsy. The rats show seizures with characteristic "spike and wave discharge" EEG patterns. Experiments were performed in accordance with the German law on animal protection and were approved by the Animal Care and Ethics Committee of the University of Kiel.

  • Sample Frequency: 1600
  • Day1 (18:23:57-16:35:56): Three animals (R1, R2, R3): Array (data points x channels (3))
  • Day2 (16:42:53-16:52:06): Three animals (R1, R2, R3): Array (data points x channels (3))
  • Day3 (17:32:19-10:25:19): Three animals (R1, R2, R3): Array (data points x channels (3))
  • Day4 (10:26:40-14:46:13): Two animals (R1, R3): Array (data points x channels (3))

This data set includes continuous signals and isolated saccades recorded in an EOG experimentation. The horizontal saccades are recorded using an EOG signal acquisition setup implemented using an OpenBCI Cyton board. 

The signals are processed using finite impulse response filters and Kalman filters. The results are provided in the excel file attached to the data set.  


EEG signals of various subjects in text files are uploaded. It can be useful for various EEG signal processing algorithms- filtering, linear prediction, abnormality detection, PCA, ICA etc.


This dataset contains EEG signals from 73 subjects (42 healthy; 31 disabled) using an ERP-based speller to control different brain-computer interface (BCI) applications. The demographics of the dataset can be found in info.txt. Additionally, you will find the results of the original study broken down by subject, the code to build the deep-learning models used in [1] (i.e., EEG-Inception, EEGNet, DeepConvNet, CNN-BLSTM) and a script to load the dataset.


This dataset contains cardiovascular data recorded during progressive exsanguination in a porcine model of hemorrhage. Both wearable and catheter-based sensors were used to capture cardiovascular function; the wearable system contained a fusion of ECG, SCG, and PPG sensors while the catheter-based system was comprised of pressure catheters in the aortic arch, femoral artery, and right and left atria via a Swan-Ganz catheter.


Experimental Protocol

This protocol included 6 Yorkshire swine (3 castrated male, 3 female, Age: 114–-150 days, Weight: 51.5-–71.4 kg), each of which passed a health assessment examination but were not subject to other exclusion criteria. Anesthesia was induced in the animal with xylazine and telazol and maintained with inhaled isoflurane during mechanical ventilation. Intravenous heparin was administered as needed to prevent coagulation of blood during the protocol. Before the induction of hypovolemia, a blood sample was taken to assess baseline plasma absorption. Following this baseline sample, Evans Blue dye was administered for blood volume estimation. After waiting several minutes to allow for even distribution of the dye, a second blood sample was taken to measure plasma volume. In this method, plasma volume is used along with hematocrit to estimate total blood volume. For one animal in the protocol (Pig 4), atropine was administered to raise the starting heart rate and blood pressure due to critically low values.

Hypovolemia was induced by draining blood through an arterial line at four levels of blood volume loss (7%, 14%, 21%, and 28%) as determined by the estimated total blood volume from the Evans Blue dye protocol. After draining passively through the arterial line, the blood was stored in a sterile container. Following each level of blood loss, exsanguination was paused for approximately 5-10 minutes to allow the cardiovascular system to stabilize. If cardiovascular collapse occurred once a level was reached, as defined by a 20% drop in mean aortic pressure from baseline after stabilization, exsanguination was terminated. Note that cardiovascular collapse was reached at different blood volume levels for each animal: Pigs 1, 3, and 4 reached 21% blood volume loss; Pigs 2 and 6 reached 28% blood volume loss; and Pig 5 reached 14% blood volume loss before the experimental protocol was terminated.


Signals from wearable sensors were continuously recorded using a BIOPAC MP160 data acquisition system (BIOPAC Systems, Inc., Goleta, California, USA) with a sampling frequency of 2 kHz. Electrocardiogram (ECG) signals were captured using a three-lead system of adhesive-backed Ag/AgCl electrodes placed in Einthoven Lead II configuration, which interfaced with a BIOPAC ECG100C amplifier. Reflectance-mode photoplethysmogram (PPG) was captured with a BIOPAC TSD270A transreflectance transducer, which interfaced with a BIOPAC OXY200 veterinary pulse oximeter. The transducer was placed over the femoral artery on either the right or left caudal limb, contralateral to inducer placement. Seismocardiogram (SCG) signals were captured using an ADXL354 accelerometer (Analog Devices, Inc., Norwood, Massachusetts, USA) placed on the mid-sternum, interfacing with a BIOPAC HLT100C transducer interface module.

Aortic root pressure was captured by inserting a fluid-filled catheter through a vascular introducer in the right carotid artery, fed through to the aortic root. Femoral artery pressure was obtained directly from an introducer placed on either the left or right femoral artery depending on accessibility. Right and left atrial pressures were captured with a Swan-Ganz catheter with proximal and distal monitoring ports inserted in either the right or left femoral vein. Left atrial pressure was inferred via PCWP captured using an Edwards 131F7 Swan-Ganz catheter (Edwards Lifesciences Corp, Irvine, California, USA). The vascular introducers were connected via pressure monitoring lines to ADInstruments MLT0670 pressure transducers (ADInstruments Inc., Colorado Springs, Colorado, USA). Data from the catheters were continuously recorded with an ADInstruments Powerlab 8/35 acquisition system sampling at 2 kHz.


Signal Pre-Processing

All signals were filtered with finite impulse response band-pass filters with Kaiser window, both in the forward and reverse directions to offset phase shift. Cutoff frequencies were 0.5–-40Hz for ECG and 1-40Hz for SCG. Only the dorso-ventral component of the SCG acceleration signal was used in this study. PPG signals, along with all four catheter-based pressure signals, were filtered with cutoffs at 0.5-10Hz. After filtering, data from all signals were heartbeat-separated using ECG R-peaks. The signal segments were then abbreviated to a length of 1,000 samples (500 ms) to enable more uniform analysis; however, due to the long left ventricular ejection time of Pig 3, a length of 1,500 samples (750 ms) is provided for this subject.


Using the Dataset

This dataset contains a separate .mat file for each of the 6 animal subjects in the protocol. The variables "scg" and "ppg" contain R-peak-separated signals from the SCG and PPG respectively during the protocol. The variables "aortic", "femoral", "rightAtrium", and "wedge" contain the R-peak-separated pressure waveforms from the catheters placed in the aortic root, femoral artery, right atrium, and left atrium (wedge pressure) respectivley. Each of these variables is a struct, with each of its fields representing a different level of blood volume loss. The field "B1" corresponds to the baseline level (pre-exsanguination); "L1", "L2", "L3", and "L4" correspond to the 7%, 14%, 21%, and 28% drop in blood volume respectively. Thus, the data in each field represents the heartbeat-separated signals collected during each blood volume level. The data has been selected such that periods of active draining of blood have been removed, such that the provided data reflects the heartbeat-separated signals during the resting period between blood-draws. The data is formatted in columnwise matrices, with the columns arranged in sequention order such that the first column is the first heartbeat and the last row is the last heartbeat.


The indices of ECG R-peaks are provided as a vector as well during each blood volume level, such that each element in the vector corresponds to its respective column in the provided column matrices. The unit of these values is in miliseconds, staring from t = 0 (onset of baseline recording).


Data are collected before and after percutaneous transluminal angiography (PTA) for dialysis patients.

Each sample is labeled as a-b-before.wav or a-b-after.wav and the associated txt, where a is the patient id and b is the location id.

The first position was the arterial-venous junction,  and the second point was 3 cm from the first position along the vein.

 The distances between the adjacent positions were also about 3 cm.



Each voice sample is stored as a .WAV file, which is then pre-processed for acoustic analysis using the specan function from the WarbleR R package. Specan measures 22 acoustic parameters on acoustic signals for which the start and end times are provided.

The output from the pre-processed WAV files were saved into a CSV file, containing 3168 rows and 21 columns (20 columns for each feature and one label column for the classification of male or female).


[17-APR-2020: WE ARE STILL UPLOADING THE DATASET, PLEASE WAIT UNTIL IT IS COMPLETED] -The dataset comprises a set of 11 different actions performed by 17 subjects that is created for multimodal fall detection. Five types of falls and six daily activities were considered in the experiment. Data collection comes from five wearable sensors, one brainwave helmet sensor, six infrared sensors around the room and two RGB-cameras. Three attempts per action were recorded. The dataset contains raw signals as well as three windowing-based feature sets.


We will upload the instructions in the following days.


This dataset is a collection of brainwave EEG signals from eight subjects. The data is collected in a lab controlled environment under a specific visualization experiment.