Co-cultures are a traditional method for studying the cellular properties of cell to cell interactions among different cell types. How network properties in these multicellular synthetic systems vary from monocultures are of particular interest. Understanding the changes in the functional output of these in vitro spiking neural networks can provide new insights into in vivo systems and how to develop biological system models that better reflect physiological conditions - something of paramount importance to the progress of synthetic biology.


The folder name is based on plating date of the culture. If the folder name contains, "myo," then it is a myocyte coculture. If the name contains, "astro," then it is an astrocyte coculture. If it contains neither, "astro," or, "myo," then it is a ventral horn culture. The original raw recording are .modat file type. All other file types are associated with post-processing analysis.


For a detailed describtion of this dataset see accompanying publication "Stand-alone Heartbeat Detection in Multidimensional Mechanocardiograms" by Kaisti M., et al. IEEE Sensors 2018, 10.1109/JSEN.2018.2874706. This datasets consists of 29 mechanocardiogram recordings with ECG reference from healthy subjects in supine position. All data were recorded with sensors attached to the sternum using double-sided tape. Mechanocardigrams incude 3-axis accelorometer signals (seismocardiograms) and 3-axis gyroscope signals (gyrocardiograms).


There are two types of data: raw EEG data recorded from the Brain-Vision system and Mat file converted by BBCI-Tool Box.


Modified characteriograph-assisted testings of spectrozonal analog lab-on-a-chip under laser beams



  • “Development of the novel physical methods for complex biomedical diagnostics based on position-sensitive mapping with the angular resolution at the tissue and cellular levels using analytical labs-on-a-chip” (RFBR grant # 16-32-00914) [6 838,27 $ per year; 2016-2017];
  • “Lab-on-a-chip development for personalized diagnostics” (FASIE grant 0019125) [3 039,00 $ per year; 2016-2017].

Microfluidic Lab-on-a-dish (3D printing).


O.V. Gradov group, INEPCP RAS, 2017-2018.




Orientation tracking of a moving object has a widevariety of applications, including but not limited to military,surgical aid, navigation systems, mobile robots, gaming, virtualreality, and gesture recognition. In this article, a novel algorithmis presented to automatically track and quantify change ofdirection (COD) incident angles or heading angles (i.e. turningangles) of a moving athlete using the inertial sensor signals froma microtechnology unit (an inertia measurement unit (IMU))commonly used in elite sport.


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.


The data for each subject follows the naming convention: subno_task.txt. For example, sub01_lo.txt would be raw EEG data for subject 1 at rest, while sub23_hi.txt would be raw EEG data for subject 23 during the multitasking test. The rows of each datafile corresponds to the samples in the recording and the columns corresponds to the 14 channels of the EEG device: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4, respectively.

The ratings for each subject is given in a separate file ratings.txt. They are given in a comma separated value format: subject number, rating at rest, rating for test. For example: 1, 2, 8 would be subject 1, rating of 2 for “at rest”, rating of 8 for “test”. Note that ratings for subjects 5, 24 and 42 are unavailable.


Electroencephalography (EEG) signal data was collected from twelve healthy subjects with no known musculoskeletal or neurological deficits (mean age 25.5 ± 3.7, 11 male, 1 female, 1 left handed, 11 right handed) using an EGI Geodesics© Hydrocel EEG 64-Channel spongeless sensor net. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the University of Wisconsin-Milwaukee (17.352).


The published sEMG database was captured by the Intelligent System and Biomedical Robotics Group at University of Portsmouth, leaded by Prof. Honghai Liu.


Six subjects were volunteered for data capturing, and the sEMG data were captured in ten separate days. We manually separated the whole database into two parts: training dataset (the first 7 days) and testing dataset(the last 3 days). For each subject, two folders exist, one for training and the other for test. 



Terabytes of brain EEG data are available through open sources, collected from tests associated with human cognitive capability, stroke patient recovery, class learning ability, and other social environments, over a wide range of demographics. Some also play with stimulus such as audio, music, video, lights and digital games. 

Last Updated On: 
Thu, 02/15/2018 - 09:50
Citation Author(s): 
J.A. Anguera, J. Boccanfuso, J.L. Rintoul, O. Al-Hashimi, F. Faraji, J. Janowich, E. Kong, Y.Larraburo, C. Rolle, E. Johnston and A. Gazzaley