Biophysiological Signals

Orientation tracking of a moving object has a wide
variety of applications, including but not limited to military,
surgical aid, navigation systems, mobile robots, gaming, virtual
reality, and gesture recognition. In this article, a novel algorithm
is presented to automatically track and quantify change of
direction (COD) incident angles or heading angles (i.e. turning
angles) of a moving athlete using the inertial sensor signals from
a microtechnology unit (an inertia measurement unit (IMU))

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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.

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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).

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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. 

 

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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

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: 
Wed, 10/11/2017 - 15:44
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

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: 
Fri, 04/20/2018 - 15:35
Citation Author(s): 
To be provided

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