Biophysiological Signals

Music and animal's basic emotions associated with acoustic signals.
Files associated with animals’ sounds mainly were based on the records from Volodins Bioacoustic Group Homepage
http://www.bioacoustica.org/index_eng.html
http://www.bioacoustica.org/gallery/gallery_rus.html
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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.
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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.
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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.
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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.
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Dataset description
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.
Original article:
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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.
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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.
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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).
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