This dataset contains light-field microscopy images and converted sub-aperture images. 

 

The folder with the name "Light-fieldMicroscopeData" contains raw light-field data. The file LFM_Calibrated_frame0-9.tif contains 9 frames of raw light-field microscopy images which has been calibrated. Each frame corresponds to a specific depth. The 9 frames cover a depth range from 0 um to 32 um with step size 4 um. Files with name LFM_Calibrated_frame?.png are the png version for each frame.

 

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This databases includes brain tumour images of both malignant and benign type. 

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BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019). 

Instructions: 

There are two folders under each user; session I and sessions II. Each session folder contains four different folders; one for each activity performed by the user. Each activity folder contains .csv files; (1) EEG Data (brainwave.csv), (2) Handmovement Accelerometer Data (accelerometer.csv), and (3) Handmovement Gyroscope Data (gyroscope.csv).

A more deatailed description of the data is given in BS-HMS-Dataset-Documentation.pdf file.

Acknowledgement: This data collection was supported in part by the National Science Foundation (NSF) under grant SaTC-1527795.

Please cite: [1] Diksha Shukla, Sicong Chen, Yao Lu, Partha Pratim Kundu, Ravichandra Malapati, Sujit Poudel, Zhanpeng Jin, Vir Phoha, "Brain Signals and the Corresponding Hand Movement Signals Dataset (BS-HMS-Dataset)", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/my1k-dd23. Accessed: Dec. 05, 2019.

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Complex networks have been successfully applied to sleep stage analysis and classification. However, whether the electroencephalogram (EEG) montage reference will affect the network properties is still unclear.

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The dataset consists of EEG recordings obtained when subjects are listening to different utterances : a, i, u, bed, please, sad. A limited number of EEG recordings where also obtained when the three vowels were corrupted by white and babble noise at an SNR of 0dB. Recordings were performed on 8 healthy subjects.

Instructions: 

Recordings were performed at the Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS), Sherbrooke (Quebec), Canada. The EEG recordings were performed using an actiCAP active electrode system Version I and II (Brain Products GmbH, Germany) that includes 64 Ag/AgCl electrodes. The signal was amplified with BrainAmp MR amplifiers and recorded using the Vision Recorder software. The electrodes were positioned using a standard 10-20 layout. Experiments were performed on 8 healthy subjects without any declared hearing impairment. Each session lasted approximately 90 minutes and was separated in 2 parts. The first part, lasting 30 minutes, consisted in installing the cap on the subject where an electroconductive gel was placed under each electrode to ensure a proper contact between the electrode and the scalp. The second part, which was the listening and EEG acquisition, lasted approximately 60 minutes. The subjects then had to stay still with eyes closed while avoiding any facial movement or swallowing. They had to remain concentrated on the audio signals during the full length of the experiment. Audio signals were presented to the subjects through earphones while EEGs were recorded. During the experiment, each trial was repeated randomly at least 80 times. A stimulus was presented randomly within each trial which lasted approximately 9 seconds. A 2-minute pause was given after 5 minutes of trials where the subjects could relax and stretch. Once the EEG signals were acquired, they were resampled at 500 Hz and band-pass filtered between 0.1 Hz and 45 Hz in order to extract the frequency bands of interest for this study. EEG signals were then separated into 2-second intervals where the stimulus was presented at 0.5 second within each interval. If the signal amplitude exceeded a pre-defined 75 V limit, the trial was marked for rejection. A sample code is provided to read the dataset and generate ERPs. One needs first to run the epoch_data.m for the specific subject and then run the mean_data.m file in the ERP folder. EEGLab for Matlab is required.

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The EEG data were acquired from 16 healthy young adults (age range 22 - 30 years) with no neurological, physical, or psychiatric illness history. All the participants were naive BCI users who had not participated in any related experiments before. Informed consent was received from all participants.   

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This material is associated with the PhD Thesis of Javier Olias (which is supervised by Sergio Cruces) and the article:
EEG Signal Processing in MI-BCI Applications with Improved Covariance Matrix Estimators
by J.Olias, R. Martin-Clemente, M.A. Sarmiento-Vega and S. Cruces,
which was accepted in 2019 by IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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Our state of arousal can significantly affect our ability to make optimal decisions, judgments, and actions in real-world dynamic environments. The Yerkes-Dodson law, which posits an inverse-U relationship between arousal and task performance, suggests that there is a state of arousal that is optimal for behavioral performance in a given task. Here we show that we can use on-line neurofeedback to shift an individual's arousal from the right side of the Yerkes-Dodson curve to the left toward a state of improved performance.

Instructions: 

We hope you find this dataset useful. For help please see the provided readme file, the article by Faller et al. (2019) in PNAS, the preprint by Faller et al. (2018) on BioRxiv, the conference paper by Faller et al. (2016) at IEEE SMC and/or the tutorials for the Matlab toolboxes EEGLAB and BCILAB. Thank you very much.

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The proposed signals are used  for electromagnetic-based stroke classification.  Six realistic head phantom computed from MRI scans, is surrounded by an antenna array of 16 dipole antennas distributed uniformly around the head. These antennas are deployed in a fixed circular array around the head, at a distance of approximately 2-3 mm from the head. A Gaussian pulse covering the bandwidth from 0:7 to 2 GHz is emitted from each of the antennas, sequentially, while all of the antennas capture the scattered signals. Since 16 antennas were used, there are a total of 256 channel signals (i.e.

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