Neuroscience
Brainwave entrainment beats detection has become an important topic due to the ability of these beats to change human brain waves to decrease anxiety, help focus attention, improve memory, improve mood, enhance creativity, reduce pain, help with meditation, enhance mental flexibility, and enhance sleep quality. However, listening to it can cause unwanted side effects as it can increase feelings of depression, anxiety, anger, and confusion in some people.
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Brainwave entrainment beats detection has become an important topic due to the ability of these beats to change human brain waves to decrease anxiety, help focus attention, improve memory, improve mood, enhance creativity, reduce pain, help with meditation, enhance mental flexibility, and enhance sleep quality. However, listening to it can cause unwanted side effects as it can increase feelings of depression, anxiety, anger, and confusion in some people.
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Please cite the following paper when using this dataset:
N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007
Abstract
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This dataset is in support of my Research paper 'Detection of Pancreatic,Ovarian & Prostate Tumor, Cancer and Treatment by Ablation'.Due to computer crash, all work, datasets and old papers lost. Re-work may be submitted.
For Machine design, pls refer, open-access page 'Data and Designs of B-Machines'
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This dataset consists of EEG data of 40 epileptic seizure patients (both male and female) of age from 4 to 80 years. The raw data was collected from Allengers VIRGO EEG machine at Medisys Hospitals, Hyderabad, India. The EEG electrodes were placed according to 10 – 20 International standard. The EEG data was recorded from 16 channels (FP2-F4, F4-C4, C4-P4, P4-O2, FP1-F3, F3-C3, C3-P3, P3-O1, FP2-F8, F8-T4, T4-T6, T6-O2, FP1-F7, F7-T3, T3-T5, and T5-O1) at 256 samples per second.
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Recent advances in computational power availibility and cloud computing has prompted extensive research in epileptic seizure detection and prediction. EEG (electroencephalogram) datasets from ‘Dept. of Epileptology, Univ. of Bonn’ and ‘CHB-MIT Scalp EEG Database’ are publically available datasets which are the most sought after amongst researchers. Bonn dataset is very small compared to CHB-MIT. But still researchers prefer Bonn as it is in simple '.txt' format. The dataset being published here is a preprocessed form of CHB-MIT. The dataset is available in '.csv' format.
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Dataset asscociated with a paper in Computer Vision and Pattern Recognition (CVPR)
"Object classification from randomized EEG trials"
If you use this code or data, please cite the above paper.
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