SPIS Resting State Dataset: 10 subjects, 64 channels, 2.5 minutes recording in each state (eyes-closed and eyes-open) prior to a 105-minute session of Sustained Attention to Response Task with fixed-sequence and varying ISIs. [Article] Alpha-waves 20 subjects, 16 channels, 10s samples of tw...
EEG Alpha Waves Dataset; Centre pour la Communication Scientifique Directe: Grenoble, France, 2019. 29. Katsigiannis, S.; Ramzan, N. DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices. IEEE J. Biomed. Heal. Informatics 2...
In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. ...
These brain cells are characterized by EEG patterns such as alpha, beta, theta, and gamma frequency band waves, and offer valuable insights such as frequency analysis, categorization of valence and arousal, and machine learning techniques, into distinct mental states and neural activity. The ...
Alpha waves (8–12 Hz) are a spontaneous neural signal that can reflect a person’s state of relaxation, which makes them an important spectral feature in ExG-based drowsiness classification15. A sample recording from a single user demonstrating alpha wave modulation is presented in Fig.6a....
(body asleep/mind awake). alpha waves are related to the case of dreaming and relaxation. beta waves are the dominant with the waking state with large attention. gamma waves are highly related to the decision-making mode of the brain. when dealing with mental illnesses states, unexpected ...
model. Using cWGAN, applied to Shallow improved the classification accuracy from 72.97% by 1.65%, while Deep4 improvement was 2.89%. Furthermore, it was clear that whenever the size of generated EEG data is less than the original dataset size, the classification improvement accuracy was more ...
Lawhern V, Kerick S, Robbins KA (2013) Detecting alpha spindle events in EEG time series using adaptive autoregressive models. BMC Neurosci 14(1):1–16 Google Scholar Li W, Duan Y, Yan J, Gao H, Li X (2020) Association between loss of sleep-specific waves and age, sleep efficiency...
The functional data used in this study were acquired from 18 healthy subjects (28.5 ± 3.7 years) through an open dataset67that has been extensively used in various studies related to BCI applications68,69,70, and complex analyses71,72,73. The dataset included synchronous EEG and fNIRS recordin...
Dataset For the present study, we selected 37 Drug-Resistant Epilepsy (DRE) patients (17 females and 20 males, with a mean age of 41.0 ± 16.1 years) from the European Epilepsy Database (EPILEPSIAE)26. The usage of these data for research purposes has been approved by the Ethical Committe...