… EEG recordings of longer duration. Expert neurologists examine epilepsy by studying
continuous EEG signals … Over the years, various studies have employed machine learning (ML)-…

… articles, various supervised machine learning and deep learning algorithms have been …
machine learning and deep learning algorithms for the neural decoding of EEG signals in …

… along with machine learning algorithms for the classification and the quantitative analysis
of spontaneous EEG signals recorded during complex cognitive task. The EEG signals were …

… by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used
… Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and …

… In this research, EEG signals from the DEAP dataset are windowed into 2- and 4-seconds
windows with 50% overlap, then, decomposed into 5 frequency bands; gamma, beta, alpha, …

… Herein, we focus on studying BrainAGE using EEG signals. Several studies have demonstrated
that EEG features like EEG rhythmic activity (eg, delta, theta, alpha, beta, and gamma) …

… learning algorithms. In this automation, characterizing features of EEG signals are extracted
to train machine learning … of EEG signals for the automatic detection of epileptic seizures. …

… This paper provides a comprehensive overview of Machine Learning applications used in
EEG analysis. It … In this paper, we will be focusing primarily on conventional scalp EEG signals. …

… Machine learning techniques are already well known for the classification of …
electroencephalographic (EEG) signals using Wavelet Packet Decomposition (WPD) and machine …

… on EEG signals. The proposed model focuses on creating an effective method that combines
the basic stages of EEG … based on EEG signals by applying machine learning methods to …