… , imagined speech and AI) and target data (EEG signals), and some of the search terms …
EEG signal devices to be the most widely used in the included studies. The most common signal …

… detect normal, pre-ictal, and ictal conditions from EEG signals. 2015 and 2017 witnessed
the publication of two impactful AI-enhanced EEG studies. Specifically, studies [50, 51] were …

… Abstract: Correctly interpreting an electroencephalogram signal with high accuracy is a …
(CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning …

… ML technologies to EEG signals, we require to preprocess the EEG signals and then extract
… the preprocessed EEG data. Fig. 3 shows a genetic pipeline of data processing in the EEG‐…

… EEG signals from the 19 sensors (10-20 system) … signals were converted into NumPy arrays.
The model was configured to access 19 channels of EEG signals, 1 channel of ECG signal …

… in Artificial Intelligence (AI)-powered research. In this article, we presented a systematic review
of automated emotion recognition from EEG signals … After that EEG databases, and EEG …

… The literature presents a certain variety of artificial intelligence methods applied to EEG …
Among the previous applications of neural networks to EEG signal processing we find, in fact, …

… convincing for solving complex problems, through artificial intelligence. In particular, this work,
focused on the development of an artificial neural network for identifying diseases: … signals …

… working with EEG signals is that it has a low signal-to-noise ratio since EEG signals can …
impurities from EEG signals and get the filtered signals. Another most important limitation of …

… -signals for the early diagnosis of PD (Parkinson’s disease). EEG (Electroencephalography)
and EMG have been used to examine human brain and muscle signals to learn more about …