EEG Signal Processing

1,718 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A review of epileptic seizure detection using machine learning classifiers (2020, background): “The nature of EEG and ECoG signals is very complex, non-stationary and time-dependent [105–107].”
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, background): “During the past decades, many machine learning and signal processing methods are proposed to deal with the EEG emotion recognition [20][21].”
Exploring EEG Features in Cross-Subject Emotion Recognition (2018, background): “Researchers have found that human brain manifests many characteristics speciﬁcally belonging to non-linear and chaotic dynamical systems; thus, the EEG signal is inherently complex, non-linear, non-stationary, and random in nature (Stam, 2005; Sanei and Chambers, 2013).”
Real-Time EEG-Based Human Emotion Recognition and Visualization (2010, methodology): “2 to 42 Hz band-pass filter was applied to the raw data as the major EEG waves (alpha, theta, beta, delta, and gamma) lie in this band [31-32].”
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting (2010, methodology): “The range of d(E, c, a) is then matched to the interval [0, 1] as [32]:”