EEG Signal Analysis: A Survey

612 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.

Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, methodology): “And then we extract 91 features for EEG, EMG and ECG signals [68], [69], [70]; In Mental Fatigue dataset, we preprocess physiological signals by interpolation and filtering algorithm.”
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review (2020, methodology): “2) Analysis Techniques: EEG analysis methods can mainly be classified into the time domain methods, frequency domain methods, time-frequency domain methods, and linear or nonlinear methods [4].”
Altered Resting State Complexity in Schizophrenia (2011, methodology): “…predictability of a single brain signal can be examined using Lyapunov exponents, Hurst exponents, dimensional complexity, and multi-scale entropy (Subha et al., 2010), which vary with age and development (McIntosh et al., 2010; Meyer-Lindenberg and Bassett, 2008), task (Barnes et al., 2009;…”
Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review (2024, background): “Linear spectral analysis, which explains a single EEG signal as a combination of several different oscillatory components, uses physical properties, such as the amplitude or phase of each oscillatory component [23,24].”