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Feb 4, 2021 · To achieve a better understanding of human mental functions, perplexing signal processing techniques are required to analyze EEG data and extract relevant EEG measures. To facilitate the applications of EEG techniques for psychologists, we introduce useful EEG signal processing techniques with MATLAB scripts (provided in the supplementary ...
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- 1 Linear Regression
- 2 Filtering
- 3 Wavelet Transform and Empirical Mode Decomposition
- 4 Blind Source Separation
- 5 Hybrid Artifact Removal Methods
Assuming that artifact reference channels are available and contain thorough waveforms of artifacts, linear regression has been one of the main vehicles used to cancel artifacts from the EEG signal due to its simplicity and ease-of-use. A basic procedure is to estimate a portion of EEG contaminated by artifacts using regression and to subtract the ...
Filters used for artifact removal build a statistical machine whose parameters are adaptively estimated with certain objectives, learning rules, model structures as well as data. Three types of filters have been primarily adopted for EEG artifact removal [104]. Adaptive filters model the way artifacts contaminate the EEG signal by adjusting the fil...
EEG denoising can be achieved by decomposing a single-channel EEG signal into a set of fundamental basis signals, with a premise that some basis signals may contain the information of artifacts only. As such, we can find those artifact-related basis signals and remove them from the decomposed set. Two representative methods for decomposition of an ...
Blind source separation (BSS) has been most widely used for artifact removal when the information about artifacts is limited—for instance, no reference is provided. The basic BSS methods used for artifact removal assume a linear mixture model in which the observed multi-channel EEG signals are assumed to be a linear mixture of unknown sources with ...
Recent studies have proposed hybrid approaches for EEG artifact removal by combining more than one artifact removal algorithms. Many studies blend one algorithm from the BSS family and the other with decomposition (e.g. wavelet transform or EMD). A hybrid method can be characterized by the order of the applications of the selected algorithms. One g...
- Sung-Phil Kim
- spkim@unist.ac.kr
- 2018
Jul 16, 2023 · We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends.
Nov 9, 2023 · Electroencephalography (EEG) is a widely recognised non-invasive method for capturing brain electrophysiological activity. It stands out for its cost-effectiveness, portability, ease of administration, and widespread availability in most hospital settings.
Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, improve subjective video quality, and to detect or pinpoint components of interest in a measured signal.
The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing.
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Jan 7, 2018 · Even beforehand, around World War II, signal processing began to develop, mostly for military purposes related to sonar and radar. This led to new approaches aimed at solidifying the...
