scholarly journals A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain–Computer Interfaces

2014 ◽  
pp. 133-161 ◽  
Author(s):  
Fabien Lotte
Keyword(s):  
Signal Processing ◽  
Mental State ◽  
Eeg Signal ◽  
Brain Computer Interfaces ◽  
State Recognition ◽  
Computer Interfaces ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

scholarly journals Demystifying signal processing techniques to extract resting-state EEG features for psychologists

2020 ◽  
Vol 6 (3) ◽  
pp. 189-209 ◽  
Author(s):  
Zhenjiang Li ◽  
Libo Zhang ◽  
Fengrui Zhang ◽  
Ruolei Gu ◽  
Weiwei Peng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Resting State ◽  
Neural Dynamics ◽  
Complex Signal ◽  
Eeg Signal ◽  
Microstate Analysis ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. 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. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

scholarly journals Analysis of EEG Signal Processing Techniques based on Spectrograms

2017 ◽  
Vol 145 (1) ◽  
pp. 151-162 ◽  
Author(s):  
Ricardo Ramos ◽  
José Arturo Olvera ◽  
Ivan Olmos
Keyword(s):  
Signal Processing ◽  
Eeg Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

scholarly journals EEG Signal Processing for Brain–Computer Interfaces

2014 ◽  
pp. 797-812 ◽  
Author(s):  
Petia Georgieva ◽  
Filipe Silva ◽  
Mariofanna Milanova ◽  
Nikola Kasabov
Keyword(s):  
Signal Processing ◽  
Eeg Signal ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Eeg Signal Processing

scholarly journals EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1423 ◽  
Author(s):  
Natasha Padfield ◽  
Jaime Zabalza ◽  
Huimin Zhao ◽  
Valentin Masero ◽  
Jinchang Ren
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Motor Imagery ◽  
State Of The Art ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Processing Techniques ◽  
Imagery Techniques ◽  
Signal Processing Techniques

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

scholarly journals EEG Calmness Index Establishment Using Computational of Z-Score

2018 ◽  
Vol 7 (4.11) ◽  
pp. 44
Author(s):  
S. A. M. Aris1 ◽  
N. A. Bani ◽  
M. N.Muhtazaruddin ◽  
M. N. Taib
Keyword(s):  
Signal Processing ◽  
Asymmetry Index ◽  
Eeg Signal ◽  
Z Score ◽  
Fuzzy C Means ◽  
Processing Techniques ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Brain Condition

A lot of useful information can be obtained through observation of the electroencephalogram (EEG) signal such as the human psychophysiology. It has been proven that EEG is handy in human diagnosis and tools to observe the brain condition. The study aims to establish a calmness index, which can differentiate the calmness level of an individual. Alpha waves were selected as the data features and computed into asymmetry index. The data features were clustered using Fuzzy C-Means (FCM) and resulted in three clusters. Wilcoxon Signed Ranks test was applied to determine the significance of the data features clustered by FCM. The Z-score obtained successfully distinguish three level of calmness index from the lower index until the higher index. With the advancement of signal processing techniques, the feature extractions for calmness index establishment computation is achievable.  

scholarly journals A Review of Processing Methods and Classification Algorithm for EEG Signal

2020 ◽  
Vol 13 (3) ◽  
pp. 23-29
Author(s):  
Yu Xie ◽  
Stefan Oniga
Keyword(s):  
Feature Extraction ◽  
Classification Algorithm ◽  
Eeg Signal ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Research Directions ◽  
Processing Methods ◽  
Computer Interfaces ◽  
Analysis Technique ◽  
Processing Techniques

AbstractThe analysis technique of EEG signals is developing promptly with the evolution of Brain Computer- Interfaces science. The processing and classification algorithm of EEG signals includes three states: pre-processing, feature extraction and classification. The article discusses both conventional and recent processing techniques of EEG signals at the phases of preprocessing, feature extraction and classification. Finally, analyze popular research directions in the future.

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