EEG Analysis Based on the Empirical Mode Decomposition for Detection of Mental Activity

2017 ◽  
Author(s):  
Alexander Yu. Tychkov ◽  
Pyotr P. Churakov ◽  
Alan K. Alimuradov ◽  
Anna N. Tychkova ◽  
Alexey V. Ageykin ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Mental Activity ◽  
Eeg Analysis ◽  
Mode Decomposition

Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

2018 ◽  
Vol 10 (02) ◽  
pp. 1840001 ◽  
Author(s):  
Catherine M. Sweeney-Reed ◽  
Slawomir J. Nasuto ◽  
Marcus F. Vieira ◽  
Adriano O. Andrade
Keyword(s):  
Empirical Mode Decomposition ◽  
Data Driven ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Data Driven Approach

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

Improving the performance of empirical mode decomposition via Tsallis entropy: Application to Alzheimer EEG analysis

2018 ◽  
Vol 29 (5) ◽  
pp. 551-566 ◽  
Author(s):  
Prinza Lazar ◽  
Rajeesh Jayapathy ◽  
Jordina Torrents-Barrena ◽  
M. Mary Linda ◽  
Beena Mol ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Tsallis Entropy ◽  
Eeg Analysis ◽  
Mode Decomposition
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