Empirical mode decomposition of EEG signals for brain computer interface

2017 ◽  
Author(s):  
MD Erfanul Alam ◽  
Biswanath Samanta
Keyword(s):  
Empirical Mode Decomposition ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Mode Decomposition

scholarly journals Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain–computer interface applications

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150199 ◽  
Author(s):  
Apit Hemakom ◽  
Valentin Goverdovsky ◽  
David Looney ◽  
Danilo P. Mandic
Keyword(s):  
Empirical Mode Decomposition ◽  
Information Transfer ◽  
Multiscale Analysis ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Analysis Framework ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Information Transfer Rate ◽  
Multichannel Data

An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain–computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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