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Wavelet-based semblance methods to enhance single-trial ERP detection

Carolina Saavedra 1 
1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Brain-Computer Interfaces (BCI) are control and communication systems which were initially developed for people with disabilities. The idea behind BCI is to translate the brain activity into commands for a computer application or other devices, such as a spelling system. The most popular technique to record brain signals is the electroencephalography (EEG), from which Event-Related Potentials (ERPs) can be detected and used in BCI systems. Despite the BCI popularity, it is generally difficult to work with brain signals, because the recordings contains also noise and artifacts, and because the brain components amplitudes are very small compared to the whole ongoing EEG activity. This thesis presents new techniques based on wavelet theory to improve BCI systems using signals' similarity. The first one denoises the signals in the wavelet domain simultaneously. The second one combines the information provided by the signals to localize the ERP in time by removing useless information.
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Submitted on : Wednesday, December 11, 2013 - 5:39:58 PM
Last modification on : Saturday, July 2, 2022 - 3:52:18 AM
Long-term archiving on: : Tuesday, March 11, 2014 - 11:30:21 PM


  • HAL Id : tel-02074919, version 3


Carolina Saavedra. Wavelet-based semblance methods to enhance single-trial ERP detection. Computer science. Université de Lorraine, 2013. English. ⟨NNT : 2013LORR0138⟩. ⟨tel-02074919v3⟩



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