Signal modality characterisation of EEG with response to steady-state auditory and visual BCI paradigms

Mo Chen, Danilo P. Mandic, Tomasz M. Rutkowski, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Novel nonlinear dynamical analysis of the electroencephalogram (EEG) data recorded in steady state brain stimulation paradigms is provided. This is achieved based on some recent developments in the local predictability in phase space, which allows for the assessment of the degree of nonlineariry and uncertainty within the EEG data. Both the responses from the visual and auditory experiments are addressed, based on the auditory steady-state responses (ASSR) and steady-state visual evoked potentials (SSVEP). Simulation results show clear difference in the degree of nonlineariry and uncertainty between the segments of EEG data recorded before, during and after the stimulus. This provides a novel insight into the dynamics of the brain information processing mechanism captured in EEG.

Original languageEnglish
Title of host publicationMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
Pages223-228
Number of pages6
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, Greece
Duration: 27 Aug 200729 Aug 2007

Publication series

NameMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

Conference

Conference17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
Country/TerritoryGreece
CityThessaloniki
Period27/08/0729/08/07

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