Selective auditory attention detection based on effective connectivity by single-trial EEG.

Journal of Neural Engineering
Masoud Geravanchizadeh, Sahar Bakhshalipour Gavgani

Abstract

Focusing attention on one speaker in an environment with lots of speakers is one of the important abilities of the human auditory system. The temporal dynamics of the attention process and how the brain precisely performs this task are yet unknown. This paper proposes a new method for the selective auditory attention detection (SAAD) from single-trial EEG signals using the brain effective connectivity and complex network analysis for two groups of listeners attending to the left or right ear. Here, the connectivity matrices of all subjects obtained from the Granger causality method are used to extract different features. Then, by employing the processes of feature selection and optimization, an optimized feature set is determined for the train of a classifier. Among different measures of brain connectivity (i.e. segregation, integration, and centrality), the evaluation results show that the optimized feature set obtained by the combination of the centrality measures contain the most discriminative features for the classification process. The proposed SAAD method as compared with state-of-the-art attention detection approaches from the literature yields the best performance in terms of various measures. The new SAAD approach is ...Continue Reading

References

Aug 31, 2002·Science·E RavaszA L Barabási
Jun 20, 2003·NeuroImage·Barry Horwitz
Jun 17, 2006·Annual Review of Neuroscience·Michael C-K WuJack L Gallant
Feb 7, 2008·IEEE Transactions on Neural Networks·V CherkasskyV N Vapnik
Oct 13, 2009·NeuroImage·Mikail Rubinov, Olaf Sporns
Dec 28, 2010·Neuropsychologia·Adam Gazzaley
Jan 1, 2011·Brain Connectivity·Karl J Friston
Apr 3, 2012·The European Journal of Neuroscience·Alan J PowerEdmund C Lalor
Jul 4, 2012·Proceedings of the National Academy of Sciences of the United States of America·Nai Ding, Jonathan Z Simon
Oct 26, 2012·Computational Intelligence and Neuroscience·E W LangC G Puntonet
Apr 12, 2013·Frontiers in Human Neuroscience·Inyong ChoiBarbara G Shinn-Cunningham
Jan 17, 2014·Cerebral Cortex·James A O'SullivanEdmund C Lalor
Jun 26, 2014·Journal of Neural Engineering·Cort HortonMichael D'Zmura
Dec 3, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Jürgen Schmidhuber
Feb 27, 2015·The Journal of Neuroscience : the Official Journal of the Society for Neuroscience·Anil K SethLionel Barnett
May 29, 2015·Nature·Yann LeCunGeoffrey Hinton
Jun 3, 2015·Journal of Neural Engineering·Bojana MirkovicMaarten De Vos
Jan 19, 2016·Frontiers in Systems Neuroscience·André M Bastos, Jan-Mathijs Schoffelen
Jan 1, 2015·Brain Computer Interfaces·K DijkstraG Schalk
Aug 5, 2017·Journal of Neural Engineering·James O'SullivanNima Mesgarani
May 17, 2018·Frontiers in Neuroscience·Sina MiranBehtash Babadi
May 1, 2019·Brain Topography·Dezhong YaoPedro A Valdés Sosa

❮ Previous
Next ❯

Citations

Dec 1, 2020·Journal of Neural Engineering·Jaakko Johannes SyrjäläVittorio Pizzella
Feb 25, 2021·Journal of Neural Engineering·Jaakko SyrjäläVittorio Pizzella
Jul 31, 2021·Scientific Reports·Masoud Geravanchizadeh, Hossein Roushan

❮ Previous
Next ❯

Related Concepts

Related Feeds

Brain-Computer Interface

A brain-computer interface, also known as a brain-machine interface, is a bi-directional communication pathway between an external device and a wired brain. Here is the latest research on this topic.

Related Papers

Biomedizinische Technik. Biomedical Engineering
Martin BillingerGernot R Müller-Putz
SAAD Digest
R Macintosh
© 2021 Meta ULC. All rights reserved