Quantifying evoked responses through information-theoretical measures

Frontiers in Neuroinformatics

Neuroscience

Anaïs Llorens¹  Julian Fuhrer²  Kyrre Glette²  Alejandro Omar Blenkmann³  Tor Endestad⁴  Anne-Kristin Solbakk⁵ 

[1] Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, Berkeley, CA, United States;RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway;Department of Informatics, University of Oslo, Oslo, Norway;RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway;Department of Psychology, University of Oslo, Oslo, Norway;RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway;Department of Psychology, University of Oslo, Oslo, Norway;Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway;RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway;Department of Psychology, University of Oslo, Oslo, Norway;Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway;Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway;

关键词: EEG;    ECoG;    information content;    algorithmic complexity;    frequency tagging;    t;   

DOI  :  10.3389/fninf.2023.1128866

 received in 2022-12-21, accepted in 2023-05-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t-test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t-test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present.

【 授权许可】

Unknown   
Copyright © 2023 Fuhrer, Glette, Llorens, Endestad, Solbakk and Blenkmann.