Exploratory analysis of brain signals through low dimensional embedding

Yuxiao Wang, Chee-Ming Ting, Xu Gao, Hernando Ombao

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

2 Citations (Scopus)


In this paper, we develop computationally efficient and theoretically justified tools for analyzing high dimensional brain signals. Our approach is to extract the optimal lower dimensional representations for each brain region and then characterize and estimate connectivity between regions through these factors. This approach is motivated by our observation that electroencephalograms (EEGs) from many channels within each region exhibit a high degree of multicollinearity and synchrony thereby suggesting that it would be sensible to extract summary factors for each region. Here, the summary factors are the encodings that lead to the lowest reconstruction error. We focus on two special cases of linear auto encoder and decoder. The first characterizes the factors as instantaneous linear mixing of the observed signals. In the second approach, the factors are convolutions of the observed signals (which is more general than the first). These methods were compared through simulations under different conditions and the results provide insights on advantages and limitations of each. Finally, we performed exploratory analysis of resting state EEG data. The spectral properties of the factors were estimated and connectivity between regions via the factors using coherence measures were computed. We implemented these methods in a Matlab toolbox XHiDiTS (https://goo.gl/uXc8ei). The toolbox was utilized to investigate consistency of these factors across all epochs during the entire resting-state period.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering
EditorsMichel Maharbiz, Cynthia Chestek
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538679210
ISBN (Print)9781538679227
Publication statusPublished - 2019
Externally publishedYes
EventInternational IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER) 2019 - San Francisco, United States of America
Duration: 20 Mar 201923 Mar 2019
Conference number: 9th

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554


ConferenceInternational IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER) 2019
Abbreviated titleNER 2019
Country/TerritoryUnited States of America
CitySan Francisco
Internet address

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