Identifying multiscale hidden states to decode behavior

Hamidreza Abbaspourazad, Yan Wong, Bijan Pesaran, Maryam M. Shanechi

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

2 Citations (Scopus)

Abstract

A key element needed in a brain-machine interface (BMI) decoder is the encoding model, which relates the neural activity to intended movement. The vast majority of work have used a representational encoding model, which assumes movement parameters are directly encoded in neural activity. Recent work have in turn suggested the existence of neural dynamics that represent behavior. This recent evidence motivates developing dynamical encoding models with hidden states that encode movement. Regardless of their type, encoding models have vastly characterized a single scale of activity, e.g., either spikes or local field potentials (LFP). In our recent work we developed a multiscale representational encoding model to simultaneously characterize and decode discrete spikes and continuous field activity. However, learning a multiscale dynamical model from simultaneous spike-field recordings in the presence of hidden states is challenging. Here we present an unsupervised learning algorithm for estimating a multiscale state-space model with hidden states and validate it using spike-LFP activity during a reaching movement. We use the learned multiscale statespace model and a corresponding decoder to identify hidden states from spike-LFP activity. We then decode the movement trajectories using these hidden states. We find that the identified states can accurately decode the trajectories. Moreover, we demonstrate that adding LFP to spikes improves the decoding accuracy, suggesting that our unsupervised learning algorithm incorporates information across scales. This learning algorithm could serve as a new tool to study encoding across scales and to enhance future BMI systems.

Original languageEnglish
Title of host publication2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2018)
EditorsGregg Suaning, Olaf Doessel
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages3778-3781
Number of pages4
ISBN (Electronic)9781538636466, 9781538636459
ISBN (Print)9781538636473
DOIs
Publication statusPublished - 2018
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society 2018 - Honolulu, United States of America
Duration: 17 Jul 201821 Jul 2018
Conference number: 40th
https://embc.embs.org/2018/
https://ieeexplore.ieee.org/xpl/conhome/8471725/proceeding (Proceedings)

Conference

ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society 2018
Abbreviated titleEMBC 2018
CountryUnited States of America
CityHonolulu
Period17/07/1821/07/18
Internet address

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