Abstract:
OBJECTIVE:Electrocorticography (ECoG) based studies generally analyze features from specific frequency bands selected by manual evaluation of spectral power. However, the definition of these features can vary across subjects, cortical areas, tasks and across time for a given subject. We propose an autoencoder based approach for summarizing ECoG data with 'template spectrograms', i.e. informative time-frequency (t-f) patterns, and demonstrate their efficacy in two contexts: brain-computer interfaces (BCIs) and functional brain mapping. APPROACH:We use a publicly available dataset wherein subjects perform a finger flexion task in response to a visual cue. We train autoencoders to learn t-f patterns and use them in a deep neural network to decode finger flexions. Additionally, we propose and evaluate an unsupervised method for clustering electrode channels based on their aggregated activity. MAIN RESULTS:We show that the learnt t-f patterns can be used to classify individual finger movements with consisentently higher accuracy than with traditional spectral features. Furthermore, electrodes within automatically generated clusters tend to demonstrate functionally similar activity. SIGNIFICANCE:With increasing interest in and active development towards higher spatial resolution ECoG, along with the availability of large scale datasets from epilepsy monitoring units, there is an opportunity to develop automated and scalable unsupervised methods to learn effective summaries of spatial, temporal and frequency patterns in these data. The proposed methods reduce the effort required by neural engineers to develop effective features for BCI decoders. The clustering approach has applications in functional mapping studies for identifying brain regions associated with behavioral changes.
journal_name
J Neural Engjournal_title
Journal of neural engineeringauthors
Pailla T,Miller KJ,Gilja Vdoi
10.1088/1741-2552/aaf13fsubject
Has Abstractpub_date
2019-02-01 00:00:00pages
016025issue
1eissn
1741-2560issn
1741-2552journal_volume
16pub_type
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journal_title:Journal of neural engineering
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pub_type: 杂志文章
doi:10.1088/1741-2552/aacdbb
更新日期:2018-10-01 00:00:00
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journal_title:Journal of neural engineering
pub_type: 杂志文章
doi:10.1088/1741-2560/6/4/046004
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pub_type: 杂志文章
doi:10.1088/1741-2560/6/2/026004
更新日期:2009-04-01 00:00:00
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pub_type: 杂志文章
doi:10.1088/1741-2560/12/1/016009
更新日期:2015-02-01 00:00:00
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pub_type: 杂志文章
doi:10.1088/1741-2552/aaa87a
更新日期:2018-04-01 00:00:00
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