Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912372139> ?p ?o ?g. }
Showing items 1 to 59 of
59
with 100 items per page.
- W2912372139 abstract "Author(s): Ibagon, Gabriel | Advisor(s): de Sa, Virginia | Abstract: In this work, we explore the topic of forecasting the neural time series using machine-learning based techniques on electroencephalography (EEG) data. Forecasting EEG has a number of potential applications in brain-computer interfaces (BCI), such as ahead-of-time event classification, cognitive response prediction, and preemptive intervention therapy. However, previous work in EEG forecasting has failed to accurately predict the time series more than a few steps into the future. Simple linear models lack the capacity to model the high-dimensional dynamics of EEG activity, while complex nonlinear models are difficult to specify and implement. However, recent deep neural networks have effectively modeled high-dimensional systems in a variety of domains. In this work, we hope to bridge the gap between previous work in EEG forecasting and current techniques in deep learning. In particular, we explore forecasting the EEG patterns that occur after the presentation of a time-locked visual stimulus. We implement a deep neural network that extracts features from pre-event data in order to predict single-trial event-locked EEG data. To capture the variation of a single trial, the network constructs the post-event waveform in two parts: 1) generating ongoing neural activity and 2) generating evoked event-related responses. We evaluate our model by forecasting 500 milliseconds of single channel post-event data from a Rapid Serial Visual Presentation (RSVP) task. Our results indicate a significant increase in forecasting performance compared to baseline methods, suggesting that deep neural networks can extract informative features from EEG data in order to generate a prediction of the post-event waveform." @default.
- W2912372139 created "2019-02-21" @default.
- W2912372139 creator A5039965428 @default.
- W2912372139 date "2018-01-01" @default.
- W2912372139 modified "2023-09-24" @default.
- W2912372139 title "Forecasting the Neural Time Series: Deep Neural Networks for Predicting Event-Related EEG Responses" @default.
- W2912372139 hasPublicationYear "2018" @default.
- W2912372139 type Work @default.
- W2912372139 sameAs 2912372139 @default.
- W2912372139 citedByCount "0" @default.
- W2912372139 crossrefType "journal-article" @default.
- W2912372139 hasAuthorship W2912372139A5039965428 @default.
- W2912372139 hasConcept C108583219 @default.
- W2912372139 hasConcept C119857082 @default.
- W2912372139 hasConcept C147168706 @default.
- W2912372139 hasConcept C151406439 @default.
- W2912372139 hasConcept C154945302 @default.
- W2912372139 hasConcept C15744967 @default.
- W2912372139 hasConcept C169760540 @default.
- W2912372139 hasConcept C41008148 @default.
- W2912372139 hasConcept C50644808 @default.
- W2912372139 hasConcept C522805319 @default.
- W2912372139 hasConceptScore W2912372139C108583219 @default.
- W2912372139 hasConceptScore W2912372139C119857082 @default.
- W2912372139 hasConceptScore W2912372139C147168706 @default.
- W2912372139 hasConceptScore W2912372139C151406439 @default.
- W2912372139 hasConceptScore W2912372139C154945302 @default.
- W2912372139 hasConceptScore W2912372139C15744967 @default.
- W2912372139 hasConceptScore W2912372139C169760540 @default.
- W2912372139 hasConceptScore W2912372139C41008148 @default.
- W2912372139 hasConceptScore W2912372139C50644808 @default.
- W2912372139 hasConceptScore W2912372139C522805319 @default.
- W2912372139 hasLocation W29123721391 @default.
- W2912372139 hasOpenAccess W2912372139 @default.
- W2912372139 hasPrimaryLocation W29123721391 @default.
- W2912372139 hasRelatedWork W1535018235 @default.
- W2912372139 hasRelatedWork W2149212636 @default.
- W2912372139 hasRelatedWork W2750382711 @default.
- W2912372139 hasRelatedWork W2801360775 @default.
- W2912372139 hasRelatedWork W2804301383 @default.
- W2912372139 hasRelatedWork W2895858903 @default.
- W2912372139 hasRelatedWork W2900230652 @default.
- W2912372139 hasRelatedWork W2901655790 @default.
- W2912372139 hasRelatedWork W2910686462 @default.
- W2912372139 hasRelatedWork W2921455621 @default.
- W2912372139 hasRelatedWork W2962782417 @default.
- W2912372139 hasRelatedWork W2969288811 @default.
- W2912372139 hasRelatedWork W2969408183 @default.
- W2912372139 hasRelatedWork W2991293574 @default.
- W2912372139 hasRelatedWork W3049043038 @default.
- W2912372139 hasRelatedWork W3119089953 @default.
- W2912372139 hasRelatedWork W3169764988 @default.
- W2912372139 hasRelatedWork W3186148480 @default.
- W2912372139 hasRelatedWork W3205288591 @default.
- W2912372139 hasRelatedWork W186007012 @default.
- W2912372139 isParatext "false" @default.
- W2912372139 isRetracted "false" @default.
- W2912372139 magId "2912372139" @default.
- W2912372139 workType "article" @default.