Erratum to “The neuroergonomic evaluation of human machine interface design in air traffic control using behavioral and EEG/ERP measures” [Behav. Brain Res. 294 (2015) 246–253]

Giraudet, Louise; Imbert, Jean-Paul; Bérenger, M.; Tremblay, Sébastien; Causse, Mickaël

doi:10.1016/j.bbr.2016.01.003

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results showed decreased P300 amplitude with increased working memory load for the incongruent flanker stimuli. This result has also been found in more applied settings as evidenced by decreased P300 amplitudes under high compared to low workload/baseline in an aircraft control task (Causse et al, 2016; Giraudet et al, 2016). P300 latency is also affected by task demands, as demonstrated in a recent study by Ries et al (2016), which showed that target response latency significantly increased as a function of auditory working memory load.…”

Section: Review Of the P300 Evoked Responsesupporting

confidence: 62%

Decoding P300 Variability Using Convolutional Neural Networks

Solon

Lawhern

Touryan

et al. 2019

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition. By learning from large amounts of data, the representations encoded by these deep networks are often invariant to moderate changes in the underlying feature spaces. Recently, we proposed a CNN architecture that could be applied to electroencephalogram (EEG) decoding and analysis. In this article, we train our CNN model using data from prior experiments in order to later decode the P300 evoked response from an unseen, hold-out experiment. We analyze the CNN output as a function of the underlying variability in the P300 response and demonstrate that the CNN output is sensitive to the experiment-induced changes in the neural response. We then assess the utility of our approach as a means of improving the overall signal-to-noise ratio in the EEG record. Finally, we show an example of how CNN-based decoding can be applied to the analysis of complex data.

show abstract

Section: Review Of the P300 Evoked Responsesupporting

confidence: 62%