Institutions
|
About Us
|
Help
|
Gaeilge
0
1000
Home
Browse
Advanced Search
Search History
Marked List
Statistics
A
A
A
Author(s)
Institution
Publication types
Funder
Year
Limited By:
Subject = Brain-computer interface;
2 items found
Sort by
Title
Author
Item type
Date
Institution
Peer review status
Language
Order
Ascending
Descending
25
50
100
per page
Bibtex
CSV
EndNote
RefWorks
RIS
XML
Displaying Results 1 - 2 of 2 on page 1 of 1
Marked
Mark
An analysis of EEG signals present during target search
(2012)
Healy, Graham
An analysis of EEG signals present during target search
(2012)
Healy, Graham
Abstract:
Recent proof-of-concept research has appeared highlighting the applicability of using Brain Computer Interface (BCI) technology to utilise a subjects visual system to classify images. This technique involves classifying a users EEG (Electroencephalography) signals as they view images presented on a screen. The premise is that images (targets) that arouse a subjects attention generate distinct brain responses, and these brain responses can then be used to label the images. Research thus far in this domain has focused on examining the tasks and paradigms that can be used to elicit these neurologically informative signals from images, and the correlates of human perception that modulate them. While success has been shown in detecting these responses in high speed presentation paradigms, there is still an open question as to what search tasks can ultimately benefit from using an EEG based BCI system. In this thesis we explore: (1) the neural signals present during visual search tasks th...
http://doras.dcu.ie/16778/
Marked
Mark
Synthetic-Neuroscore: using a neuro-AI interface for evaluating generative adversarial networks
(2020)
Wang, Zhengwei; She, Qi; Smeaton, Alan F.; Ward, Tomás E.; Healy, Graham
Synthetic-Neuroscore: using a neuro-AI interface for evaluating generative adversarial networks
(2020)
Wang, Zhengwei; She, Qi; Smeaton, Alan F.; Ward, Tomás E.; Healy, Graham
Abstract:
Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. Arguably the most striking results have been in the area of image synthesis. However, evaluating the performance of GANs is still an open and challenging problem. Existing evaluation metrics primarily measure the dissimilarity between real and generated images using automated statistical methods. They often require large sample sizes for evaluation and do not directly reflect the human perception of the image quality. In this work, we introduce an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals. Our results show that Neuroscore has superior performances to the current evaluation metrics in that:(1) It is more consistent with human judgment;(2) The evaluation process needs much smaller numbe...
http://doras.dcu.ie/24650/
Displaying Results 1 - 2 of 2 on page 1 of 1
Bibtex
CSV
EndNote
RefWorks
RIS
XML
Year
2020 (1)
2012 (1)
built by Enovation Solutions