@article{leveille_effects_2022,

title = {Effects of Emotion and Emotional Ambiguity on Early Visual Event-Related Potentials},

author = {E. Léveillé and M. Desjardins and G. Dumel and C. Blais and D. Saint-Amour and P. Scherzer and L. De Beaumont},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135853427&doi=10.1037%2femo0001119&partnerID=40&md5=7e89d6dee0b96766b8a359a3a9be4d73},

doi = {10.1037/emo0001119},

issn = {15283542 (ISSN)},

year  = {2022},

date = {2022-01-01},

journal = {Emotion},

volume = {23},

number = {3},

pages = {787–804},

abstract = {The modulation of early sensory event-related potentials such as the P1, N1, and N170 by emotion and emotional ambiguity is still controversial. Some studies have found a modulation of one or all of these components by one or both of these factors, whereas others have failed to show such results. The objective of this study was to investigate the effects of emotion and ambiguity on the behavioral and electrophysiological responses to a morphed emotion recognition task. Thirty-seven healthy participants (19 men) completed an emotion recognition task where photographs of a male face expressing the six basic emotions morphed with another emotion (in a proportion ranging from 26% to 74%) were randomly presented while electroencephalography was recorded. After each face presentation, participants were asked to identify the facial emotion. We found an emotional effect on the P1, N1, and N170, with greater amplitudes for some emotional facial expressions than for others. However, we found no significant emotional ambiguity effect or interaction between emotion and ambiguity for any of these components. These findings suggest that computation of emotional facial expressions (regardless of their ambiguity) occurs from the early stages of brain processing. © 2022 American Psychological Association},

note = {Publisher: American Psychological Association},

keywords = {adult, article, Brain, clinical article, controlled study, Electroencephalography, electroencephalography (EEG), emotion, emotional facial expression (EFE), event related potential, event-related potential (ERP), Facial Expression, female, human, human experiment, male, mass univariate statistics, photography, univariate analysis},

pubstate = {published},

tppubtype = {article}

}

@article{messaoudi_efficient_2021,

title = {Efficient Embedding Network for 3D Brain Tumor Segmentation},

author = {H. Messaoudi and A. Belaid and M. L. Allaoui and A. Zetout and M. S. Allili and S. Tliba and D. Ben Salem and P. -H. Conze},

editor = {Bakas S. Crimi A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107387134&doi=10.1007%2f978-3-030-72084-1_23&partnerID=40&md5=b3aa3516b0465a1bf5611db4727d95f1},

doi = {10.1007/978-3-030-72084-1_23},

issn = {03029743},

year  = {2021},

date = {2021-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {12658 LNCS},

pages = {252–262},

abstract = {3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural image analysis, where very large datasets exist. As a result, powerful and efficient 2D convolutional neural networks have been developed and trained. In this paper, we investigate a way to transfer the performance of a two-dimensional classification network for the purpose of three-dimensional semantic segmentation of brain tumors. We propose an asymmetric U-Net network by incorporating the EfficientNet model as part of the encoding branch. As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network. Experimental results on validation and test data from the BraTS 2020 challenge demonstrate that the proposed method achieve promising performance. © 2021, Springer Nature Switzerland AG.},

note = {ISBN: 9783030720834 

Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {3D medical image processing, Brain, Brain tumor segmentation, Classification networks, Convolutional neural networks, Deep learning, Embedding network, Image segmentation, Large dataset, Large datasets, Medical imaging, Natural images, Net networks, Semantic segmentation, Semantics, Signal encoding, Tumors},

pubstate = {published},

tppubtype = {article}

}

@article{nolin_assessment_2009,

title = {Assessment of inhibition deficits with the virtual classroom in children with traumatic brain injury: A pilot-study},

author = {P. Nolin and C. Martin and S. Bouchard},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-71749121874&partnerID=40&md5=e13dc1c0941767a5d373bb1f8177b2c6},

issn = {15548716 (ISSN)},

year  = {2009},

date = {2009-01-01},

journal = {Annual Review of CyberTherapy and Telemedicine},

volume = {7},

number = {1},

pages = {240–242},

abstract = {This study compared the performance of 8 children who have sustained a traumatic brain injury on the traditional VIGIL Continuous Performance Testand the Continuous Performance Test included in the Virtual Classroom. Results supported the hypothesis, showing that the Continuous Performance Test from the Virtual Classroom showed more sensitivity concerning inhibition deficits. More precisely, children showed more commission errors and longer reaction time. These results can be explained by the ecological character of the Virtual Classroom, meaning that this instrument is close to real-life experiences and requires more attention and inhibition resources.},

keywords = {article, attention deficit disorder, Attention Deficit Disorder with Hyperactivity, Brain, Brain Injuries, brain injury, Child, Children, clinical article, Computer aided instruction, Ecological character, human, Humans, inhibition (psychology), Inhibition deficit, Life experiences, neuropsychological test, Neuropsychological Tests, Performance tests, Pilot Projects, Pilot studies, pilot study, reaction time, school, Traumatic Brain Injuries, traumatic brain injury, Virtual Classroom, virtual reality},

pubstate = {published},

tppubtype = {article}

}