@article{SchwetlickRothkegelTrukenbrodetal.2020,
  author    = {Schwetlick, Lisa and Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne and Engbert, Ralf},
  title     = {Modeling the effects of perisaccadic attention on gaze statistics during scene viewing},
  series = {Communications biology},
  volume    = {3},
  journal   = {Communications biology},
  number    = {1},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2399-3642},
  doi       = {10.1038/s42003-020-01429-8},
  pages     = {11},
  year      = {2020},
  abstract  = {Lisa Schwetlick et al. present a computational model linking visual scan path generation in scene viewing to physiological and experimental work on perisaccadic covert attention, the act of attending to an object visually without obviously moving the eyes toward it. They find that integrating covert attention into predictive models of visual scan paths greatly improves the model's agreement with experimental data. <br /> How we perceive a visual scene depends critically on the selection of gaze positions. For this selection process, visual attention is known to play a key role in two ways. First, image-features attract visual attention, a fact that is captured well by time-independent fixation models. Second, millisecond-level attentional dynamics around the time of saccade drives our gaze from one position to the next. These two related research areas on attention are typically perceived as separate, both theoretically and experimentally. Here we link the two research areas by demonstrating that perisaccadic attentional dynamics improve predictions on scan path statistics. In a mathematical model, we integrated perisaccadic covert attention with dynamic scan path generation. Our model reproduces saccade amplitude distributions, angular statistics, intersaccadic turning angles, and their impact on fixation durations as well as inter-individual differences using Bayesian inference. Therefore, our result lend support to the relevance of perisaccadic attention to gaze statistics.},
  language  = {en}
}
@article{LadeiraMarwanDestroFilhoetal.2020,
  author    = {Ladeira, Guenia and Marwan, Norbert and Destro-Filho, Joao-Batista and Ramos, Camila Davi and Lima, Gabriela},
  title     = {Frequency spectrum recurrence analysis},
  series = {Scientific reports},
  volume    = {10},
  journal   = {Scientific reports},
  number    = {1},
  publisher = {Nature portfolio},
  address   = {Berlin},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-020-77903-4},
  pages     = {9},
  year      = {2020},
  abstract  = {In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.},
  language  = {en}
}