João Luís Garcia Rosa

• Abstract

  Music can change our emotions, influence our mood and ultimately affect our health. Music therapy is one of the oldest methods used to treat neurological disorders. Therefore, the combination of the music therapy process with electroencephalography (EEG)-based human emotion recognition algorithms can be a useful approach. Thus, it would be possible to identify the user's emotional state a…

  Brain computer music interface (BCMI): supporting mental disorder treatments, AP.R
• The 2019 International Joint Conference on Neural Networks (IJCNN), AR.EXT

• Abstract

  Brain-Computer Interfaces (BCI) is a form of communication that enables individuals unable to perform movements to connect to external assistive devices using the electroencephalography (EEG) or other brain signals. Noninvasive BCIs capture changes in blood flow or fluctuations in electric and magnetic fields caused by the activity of large populations of neurons. The EEG, a non-invasive …

  Development of algorithms and computational techniques for application in brain-computer interfaces, AP.R

(Only some records are available in English at this moment)

• Abstract

  Electroencephalogram classification (EEG) consists of, from the EEG signal, detecting information about the state of the brain to identify, for example, actions and intentions of an individual. EEG classification is one way of working with brain-computer interfaces (BCI) and may have several applications, among which stands out the use in electronic games, helping people with disabilities…

  Continuous electroencephalogram classification using a Connectionist Model based on Populations of Neurons, BP.MS

• Abstract

  The automatic identification of semantic roles in natural language texts is an important step towards the computational comprehension of meaning. This project aims at developing an automatic semantic role annotator for Brazilian Portuguese, inspired in a methodology shown to be promising for the English language and which offers the possibility to use non-labeled data. Important adaptatio…

  A connectionst approach for semantic role labeling, BP.MS

• Abstract

  Semantic Role Labeling (SRL) is a natural language processing (NLP) task which, in the last years, has been highly researched because it allows an analysis of the meaning of sentences through detecting the events that are being described in them, the same as the participants involved, which is essential for computers to effectively understand the information coded in text. However, most t…

  Semi-supervised automatic semantic role labeling for brazilian portuguese, BP.MS

• Abstract

  Current neural network models are far beyond the physiology of cerebral cortex neurons. Brain models based on neurodynamics consider neurons asdynamical systems. And as such, they seek to understand and represent the reasons why neurons are excitable cells. The microscopic current from each neuron sums with currents from other neurons, which causes a macroscopic potential difference, meas…

  Brain computational models based on neurodynamical populations at mesoscopic level, BE.PQ

• Abstract

  One of the main challenges in semantic role labeling (SRL) is the development of applications for languages other than English. For Brazilian Portuguese, the necessary lexical resources are currently being developed. However, the amount of annotated data to be provided wouldn't allow for an appropriate supervised learning. Considering this data scarcity problem, semi-supervised and unsupe…

  Adapting unsupervised semantic role labeling methods for Brazilian Portuguese, BE.EP.MS