Publications by authors named "Luisa F Villa"
Article Synopsis
- The EEG provides key insights into identifying emotional states by analyzing signals through both time series and their generation processes.
- A functional connectivity measurement was used to create adjacency matrices representing different emotions, which helped in understanding relationships between EEG signals.
- Differences in electrode intensity, location, and connectivity patterns were identified, revealing shared elements among emotional states and facilitating emotional clustering from an EEG perspective.
Emotion Recognition from EEG and Facial Expressions: a Multimodal Approach.
Annu Int Conf IEEE Eng Med Biol Soc
July 2018
The understanding of a psychological phenomena such as emotion is of paramount importance for psychologists, since it allows to recognize a pathology and to prescribe a due treatment for a patient. While approaching this problem, mathematicians and computational science engineers have proposed different unimodal techniques for emotion recognition from voice, electroencephalography, facial expression, and physiological data. It is also well known that identifying emotions is a multimodal process.
View Article and Find Full Text PDFResearch into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations.
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