Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.

The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the complexity of this brain process makes the analysis and classification of this type of signals a relevant topic of research. The goals of this study were: to develop a new algorithm based on Deep Learning (DL), referred to as CNNeeg1-1, to recognize EEG signals in imagined vowel tasks; to create an imagined speech database with 50 subjects specialized in imagined vowels from the Spanish language (/a/,/e/,/i/,/o/,/u/); and to contrast the performance of the CNNeeg1-1 algorithm with the DL Shallow CNN and EEGNet benchmark algorithms using an open access database (BD1) and the newly developed database (BD2).

Evaluation of the Architect HIV Ag/Ab Combo Assay in a low-prevalence setting: The role of samples with a low S/CO ratio.

Background: The Architect HIV Ag/Ab Combo Assay, a fourth-generation ELISA, has proven to be highly reliable for the diagnosis of HIV infection. However, its high sensitivity may lead to false-positive results.

Objectives: To evaluate the diagnostic performance of Architect in a low-prevalence population and to assess the role of the sample-to-cutoff ratio (S/CO) in reducing the frequency of false-positive results.