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).

Intracerebral hemorrhage caused by varicella-induced thrombocytopenia.

We present the case of a previously healthy 44-years-old man with chickenpox, severe thrombocytopenia, mucosal hemorrhage, and intracerebral hemorrhage in the right hemisphere. The patient was treated with platelets and high doses of steroids. He recovered although with persistent left homonymous hemianopsia and epilepsy, which were controlled with medication.