Metastatic Melanoma to the Urinary Bladder: A Rare Cause of Visible Haematuria.

Bladder metastasis from cutaneous melanoma is a rare pathology. A 79-year-old woman presented to the haematuria clinic on account of painless visible haematuria. Ten years prior to this index presentation, she was diagnosed with melanoma on her right thigh following a total excision of the skin lesion.

A comparison of stimulus types in online classification of the P300 speller using language models.

The P300 Speller is a common brain-computer interface communication system. There are many parallel lines of research underway to overcome the system's low signal to noise ratio and thereby improve performance, including using famous face stimuli and integrating language information into the classifier. While both have been shown separately to provide significant improvements, the two methods have not yet been implemented together to demonstrate that the improvements are complimentary.

A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems.

Objective: The P300 speller is intended to restore communication to patients with advanced neuromuscular disorders, but clinical implementation may be hindered by several factors, including system setup, burden, and cost. Our goal was to develop a method that can overcome these barriers by optimizing EEG electrode number and placement for P300 studies within a population of subjects.

Methods: A Gibbs sampling method was developed to find the optimal electrode configuration given a set of P300 speller data.

Integrating language information with a hidden Markov model to improve communication rate in the P300 speller.

The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram (EEG) signal. Information about the structure of natural language can be valuable for BCI communication systems, but few attempts have been made to incorporate this domain knowledge into the classifier. In this study, we treat BCI communication as a hidden Markov model (HMM) where hidden states are target characters and the EEG signal is the visible output.