Tagged: Diseases

Electrooculogram based system for computer control using a multiple feature classification model

This paper discusses the creation of a system for computer-aided communication through automated analysis and processing of electrooculogram signals. In situations of disease or trauma, there may be an inability to communicate with others through standard means such as speech or typing. Eye movement tends to be one of the last remaining active muscle capabilities for people with neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS) also known as Lou Gehrig’s disease. Thus, there is a need for eye movement based systems to enable communication. To meet this need, the Telepathix system was designed to accept eye movement commands denoted by looking to the left, looking to the right, and looking straight ahead to navigate a virtual keyboard. Using a ternary virtual keyboard layout and a multiple feature classification model, a typing speed of 6 letters per minute was achieved

Brain-computer interfaces

The human brain is perhaps the most fascinating and complex signal processing machine in existence. It is capable of transducing a variety of environmental signals (the senses, including taste, touch, smell, sound, and sight) and extracting information from these disparate signal streams, ultimately fusing this information to enable behavior, cognition, and action. What is perhaps surprising is that the basic signal processing elements of the brain, i.e., neurons, transmit information at a relatively slow rate compared to transistors, switching about 106 times slower in fact. The brain has the advantage of having a tremendous number of neurons, all operating in parallel, and a highly distributed memory system of synapses (over 100 trillion in the cerebral cortex) and thus its signal processing capabilities may largely arise from its unique architecture. These facts have inspired a great deal of study of the brain from a signal processing perspective. Recently, scientists and engineers have focused on developing means in which to directly interface with the brain, essentially measuring neural signals and decoding them to augment and emulate behavior. This research area has been termed brain computer interfaces and is the topic of this issue of IEEE Signal Processing Magazine.

Recovery of metabolomic spectral sources using non-negative matrix factorization

1H magnetic resonance spectra (MRS) of biofluids contain rich biochemical information about the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, such data have been shown to provide information for disease diagnosis as well as the effects of potential therapeutics. In this paper we describe a novel approach, using non-negative matrix factorization (NMF), for rapidly identifying metabolically meaningful spectral patterns in1H MRS. We show that the intensities of these identified spectral patterns can be related to the onset of, and recovery from, toxicity in both a time-related and dose-related fashion. These patterns can be seen as a new type of biomarker for the biological effect under study. We demonstrate, using k-means clustering, that the recovered patterns can be used to characterize the metabolic status of the animal during the experiment.