NIH Stroke Scale

The National Institute of Neurological Disorders and Stroke (NINDS) offers a variety of educational materials designed for health care professionals involved in treatment of acute stroke. The materials can be ordered in single copies or in quantity, and can be used in medical schools, educational settings, hospitals, clinics, and other health care facilities. The community education kit is a tool for increasing public awareness about stroke. Additional public education materials on stroke are also available for free from NINDS:

• A portable, pocket-sized, laminated reference booklet for health professionals who administer the NIH Stroke Scale to stroke patients. Graphical Version » Text Version
• An interactive DVD training tool for administering and scoring the NIH Stroke Scale. Studio-produced footage provides clear views of doctors performing the NIH Stroke Scale. Instructional examples illustrate every potential score on each scale item. On-screen menus allow you to view, repeat, and move between sections with ease. More Information » Order »

Extensible Neuroimaging Archive Toolkit

The Extensible Neuroimaging Archive Toolkit (XNAT) is an open source software platform designed to facilitate management and exploration of neuroimaging and related data. XNAT includes a secure database backend and a rich web-based user interface (Go).

SenseLab

The SenseLab Project is a long term effort to build integrated, multidisciplinary models of neurons and neural systems, using the olfactory pathway as a model. This is one of a number of projects funded as part of the Human Brain Project whose aim is to develop neuroinformatics tools in support of neuroscience research. The project involves novel informatics approaches to constructing databases and database tools for collecting and analyzing neuroscience information, and providing for efficient interoperability with other neuroscience databases (Go).

EEGlab

EEGlab is an interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data using independent component analysis (ICA), time/frequency analysis, artifact rejection, and several modes of data visualization.
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