Neural Engineering involves applying engineering techniques to the study of the central and peripheral nervous systems, with research ranging from basic neurophysiology and neuroscience questions to neurology and neurosurgery clinical and translational research. Engineers, scientists and clinicians in this field use many different approaches to study brain function and the effects of disease, including:
- Designing devices or therapies that interface with the brain to restore or enhance function. Examples are the development of brain-computer interfaces or brain-machine interfaces, neural prostheses, neural control algorithms and neuro-rehabilitation therapies;
- Performing experimental studies of neural cells and circuits to measure and modify network properties. Applications are neural tissue regeneration, neural networks and neuromorphic computing;
- Applying advanced quantitative techniques to neural data to study the central nervous system, uncover disease biomarkers and aid in clinical assessment and diagnosis. This work overlaps with the fields of neuroinformatics, neural systems biology and signal and image processing. Studies may be based on structural and functional imaging techniques, including scalp and intracranial electroencephalogram (EEG), magnetoencephalogram (MEG), magnetic resonance imaging (MRI), functional MRI, positron emission tomography (PET) and optical techniques;
- Building mathematical models to explain underlying mechanisms and making new predictions that can be tested experimentally. This work is often described as theoretical neuroscience or systems and computational neuroscience.