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Human gait is a complex process in the central nervous system that results from the integrity of various mechanisms, including different cortical and subcortical structures. In the present study, we investigated cortical activity during lower limb movement using EEG. Assisted by a dynamic tilt table, all subjects performed standardized stepping movements in an upright position. Source localization of the movement-related potential in relation to spontaneous EEG showed activity in brain regions classically associated with human gait such as the primary motor cortex, the premotor cortex, the supplementary motor cortex, the cingulate cortex, the primary somatosensory cortex and the somatosensory association cortex. Further, we observed a task-related power decrease in the alpha and beta frequency band at electrodes overlying the leg motor area. A temporal activation and deactivation of the involved brain regions as well as the chronological sequence of the movement-related potential could be mapped to specific phases of the gait-like leg movement. We showed that most cortical capacity is needed for changing the direction between the flexion and extension phase. An enhanced understanding of the human gait will provide a basis to improve applications in the field of neurorehabilitation and brain-computer interfaces.

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A critical element of system readiness is the effectiveness of integrated diagnostics and prognostics.Errors in detection and isolation of failures cause unnecessary maintenance actions requiring additional troubleshooting time and replacement of could not duplicates (CNDs). Integrated diagnostics/prognostics is achieved through a systems engineering closed loop process from start to finish. Every step from conceptual phase through program shut down thrives on an integrated approach to insure maximum coverage of faults and unambiguous isolation while minimizing false alarms and re-test okays (RTOKs). An integrated health management system (HMS) methodology, proposed in this paper, connects functionality, failure modes and diagnostics/prognostics under one umbrella providing a conduit for tight traceability from requirements through design, analysis, integration, verification and validation, factory testing, and fielding while encouraging maturation through data collection. This framework minimizes errors between diagnostic/prognostic analyses and actual performance, maximizes test verticality, and paves the way for a lower risk fielded product. Iterative analyses and trade studies during the concept and development phases optimize diagnostic/prognostics approaches and architecture. Analyses and simulations during the development phase optimize health sensor selection, sensor placement, test effectiveness, and test strategies (i.e. embedded vs. external, test flow, test type etc.). Analyses and test data provide the foundation for the fidelity and accuracy of a diagnostic/prognostic reasoner, which selects