The next generation of Learning and Human Performance Solutions (L&HPS) are being driven by end-user needs for highly personalized, adaptive, and ubiquitous systems that can be easily and intuitively used via modern computing and communication device platforms. However, shrinking Federal budgets and upcoming Federal acquisition reforms are requiring that these systems also be low cost - both in terms of the cost of component technologies, and the human capital costs required throughout the systems engineering lifecycle.

This paper describes our research efforts in examining and combining Neuro-Technology with synergistic technologies such as Gesture Recognition, Haptics, Facial Expression Recognition, Voice Recognition and Advanced Data Visualization to identify and evaluate new paradigms of advanced "Human-Machine" command/control and feedback interfaces for future training/learning applications. The paper describes our findings in areas such as Neurofeedback, Adaptive Peak Performance Training, Thought Pattern Recognition, etc. The paper also outlines our findings in regards to a set of challenges that lie ahead.

Significantly, our research indicates the strong viability of using low cost, intuitive, stable, and commercially available component technologies that are characterized by active open source software "ecosystems". Further, these components require low cost human capital skills during systems integration. In our current research, we describe Gesture-based Computing and Brain Computer Interface technologies and examine their viability for enabling low-cost, high value applications to accelerate progress towards achieving the next generation Learning & Human Performance Solutions.