Technology Transfer

Even into the final year of the program, the CaN CTA continues to overcome a wide array of challenges to real-world neuroimaging and modeling human performance in natural environments that can be applied to facilitate a broad range of neurotechnologies. Some of these efforts have resulted in tools and concepts transitioning to academic, government, and industry partners within and outside the CTA. Following are some examples:


CTA partners have developed novel machine learning (ML) approaches to explore and exploit neuro-physiology data as never before. CTA partners have demonstrated a proof-of-concept of artificial intelligence (AI) that detects the perception of mission-relevant objects in unstructured environments using classification models trained across multiple disparate data collections and no user-specific calibration.


CTA partners have transitioned innovative solutions for dry-electrode electroencephalography (EEG) with prototypes to ARL, other academic labs, as well as industry. ARL has tested and integrated the wireless dry-electrode systems into the instrumentation for multiple applied research projects. In addition, several commercial dry-electrode EEG products have leveraged this research and are being used by multiple educational institutions. These institutions include University of California San Diego (UCSD), University of Malaysia, University of British Columbia, and Korea Advanced Institute of Science and Technology. Moreover, some of the dry-electrode products and evaluation methodologies have been transitioned to the laboratories of large and small industry stakeholders such as Nissan Motor Co. (Japan), NeuroRex Inc. (US), Alchemy (Taiwan), Neurocare (Singapore), Google X (US), and Intel (US).


Lab Streaming Layer (LSL), a multi-aspect data acquisition and synchronization software backbone, is being adopted by Neurobehavioral Systems for integration into the commercial stimulus presentation tool, Presentation™. Additionally, LSL has become a key integration and synchronization technology for a number of ARL projects, including large-scale research efforts supported by the Next-Generation Combat Vehicle and Soldier Lethality Cross-Functional Teams. Importantly, LSL is now being used by a growing number of academic and industry labs around the world to create a unified ecosystem for human sensing.


CTA partners have developed a wide array of other software tools useful for non-invasive investigations of brain function (using electroencephalography [EEG] and other modalities). Many of these tools are incorporated into larger tool suites such as BCILAB (i.e., platform for brain-computer interface) and EEGLAB, which are made available to the research community and utilized by many institutions.


The CaN CTA has also pursued technology transfer and integration targets both within and outside government laboratories. In particular, we have conducted translational research toward enabling future advances in human autonomy integration in automotive environments. We have progressed our driving research by moving the investigation into real cars on real roads while also adding real-world social effects. In parallel, we have coordinated with an applied research project that is investigating the brain processes as a driver interacts with modern driving aid technologies.


CaN CTA efforts have resulted in a new applied research program, utilizing several of the above technologies, that will refine and validate a novel concept for enhancing tactical situational awareness of mounted and dismounted Soldiers through opportunistic sensing of signals related to visual perception, across multiple individuals. This program leverages deep learning approaches trained on prior datasets to enable calibration free operation, in addition to other computational techniques to synergistically improve computer vision algorithms given human-labeled data. The goal of this technology is to improve unit effectiveness through seamless human-autonomy integration without added cognitive burden on the Soldier.

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