Recent scienti?c and technical advances enable the development of systems for creating novel interactions with the central nervous system (CNS) that can induce bene?cial plasticity. These systems, called adaptive neurotechnologies, measure signals from the CNS, derive from these signals the state of the CNS, and adaptively provide feedback that can restore, replace, enhance, supplement or improve CNS functions impaired by injury or disease. Thus, they can provide powerful new therapies for stroke, head or spinal cord injury, cerebral palsy, and other devastating disorders. For example, they can restore communication to people who have lost muscle control, and they can enhance functional recovery for people with spinal cord injury or stroke. The development of these technologies is impeded by the need for research groups to create specialized real-time software, which is usually a lengthy, dif?cult, expensive, and sometimes impractical task. Thus, realization of these new technologies could be greatly facilitated by a robust and ?exible software platform that supports complex real-time interactions with the CNS throughout the development process, from the laboratory through clinical testing. The goal of this proposal is to create this platform. The central hypothesis is that, by creating this new platform and giving it to scientists, engineers, and clinicians, this software platform will accelerate realization of adaptive neurotechnologies that reduce the devastating impact of neurological disorders. This hypothesis is supported by the investigators' past experience and success in creating and disseminating BCI2000, a software platform for brain-computer interfaces (BCIs), one category of adaptive neurotechnologies. BCI2000 has supported scienti?c and clinical studies reported in over 1000 papers. This proposed project will transform BCI2000 into BCI2000+, a hardened, expanded, easy-to-use, and fully documented software platform for a broad range of adaptive neurotechnologies.
Aim 1 will create a reliable, fail-safe, and fault-tolerant architecture, produce new functionalities for multimodal signal acquisition, real-time processing and output generation, and user extensions.
Aim 2 will produce new graphical tools for rapid system prototyping, advanced signal and data visualization, comprehensive user-appropriate documentation, and auxiliary tools for data management and of?ine analysis. BCI2000+ will be optimized and validated through extensive in-lab testing and through beta testing by other groups. Achievement of these aims will produce BCI2000+, a software platform that supports new adaptive neurotech- nologies from initial laboratory studies through clinical testing. This robust, ?exible, and easily adopted platform should encourage scientists, engineers, and clinicians to join in this exciting work; it should foster a collaborative environment that enables diverse investigators to work together and complement each other. In sum, the work proposed here will accelerate realization of novel adaptive neurotechnologies that enable scienti?c investigation and improve treatment for stroke, brain and spinal cord injury, and other devastating neurological disorders.
Adaptive neurotechnologies interact with the nervous system to provide new therapies for a wide variety of neurological disorders. The development of these technologies is complex, time consuming, and costly. In this project, we propose to create and disseminate a software platform that greatly facilitates the development process, and thereby accelerates the realization of new adaptive neurotechnologies that improve treatment for many devastating neurological disorders.
| Swift, J R; Coon, W G; Guger, C et al. (2018) Passive functional mapping of receptive language areas using electrocorticographic signals. Clin Neurophysiol 129:2517-2524 |
| Li, Guangye; Jiang, Shize; Paraskevopoulou, Sivylla E et al. (2018) Optimal referencing for stereo-electroencephalographic (SEEG) recordings. Neuroimage 183:327-335 |
| Saez, Ignacio; Lin, Jack; Stolk, Arjen et al. (2018) Encoding of Multiple Reward-Related Computations in Transient and Sustained High-Frequency Activity in Human OFC. Curr Biol 28:2889-2899.e3 |
