Abstract: The present disclosure provides systems and processes for compensating disruptions in a brain-machine interface (BMI). Briefly described, the systems and processes detect and compensate for transient disruptions, reversible disruptions, irreversible compensable disruptions, or irreversible non-compensable disruptions.

Abstract: In an approach to neural interface systems, a system includes feature extraction circuitry to identify one or more features of one or more input signals; and neural processing circuitry. The neural processing circuitry is configured to: identify a first context of a plurality of contexts based on a first trigger event; decode the one or more features of the one or more input signals to determine a first task of a plurality of tasks in the first context; and responsive to detecting a second trigger event, change the first context to a second context of the plurality of contexts.

Abstract: The present disclosure is directed to systems and methods of providing systems and methods of autonomously generating summary documents based, at least in part, on a plurality of queries provided by a system user. The systems and methods disclosed herein include processor circuitry to identify a plurality of information sources for a specific topic guided by an ontology with specific concepts and relations. The systems and methods disclosed herein also include processor circuitry to generate user-focused extractive text summarization from each of at least some of the plurality of identified information sources using a plurality of queries supplied by the user/researcher.

Abstract: Auditory communication devices and related methods are described herein. An example auditory communication device can include a microphone configured to collect acoustic energy and convert the collected acoustic energy into an audio signal, a processor operably coupled to the microphone, and a memory operably coupled to the processor. The processor can be configured to receive the audio signal from the microphone, create a time-frequency (T-F) representation of the audio signal, classify each of a plurality of T-F units into one of N discrete categories, and attenuate the T-F representation of the audio signal. A respective level of attenuation for each of the T-F units is determined by its respective classification. The processor can be further configured to create a synthesized signal from the attenuated T-F representation of the audio signal.

Abstract: Auditory communication devices and related methods are described herein. An example auditory communication device can include a microphone configured to collect acoustic energy and convert the collected acoustic energy into an audio signal, a processor operably coupled to the microphone, and a memory operably coupled to the processor. The processor can be configured to receive the audio signal from the microphone, create a time-frequency (T-F) representation of the audio signal, classify each of a plurality of T-F units into one of N discrete categories, and attenuate the T-F representation of the audio signal. A respective level of attenuation for each of the T-F units is determined by its respective classification. The processor can be further configured to create a synthesized signal from the attenuated T-F representation of the audio signal.