Abstract: Systems and methods for a machine learning system to learn a new skill without catastrophically forgetting an existing skill and to continually learn in a self-supervised manner during operation, without human intervention.

Abstract: A brain machine interface system for use with an electroencephalogram to identify a behavioral intent of a person is disclosed. The system includes an electroencephalogram configured to sense electromagnetic signals generated by a brain of a person. The electromagnetic signals include a time component and a frequency component. A monitor monitors a response of the person to a stimulus and a characteristic of the stimulus. A synchronization module synchronizes the sensed electromagnetic signals with the response and the characteristic to determine a set of electromagnetic signals corresponding to the monitored response and the characteristic. A processor processes the set of electromagnetic signals and extracts feature vectors. The feature vectors define a class of behavioral intent. The processor determines the behavioral intent of the person based on the feature vectors. A brain machine interface and a method for identifying a behavioral intent of a person is also disclosed.

Abstract: A vision-cued random-access LIDAR system and method which determines the location and/or navigation path of a moving platform. A vision system on a moving platform identifies a region of interest. The system classifies objects within the region of interest, and directs random-access LIDAR to ping one or more of the classified objects. The platform is located in three dimensions using data from the vision system and LIDAR. The steps of classifying, directing, and locating are preferably performed continuously while the platform is moving and/or the vision system's field-of-view (FOV) is changing. Objects are preferably classified using at least one smart-vision algorithm, such as a machine-learning algorithm.

Abstract: A vision-cued random-access LIDAR system and method which determines the location and/or navigation path of a moving platform. A vision system on a moving platform identifies a region of interest. The system classifies objects within the region of interest, and directs random-access LIDAR to ping one or more of the classified objects. The platform is located in three dimensions using data from the vision system and LIDAR. The steps of classifying, directing, and locating are preferably performed continuously while the platform is moving and/or the vision system's field-of-view (FOV) is changing. Objects are preferably classified using at least one smart-vision algorithm, such as a machine-learning algorithm.

Abstract: A brain machine interface system for use with an electroencephalogram to identify a behavioral intent of a person is disclosed. The system includes an electroencephalogram configured to sense electromagnetic signals generated by a brain of a person. The electromagnetic signals include a time component and a frequency component. A monitor monitors a response of the person to a stimulus and a characteristic of the stimulus. A synchronization module synchronizes the sensed electromagnetic signals with the response and the characteristic to determine a set of electromagnetic signals corresponding to the monitored response and the characteristic. A processor processes the set of electromagnetic signals and extracts feature vectors. The feature vectors define a class of behavioral intent. The processor determines the behavioral intent of the person based on the feature vectors. A brain machine interface and a method for identifying a behavioral intent of a person is also disclosed.

Abstract: A brain machine interface system for use with an electroencephalogram to identify a behavioral intent of a person is disclosed. The system includes an electroencephalogram configured to sense electromagnetic signals generated by a brain of a person. The electromagnetic signals include a time component and a frequency component. A monitor monitors a response of the person to a stimulus and a characteristic of the stimulus. A synchronization module synchronizes the sensed electromagnetic signals with the response and the characteristic to determine a set of electromagnetic signals corresponding to the monitored response and the characteristic. A processor processes the set of electromagnetic signals and extracts feature vectors. The feature vectors define a class of behavioral intent. The processor determines the behavioral intent of the person based on the feature vectors. A brain machine interface and a method for identifying a behavioral intent of a person is also disclosed.

Abstract: Systems and methods for a machine learning system to learn a new skill without catastrophically forgetting an existing skill and to continually learn in a self-supervised manner during operation, without human intervention.

Abstract: Provided is an apparatus, system, and method for targeted memory enhancement. A computer processing circuit receives a plurality of electroencephalography (EEG) signals from a plurality of spatially separated EEG sensors located on the head of a subject that is asleep. A first process of the computer processing system determines a sleep state of the subject and upon determining that the subject is in sleep stage 2 or 3 based on a specific EEG signal, the processing system triggers a second process of the computer processing system that determines a transition event in the specific EEG signal, and upon detecting the transition event delivers an intervention to the subject designed to evoke a specific neurophysiological change to the subject.

Abstract: Provided is an apparatus, system, and method for targeted sleep enhancement. A computer processing circuit receives a plurality of EEG signals from a plurality of spatially separated EEG sensors configured to be located on the head of a subject. The computer processing circuit executes machine executable instructions to: receive and process the plurality of EEG signals; determine that the subject is in sleep stage 3 based on a specific EEG signal of the processed plurality of EEG signals; determine a period of at least one of quiescent and asynchronous brain activity of the subject, wherein the period is determined based on the processed plurality of EEG signals; and deliver a transcranial electrical stimulation through the plurality of stimulation electrodes during the period of quiescent brain activity.

Abstract: Provided is an apparatus, system, and method for targeted memory enhancement. A computer processing circuit receives a plurality of electroencephalography (EEG) signals from a plurality of spatially separated EEG sensors located on the head of a subject that is asleep. A first process of the computer processing system determines a sleep state of the subject and upon determining that the subject is in sleep stage 2 or 3 based on a specific EEG signal, the processing system triggers a second process of the computer processing system that determines a transition event in the specific EEG signal, and upon detecting the transition event delivers an intervention to the subject designed to evoke a specific neurophysiological change to the subject.

Abstract: A brain machine interface system for use with an electroencephalogram to identify a behavioral intent of a person is disclosed. The system includes an electroencephalogram configured to sense electromagnetic signals generated by a brain of a person. The electromagnetic signals include a time component and a frequency component. A monitor monitors a response of the person to a stimulus and a characteristic of the stimulus. A synchronization module synchronizes the sensed electromagnetic signals with the response and the characteristic to determine a set of electromagnetic signals corresponding to the monitored response and the characteristic. A processor processes the set of electromagnetic signals and extracts feature vectors. The feature vectors define a class of behavioral intent. The processor determines the behavioral intent of the person based on the feature vectors. A brain machine interface and a method for identifying a behavioral intent of a person is also disclosed.