Teledyne Scientific and Imaging, LLC

Patrick M Connolly

Stephen Simons

Mario Aguilar-Simon

Barry Ahrens

Karen Zachery

William D Reynolds, Jr

David Krnavek

Renee Shimizu

Ming Qian

Mario Aguilar

Aaron B Hawkins

Alexandra Yanoschak

Michael Weisend

Mary Whatley

Ryan Charles Brown

Andrew Paul Brna

Rolando Estrada

Calvin Schmidt

William Reynolds

A sleep induction device includes a headband, multiple transcranial stimulation electrodes, and control electronics to drive the electrodes. The sleep induction device may be worn by an awake user prior to attempting sleep. The control electronics are configured to cause the stimulation electrodes to emit a sequence of stimulation waveforms separated by interstimulus periods. The stimulation waveforms may have the characteristics of low-delta waveforms that may characterize non-REM stage 3 sleep. The stimulation period may last from about 4-8 seconds and the interstimulus period may last from about 10 to 30 seconds. A sleep induction session may include multiple alternating stimulation and non-stimulation periods. The sleep induction session may last for about 5 minutes to about 30 minutes.

System and method of sleep induction

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.

Adaptive continuous machine learning by uncertainty tracking

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.

System and method for noninvasive identification of cognitive and behavioral goals

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.

Apparatus, system, and methods for targeted memory enhancement during sleep

Fixation-locked measurement of brain activity generates time-coded cues indicative of whether an operator exhibited a significant cognitive response to task-relevant stimuli. The free-viewing environment is one in which the presentation of stimuli is natural to the task encompassing both pre- and post-fixation stimuli and the operator is allowed to move his or her eyes naturally to perform the task.

Fixation-locked measurement of brain responses to stimuli

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Cary, NC, United States of America

Patrick M Connolly