Abstract: Methods and systems for inducing sleep in a subject. The method includes emitting, by a transducer, a first neuromodulatory signal to the subject based on a signal parameter during a first time period. A first biomarker signal of the subject is measured by a first detector a during a second time period. The second time period is subsequent to or at least partially overlaps with the first time period. The method includes determining, by a controller, a characteristic of the first biomarker signal and adjusting, by the controller, the signal parameters based on the characteristic thereby forming an adjusted parameter. Based on the adjusted parameter, the method includes performing at least one of the following: emitting, by the transducer, a second neuromodulatory signal to the subject during a third time period based on the adjusted parameter; or ceasing further emission of neuromodulatory signals based on the adjusted parameter.

Abstract: 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.

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 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.

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: 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: Cognitive biometrics comprises augmenting the richness of biometric signatures that can be extracted from mouse dynamics by introducing perturbations in the response of the computer mouse and measuring the motor responses of the individual user. User responses to unexpected and subtle perturbations (e.g., small changes in mouse velocity, position and/or acceleration) reveal new unique sources of information in the mouse movement signal that reflect the user's cognitive strategies and are inaccessible via existing mouse biometric technologies. A user's response to these perturbations contains information about intrinsic cognitive qualities that can be used as a robust biometric for personal authentication and to support profiling of the individual (e.g., gender, cultural background, cognitive or emotional state, cognitive quality etc.).