Examples include receiving and storing samples of target frequency bands filtered from EEG measurement of a subject's brain waves in an NFB training session. In an example, upon storing a time window of the samples, unsupervised adaptive adjusting an NFB reward threshold is automatic. The adjusting includes, in examples, determining neuromarker values in the time window, which indicate peak values of the target frequency bands over the time window. The adjusting computes the mean value of the neuromarker values and, utilizing same, automatically proceeds to unsupervised computing an adaptive adjusted reward threshold. The unsupervised computing, in examples, includes a multiplication product of a reward threshold adjustment factor, a training protocol value, and the computed mean value of the neuromarker values. Examples proceed to communicating the adaptive adjusted reward threshold to a controller for threshold based feedback reward to the NBF subject.

Examples include receiving and storing samples of target frequency bands filtered from EEG measurement of a subject's brain waves in an NFB training session. In an example, upon storing a time window of the samples, unsupervised adaptive adjusting an NFB reward threshold is automatic. The adjusting includes, in examples, determining neuromarker values in the time window, which indicate peak values of the target frequency bands over the time window. The adjusting computes the mean value of the neuromarker values and, utilizing same, automatically proceeds to unsupervised computing an adaptive adjusted reward threshold. The unsupervised computing, in examples, includes a multiplication product of a reward threshold adjustment factor, a training protocol value, and the computed mean value of the neuromarker values. Examples proceed to communicating the adaptive adjusted reward threshold to a controller for threshold based feedback reward to the NBF subject.

A method for improving the mental health of children, families and homes using “positive feedback loop” technology is described. One or more aspects of the method include obtaining a device; training the device to assign a predetermined threshold to at least one input, where the at least one input comprises a measurement of at least one condition of an ambient environment; measuring the at least one input; assigning a score to the measurement of the at least one condition of an ambient environment; evaluating whether the score exceeds the predetermined threshold; alerting at least one user when the score exceeds the predetermined threshold; and repeating the measuring, assigning, and evaluating steps until the score exceeds the predetermined threshold when the score does not exceed the predetermined threshold.

A computer-implemented method for detecting pathological brain activity patterns from a scalp electroencephalographic signal, the method including the steps of obtaining (A) an electroencephalographic signal as a function of multiple channels and time; identifying (C), for each channel, the zero-crossings of the electroencephalographic signal over a fixed threshold; generating a zero-crossing representation of at least a segment of the obtained electroencephalographic signal with the identified zero-crossings; obtaining (D) a reference family of real functions of time and channels from a zero-crossing statistical analysis of zero-crossing representation of pre-recorded electroencephalographic signals; calculating (E) a matching score by comparing the zero-crossing representation of a segment of the electroencephalographic signal with at least one reference function from the reference family of functions; and computing the matching score as a function of time by sliding the at least one reference function from the reference family of functions over the electroencephalographic signal.

A computer-implemented method for detecting pathological brain activity patterns from a scalp electroencephalographic signal, the method including the steps of obtaining (A) an electroencephalographic signal as a function of multiple channels and time; identifying (C), for each channel, the zero-crossings of the electroencephalographic signal over a fixed threshold; generating a zero-crossing representation of at least a segment of the obtained electroencephalographic signal with the identified zero-crossings; obtaining (D) a reference family of real functions of time and channels from a zero-crossing statistical analysis of zero-crossing representation of pre-recorded electroencephalographic signals; calculating (E) a matching score by comparing the zero-crossing representation of a segment of the electroencephalographic signal with at least one reference function from the reference family of functions; and computing the matching score as a function of time by sliding the at least one reference function from the reference family of functions over the electroencephalographic signal.

A surface electrode for patient monitoring includes a flexible substrate, a dry electrode on the substrate, and a wet electrode configured to contact an electrode gel in contact with a patient's skin. A conductive epoxy is arranged between the dry electrode and the wet electrode. The conductive epoxy is configured to protect the dry electrode from corrosion and transfer electrical potentials from the wet electrode to the printed dry electrode.

A holder apparatus of a bio-signal device comprises a holder, which comprises a pocket for a bio-signal processing device, an extension with a hollow, the hollow and the pocket forming a continuous cavity through the holder, a connector, and an elastic seal with a hole. A shape of the elastic seal is matched with a shape of the hollow of the extension at an interface of the pocket, and the hollow and a shape of the hole is matched with a shape of the connector for sealing an interface between the connector and the holder while the connector and the elastic seal are within the hollow and the connector is in contact with the elastic seal. Sealant filler fills the hollow of the extension and is in physical contact with the connector, which is in the hollow against the seal, the elastic seal and the sealant filler allowing the connector to mate electrically with a counter-connector moved within the cavity in a direction from the pocket toward the hollow.

The present invention relates to a syringe-injection-type brain signal measurement and stimulation structure, and a syringe injection method therefor, and provides a structure including a high-performance flexible element capable of minimizing a skull opening when inserted into the brain. Particularly, the present invention comprises: a flexible element, which includes a contact part making contact with a surface of a cortex so as to measure a signal generated in the brain or transmit an external stimulus to the brain, a transmitting/receiving part positioned between a skull and a skin, and a connection part for making a connection between the contact part and the transmitting/receiving part; and an integrated circuit connected to the transmitting/receiving part so as to transmit/receive a signal,

System and methods are disclosed that promote a sleep stage of a user. The systems and methods determine a current sleep stage of a user during a sleep session, with the user using a respiratory therapy system during the sleep session. The systems and methods further predict an undesired sleep stage upcoming for the user during the sleep session based, at least in part, on (i) one or more user parameters, information from one or more previous sleep sessions, or a combination thereof, and (ii) the current sleep stage. The systems and methods adjust one or more control parameters of the respiratory therapy system, of one or more devices in an environment of the user, or of a combination thereof to promote a desired sleep stage of the user, thereby optimizing sleep of the user.

A device includes a sensor signal input node and a high-pass filter stage. The high-pass filter stage includes an operational amplifier and a feedback integrator. The operational amplifier includes an input node coupled to the sensor signal input node. The feedback integrator is coupled between an output node of the operational amplifier and the input node of the operational amplifier to set a high-pass pole frequency of the high-pass filter stage.