Systems and methods for evaluating neurologic function of a subject are described. An odorant, auditory and/or somatosensory generator is configured to deliver a sensory stimulation to the subject, a plurality of electrodes are configured to be attached to the subject, and a handheld EEG control unit is configured to control the odorant, auditory and/or somatosensory generator, process the neural signals from the plurality of electrodes and generate an assessment of neurologic function of the subject.

In some embodiments, a method of increasing cognitive function and/or treating a disease or disorder associated with decreased cognitive function in a subject is provided. Treating a subject with an glutamate receptor agonist can include identifying the subject as an glutamate receptor agonist responder. The method can further include obtaining an electroencephalogram (EEG) signals from the subject. The method can further include measuring one or more EEG metrics, thereby identifying the subject as a glutamate receptor agonist. Further provided non-transitory processor-readable medium storing code with instructions for identify glutamate receptor agonist responders

In some embodiments, a method of increasing cognitive function and/or treating a disease or disorder associated with decreased cognitive function in a subject is provided. Treating a subject with an glutamate receptor agonist can include identifying the subject as an glutamate receptor agonist responder. The method can further include obtaining an electroencephalogram (EEG) signals from the subject. The method can further include measuring one or more EEG metrics, thereby identifying the subject as a glutamate receptor agonist. Further provided non-transitory processor-readable medium storing code with instructions for identify glutamate receptor agonist responders

A method receives continuous EEG data for a long duration of time from at least one electrode intracranially implanted in a subject. The method determines a current or predicted brain state from the EEG data using an artificial intelligence (AI) model.

A method receives continuous EEG data for a long duration of time from at least one electrode intracranially implanted in a subject. The method determines a current or predicted brain state from the EEG data using an artificial intelligence (AI) model.

A flexible sheet for neurostimulation is described having a flexible non-conductive substrate matrix in which electrodes are embedded along a lower surface. Electrically conductive wires extend from the electrodes through the flexible substrate to another exterior surface of the substrate. Methods of making the flexible sheet and making a device using the flexible sheet are also disclosed.

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 measurement apparatus includes a first biometric sensor configured to measure, as a biological signal, at least one of a brain wave signal and a pulse wave signal of a living body, a second biometric sensor configured to measure a respiratory rate of the living body per unit time, a differential operation unit configured to calculate a differential value of a periodic feature of the biological signal, an activity level analysis unit configured to analyze an activity level of an autonomic nerve of the living body based on the differential value, and an activity level correction unit configured to correct the activity level of the autonomic nerve by eliminating a component caused by respiration of the living body and included in the activity level of the autonomic nerve based on the respiratory rate of the living body per unit time.

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.