Systems methods are described for modifying sleep patterns based on biofeedback. The method may include identifying a target sleep pattern from a plurality of candidate sleep patterns, receiving biofeedback information from a user, identifying a stimulus pattern based on the target sleep pattern and the biofeedback information, and providing at least one stimulus to the user based on the stimulus pattern.

Systems methods are described for modifying sleep patterns based on biofeedback. The method may include identifying a target sleep pattern from a plurality of candidate sleep patterns, receiving biofeedback information from a user, identifying a stimulus pattern based on the target sleep pattern and the biofeedback information, and providing at least one stimulus to the user based on the stimulus pattern.

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.

The disclosed embodiments include systems and methods to provide a patient suffering from at least one communication and memory impairment with positive stimuli. In one of such embodiments, the method includes obtaining data indicative of a current condition of a first patient. The method also includes determining, based on prior patient data of the first patient, a first stimulus that triggered a positive response from the first patient while the first patient was in a condition similar to the current condition. The method further includes providing a first recommendation for the first patient to experience the first stimulus for display on an electronic device. The method further includes receiving a first response triggered by the first stimulus. The method further includes storing data indicative of the first response in a storage medium.

Method and devices are provided for treating subjects with Transcranial Magnetic Stimulation (TMS). According to some approaches, the methods and devices are configured for the treatment of ongoing seizures. Other approaches relate to the use of TMS as an antiepileptogenic or for use in determining preferential placement of intracranial probes.

An automatic evolution method used for a brainwave database which collects physiological information of brainwaves about healthy and clinical groups, the automatic evolution method includes: classifying the physiological information of brainwaves collected by the brainwave database according to data characteristics; establishing a feedback algorithm model based on a neural network architecture according to the physiological information of brainwaves classified by the parameters; using the feedback algorithm model to input a subject's physiological information of brainwaves; measuring an accuracy of the subsequent performance data calculated by the feedback algorithm model; and incorporating the physiological information of brainwaves of the subject into the brainwave database, establishing an updated feedback algorithm model based on an updated neural network architecture, and feeding a comparison result generated by the updated feedback algorithm model back to the subject.

A method for context-aware self-calibration includes measuring for a plurality of time segments, at least one feature of at least one biosignal or each of at least one channel. Each biosignal is created in response to a user imagining an intended direction for each time segment. An object is moved along an actual decoded direction determined by an output of a decoder configured to correlate for each time segment the at least one feature to the intended direction. The decoder self-calibrates to minimize for each time segment, an error between the actual decoded direction, and the intended direction inferred subsequent to the respective time segment.

A non-invasive system and method are provided. Brain activity of a user is detected using a non-invasive brain interface when the user is exposed to an external stimulus. The user is determined to be negatively primed by the external stimulus based on the detected brain activity. An alert that the user is being negatively primed by the external stimulus is automatically provided. A tagged training session may be automatically provided to the user in response determining that the user has a negative mental state, thereby promoting a positive mental state of the user. A training session list containing the tagged training session may be automatically modified based on the determined mental state of the user.

A non-invasive system and method are provided. Brain activity of a user is detected using a non-invasive brain interface when the user is exposed to an external stimulus. The user is determined to be negatively primed by the external stimulus based on the detected brain activity. An alert that the user is being negatively primed by the external stimulus is automatically provided. A tagged training session may be automatically provided to the user in response determining that the user has a negative mental state, thereby promoting a positive mental state of the user. A training session list containing the tagged training session may be automatically modified based on the determined mental state of the user.