Electrical non-invasive brain stimulation (NIBS) delivers weak electrical currents to the brain via electrodes that are affixed to the scalp. NIBS can excite or inhibit the brain in areas that are impacted by that electrical current during and for a short time following stimulation. Electrical NIBS can be used to change brain structure in terms of increasing white matter integrity as measured by diffusion tensor imaging. Together the electrical NIBS can induce changes in brain structure and function. The present methods and devices are adaptable to and configurable for facilitating the enhancement of brain performance, and the treatment of neurological diseases and tissues. The present methods and devices are advantageously designed to utilize modern electrodes deployed with, inter alia, various spatial arrangements, polarities, and current strengths to target brain areas or networks to thereby enhance performance or deliver therapeutic interventions.

A system and method for identifying sleep states of a subject are provided. In some aspects, the method includes acquiring physiological data from a subject over a sleep period using sensors positioned about the subject, and assembling the physiological data into time-series datasets. The method also includes selecting a temporal window in which signals associated with the time-series datasets are substantially stationary, computing a time bandwidth product based on a selected spectral resolution and the selected temporal window, and determining a number of tapers using the computed time bandwidth product. The method further includes computing a spectrogram using the determined number of tapers and the time-series datasets, analyzing the spectrogram to identify signatures of sleep in the subject, and generating, using the identified signatures, a report indicative of sleep states of the subject.

One aspect of the apparatus comprising, a sensor configured to acquire a biological signal of a user, and a controller configured to, determine whether the biological signal is continuously outside a predetermined range for a first time period, after determining that the biological signal has been continuously outside the predetermined range for the first time period, then determine whether the biological signal is inside the predetermined range, and activate an alarm if the controller has determined that (i) the biological signal has been outside the predetermined range for the first time period, and (ii) the biological signal has been continuously inside the predetermined range for a second time period, the second time period being longer than the first time period.

Methods, systems, and devices for controlling external devices are described. A method may include receiving physiological data associated with a user from a wearable device, and identifying one or more physiological states, physical activities, or both, associated with the user based on the physiological data. Physiological states may include physiological states associated with waking up, falling asleep, anxiety, relaxation, and the like. The method may further include transmitting an instruction to one or more external devices based on the one or more physiological states, physical activities, or both, where the instruction is configured to selectively modify one or more operational parameters associated with the one or more external devices.

A healthcare apparatus includes a BCG sensor; a camera; and a processor configured: to detect a ROI) corresponding to the face from the color facial image; to convert the detected first color image into a black and white image to acquire a first black and white image; to convert the detected second color image into a black and white image to acquire a second black and white image; to apply the acquired first black and white image and the acquired second black and white image to a predetermined trained algorithm model to output a remote photoplethysmography (rPPG) signal waveform of the subject; to calculate a first stress index based on the first heart rate variability; to calculate a second stress index based on the second heart rate variability; and to output a stress index of the subject based on the first stress index and the second stress index.

Determining low power frequency range information from spectral data. Raw signal data can be adjusted to increase dynamic range for power within low power frequency ranges as compared to higher-power frequency ranges to determine adjusted source data valuable for acquiring low power frequency range information. Low power frequency range information can be used in the analysis of a variety of raw signal data. For example, low power frequency range information within electroencephalography data for a subject from a period of sleep can be used to determine sleep states. Similarly, automated full-frequency spectral electroencephalography signal analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, and determining the effect of medication on sleep states.

An Example of a system for providing a patient with pain management may include a sleep monitoring circuit, a pain relief device, and a control circuit. The sleep monitoring circuit may be configured to sense one or more sleep signals from the patient and to determine a sleep state of the patient using the one or more sleep signals. The one or more sleep signals may include one or more physiological signals corresponding to the sleep state of the patient. The pain relief device may be configured to deliver one or more pain relief therapies. The control circuit may be configured to control the delivery of the one or more pain relief therapies using therapy parameters and to adjust the therapy parameters based on the determined sleep state.

Methods and systems for remote sleep monitoring are provided. Such methods and systems provide non-contact sleep monitoring via remote sensing or radar sensors. In this regard, when processing backscattered radar signals from a sleeping subject on a normal mattress, a breathing motion magnification effect is observed from mattress surface displacement due to human respiratory activity. This undesirable motion artifact causes existing approaches for accurate heart-rate estimation to fail. Embodiments of the present disclosure use a novel active motion suppression technique to deal with this problem by intelligently selecting a slow-time series from multiple ranges and examining a corresponding phase difference. This approach facilitates improved sleep monitoring, where one or more subjects can be remotely monitored during an evaluation period (which corresponds to an expected sleep cycle).

Some systems, devices and methods detailed herein provide a system for use in determining metrics of a subject. The system can provide, as an output, a function-metric value determined based on a defined relationship between physiological measures and a chronological age.

Sleep performance systems and methods of using the same are disclosed. The sleep performance systems can improve the quality of sleep by making one or more recommendations to the subject for increasing a sleep quality score. The sleep performance systems can have one or more electroencephalography (EEG) electrodes configured to measure a subject's brain activity during sleep. The sleep performance systems can have a processor configured to quantify the quality of the subject's slow-wave sleep by determining one or more sleep performance scores associated with the measured brain activity. The sleep performance systems can recommend and/or activate sleep improvement programs based on various threshold scores.