The present application provides a left and right brain recognition method and device, and relates to the field of wearable devices. The method comprises: in response to that a user listens to an audio content meeting a predetermined condition, acquiring first brain electrical information of a first hemisphere of the user; and recognizing that the first hemisphere is a left brain or a right brain according to the first brain electrical information and reference information. The method and the device provide a left and right brain recognition method, facilitate a device that the user wears to perform automatic setting according to a recognition result, and enhance user experience.

A medical device senses electrical activity within a patient and, in some embodiments, delivers stimulation to the patient via a plurality electrical paths, which include electrodes and associated conductors of one or more leads. The medical device determines whether a symptomatic event, such as a seizure, is detected based on the sensed electrical activity, and measures the impedance of one or more of the paths in response to the determination. If the medical device identifies a dysfunctional electrical path based on the measured impedance, the device may, as examples, disable the dysfunctional electrical path, or modify a stimulation or sensing program to not use the dysfunctional electrical path. In this manner, the medical device may identify inaccurate symptomatic event detection and, where the device delivers a therapy in response to such detection, such as stimulation via the electrical paths, avoid inappropriate therapy delivery.

A medical imaging decision support system is provided that can conduct, and help medical professionals conduct multi-factor brain analysis. Data for disparate processing modes (for example, EEG, MRI, etc.) can be input to the system, processed in parallel in a cloud environment, and the results can be rendered in a thin client (for example, browser) for a user's rapid multi-modal evaluation of a brain.

An initial set of parameters for operating one or more detection tools is automatically derived and subsequently adjusted so that each detection tool is more or less sensitive to signal characteristics in a region of interest. Detection tool(s) may be applied to physiological signals sensed from a patient (such as EEG signals) and may be configured to run in an implanted medical device that is programmable with the parameters to look for rhythmic activity, spiking, and power changes in the sensed signals, etc. A detection tool may be selected and parameter values derived in a logical sequence and/or in pairs based on a graphical representation of an activity type which may be selected by a user, for example, by clicking and dragging on the graphic via a GUI. Displayed simulations allow a user to assess what will be detected with a derived parameter set and then to adjust the sensitivity of the set or start over as desired.

A neurostimulation device includes a plurality of electrodes adapted to be electrically connected to a subject to receive multichannel electrical signals from the subject's brain, a multichannel seizure detection unit electrically connected to the plurality of electrical leads to receive the multichannel electrical signals, and a neurostimulation unit in communication with the multichannel seizure detection unit. The plurality of electrodes are at least three electrodes such that the multichannel electrical signals are at least three channels of electrical signals, and the multichannel seizure detection unit detects a presence of a seizure based on multichannel statistics from the multichannel electrical signals including higher order combinations than two-channel combinations.

The system of the present invention includes at least one brain signal capturing device, at least one memory device for storing software instructions, and at least one processor. The processor is in communication with the brain signal capturing device and executes the instructions stored on the memory. The instructions include the steps of analyzing a user's brain signal patterns and searching based on the user's brain signals.

A system and method for monitoring a patient suspected of experiencing a state of unconsciousness are provided. In certain aspects, the method includes assembling physiological data, obtained from a plurality of sensors placed on a subject, into sets of time-series data, separating, from the sets of time-series data, a plurality of electroencephalogram signals, and determining, from the plurality of electroencephalogram signals, at least one of frequency information and amplitude information. The method can also include identifying, using the at least one of the frequency information and the amplitude information, spatiotemporal signatures indicative of a likelihood of arousing the patient to consciousness by applying an external stimulus and generating a report indicating the likelihood of arousing the patient to consciousness by applying the external stimulus.

A method and apparatus using brainwaves to control real objects is provided. The method and apparatus comprise using sensors to detect the brain's electrical signals and transmit at least two brainwaves to an apparatus that converts the brainwaves into a format usable by a signal processor. The signal processor determines a coherence between portions of the brainwaves, typically in the frequency domain, and compares the coherence values, which change rapidly from moment to moment, to thresholds. Based on the comparison of the coherence value to the thresholds, which are adjusted over time based on feedback relating to success, a control signal is developed that can be sent to a real object to control 3 dimensional motion of the control object.

The present disclosure pertains to a system configured to detect slow waves in a subject during a sleep session. The system generates output signals conveying information related to brain activity of the subject. The system is configured to detect individual sleep stages of the subject, the individual sleep stages including a deep sleep stage; and, responsive to detecting the deep sleep stage, generate a harmonic representation of the output signals for a period of time during the sleep session that includes the deep sleep stage; identify two or more points of significance on the harmonic representation of the output signals; and analyze a shape of the harmonic representation of the output signals around the two or more points of significance to determine whether the shape of the harmonic representation of the output signals around the two or more points of significance corresponds to a shape of a slow wave.

A method of predicting substance levels from EEG data is disclosed. The method includes analyzing EEG data to obtain the average power for each of a plurality of predetermined frequency bands and calculating a value from the average powers derived for each frequency band, said value being calculated by combining the average powers for each frequency band by dividing and/or multiplying according to a predetermined order. The method further includes obtaining an estimate of the hormone level from the equation Y=bX+C, where Y is the substance level to be predicted, X is the value and b and C are constants, wherein the substance is selected from hormones, neuro transmitters and bio markers.