A system and method for diagnosing mental or emotional disorders is disclosed. An affective BCI component is incorporated into a closed loop, symptomresponsive psychiatric DBS system. A series of input data related to a brain of the patient is acquired while the patient performs a battery of behavioral tasks. From the patient's performance on the task battery, the system identifies what is abnormal for that individual patient in terms of functional domains. Patient-specific behavioral measurements are then linked to patterns of activation and de-activation across different brain regions, identifying specific structures that are the source of the patient's individual impairment.

A closed-loop brain computer interface (BCI) system for treating mental or emotional disorders with responsive brain stimulation is disclosed. The system includes an implanted module including a processor configured to process neural data acquired from one or more electrodes in communication with one or more brain regions of a patient. The implanted module is configured to deliver stimulation to electrodes in contact with the brain regions. An interface is in wireless communication with the implanted module and configured to receive the neural data from the implanted module. A controller processes the patient's brain and body signals to provide patient intentional control over the stimulation applied to the one or more electrodes and to control the stimulation.

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 medical magnetometer 10 comprising one or more induction coils 2 for detecting a time varying magnetic field of a region of a subject's body, such as the heart. Each coil has a maximum outer diameter of 4 to 7 cm, and a configuration such that the ratio of the coil's length to its outer diameter is at least 0.5, and the ratio of the coil's inner diameter to its outer diameter is 0.5 or less. Each induction coil 2 is coupled to a respective detection circuit comprising a low impedance pre-amplifier 3, a low pass filter 5, a notch filter 6 to remove line noise, and an averaging element 7. Each detection circuit produces an output signal 9 for use to analyze the time varying magnetic field of the region of the subject's body.

A system for detecting bioelectrical signals of a user comprising: a set of sensors configured to detect bioelectrical signals from the user, each sensor in the set of sensors configured to provide non-polarizable contact at the body of the user; an electronics subsystem comprising a power module configured to distribute power to the system and a signal processing module configured to receive signals from the set of sensors; a set of sensor interfaces coupling the set of sensors to the electronics subsystem and configured to facilitate noise isolation within the system; and a housing coupled to the electronics subsystem, wherein the housing facilitates coupling of the system to a head region of the user.

A system for monitoring biosignals of a user includes a first end region, positionable proximate a first ear of a user and including a first sensor array; a second end region, positionable proximate a second ear of the user and including a second sensor array; an intermediate region, positionable on a neck region of the user; a coupling element configured to couple the first and second end regions to the intermediate region; and a first attachment element and a second attachment element. The first attachment element couples the first end region to a head-mounted accessory and the second attachment element couples the second end region to the head-mounted accessory. The first end region includes a first electrode and the second end region includes a second electrode, such that there is a fixed distance between the first and second electrodes.

Systems and methods according to which a plurality of dipoles embedded within a simulated human brain of a simulated human head are stimulated with electricity. In one or more embodiments, the electricity with which the plurality of dipoles are stimulated is based on a recording of an animate human head. In one or more embodiments, stimulating the plurality of dipoles with the electricity causes the simulated human head to generate one or more electromagnetic properties that simulate same of the animate human head. In one or more embodiments, the one or more electromagnetic properties are detected from the simulated human head via: a first non-invasive technique; a second non-invasive technique that is different from the first non-invasive technique; or both the first non-invasive technique and the second non-invasive technique. For example, the one or more electromagnetic properties may be detected via both the first and second non-invasive techniques simultaneously.

A computer implemented method of classifying brain activity signals includes: receiving, as input to a neural network, input data comprising a plurality of brain activity signals; applying a first block to the input data to generate a plurality of first order wavelet scalograms, wherein the first convolutional block is configured to apply a plurality of Gabor filters to each of the plurality of brain activity signals, wherein each Gabor filter is associated with a learned bandwidth and learned frequency; applying one or more further blocks to the plurality of first order wavelet scalograms to generate a plurality of feature maps, wherein each further block comprises one or more convolutional layers; and applying a classification block to the plurality of feature maps, wherein the classification block is configured to generate one or more classifications of the plurality of brain activity signals from the plurality of feature maps.

A computer implemented method of classifying brain activity signals includes: receiving, as input to a neural network, input data comprising a plurality of brain activity signals; applying a first block to the input data to generate a plurality of first order wavelet scalograms, wherein the first convolutional block is configured to apply a plurality of Gabor filters to each of the plurality of brain activity signals, wherein each Gabor filter is associated with a learned bandwidth and learned frequency; applying one or more further blocks to the plurality of first order wavelet scalograms to generate a plurality of feature maps, wherein each further block comprises one or more convolutional layers; and applying a classification block to the plurality of feature maps, wherein the classification block is configured to generate one or more classifications of the plurality of brain activity signals from the plurality of feature maps.

A sensor unit for detecting brain current-induced magnetic fields in an unshielded environment has a plurality of gradiometer units configured for arrangement around a head of a user. Each gradiometer unit has two magnetometers which are arranged at a fixed distance from each other. Each magnetometer has a sensor medium and is configured to detect a magnetic field strength at a measurement location by reading a spin resonance in the sensor medium depending on the magnetic field strength. The sensor unit further includes at least one excitation light source for radiating light into the sensor media of the magnetometer. The sensor unit further incudes at least one signal processing unit for determining a magnetic field gradient at a gradiometer unit as a difference of the output signals of the two magnetometers of the gradiometer unit and for detecting a time course of the magnetic field gradient.