Abnormalities in electromagnetic fields in the heart, brain, and stomach, among other organs and tissues of the human body, can be indicative of serious health conditions. Described herein are methods, software, systems and devices for detecting the presence of an abnormality in an organ or tissue of a subject by analysis of the electromagnetic fields generated by the organ or tissue.

Abnormalities in electromagnetic fields in the heart, brain, and stomach, among other organs and tissues of the human body, can be indicative of serious health conditions. Described herein are methods, software, systems and devices for detecting the presence of an abnormality in an organ or tissue of a subject by analysis of the electromagnetic fields generated by the organ or tissue.

A system for classifying working memory task magnetoencephalography based on machine learning, including: the magnetoencephalography data acquisition module configured to acquire magnetoencephalography data of a subject in different working memory task states; the magnetoencephalography data preprocessing module configured to control the quality of magnetoencephalography data in different working memory tasks and separate noises and artifacts; the magnetoencephalography source reconstruction module configured for sensor signal analysis and source reconstruction analysis for the data processed by the magnetoencephalography data preprocessing module; and the machine learning classification module is configured to classify the working memory tasks to which the subjects belong by taking power time series as features. The present disclosure integrates the complete analysis pipeline from preprocessing to source reconstruction of the working memory magnetoencephalography data, classifies the working memory task magnetoencephalography data, and is of great significance to the study of working memory decoding and brain memory related mechanisms.

A system for classifying working memory task magnetoencephalography based on machine learning, including: the magnetoencephalography data acquisition module configured to acquire magnetoencephalography data of a subject in different working memory task states; the magnetoencephalography data preprocessing module configured to control the quality of magnetoencephalography data in different working memory tasks and separate noises and artifacts; the magnetoencephalography source reconstruction module configured for sensor signal analysis and source reconstruction analysis for the data processed by the magnetoencephalography data preprocessing module; and the machine learning classification module is configured to classify the working memory tasks to which the subjects belong by taking power time series as features. The present disclosure integrates the complete analysis pipeline from preprocessing to source reconstruction of the working memory magnetoencephalography data, classifies the working memory task magnetoencephalography data, and is of great significance to the study of working memory decoding and brain memory related mechanisms.

This invention is a head-worn device (e.g. headband, halo, or headset) with sensors (e.g. electrodes) which record brain activity. In an example, the device can be undulating with concave undulations which rest on the tops of a person's ears. In an example, the device can further comprise right side and left side ear prongs (e.g. arms, segments, or portions) which curve around the posterior and upper surfaces of a person's right and left ears.

This invention is a head-worn device (e.g. headband, halo, or headset) with sensors (e.g. electrodes) which record brain activity. In an example, the device can be undulating with concave undulations which rest on the tops of a person's ears. In an example, the device can further comprise right side and left side ear prongs (e.g. arms, segments, or portions) which curve around the posterior and upper surfaces of a person's right and left ears.

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.

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 method for verifying transactions in a metaverse environment is disclosed. The method comprises: detecting a first user involved in an activity using user biometrics, wherein the first user is in focused attention state or addiction state; identifying a second user interacting with the first user during a transaction; identifying an intention of the second user interacting with the first user; determining a presence of an abnormality in a physiological state of the first user; and recommending to the first user to focus on the transaction.

A method for verifying transactions in a metaverse environment is disclosed. The method comprises: detecting a first user involved in an activity using user biometrics, wherein the first user is in focused attention state or addiction state; identifying a second user interacting with the first user during a transaction; identifying an intention of the second user interacting with the first user; determining a presence of an abnormality in a physiological state of the first user; and recommending to the first user to focus on the transaction.