Brain computer interface (bci) system based on gathered temporal and spatial patterns of biophysical signals

#Communication and control of the environment is important to everyday life. In particular, disabled people have gone to extraordinary lengths to communicate. A Brain Computer Interface (BCI) enables direct communication between the brain and a computer or electronic device. BCI could also be applied in applications where existing communication methods exhibit shortcomings, e.g. in noisy industrial applications, military environments where stealth and movement are constrained, etc. In the consumer market. BCI may provide advantages as a gaming or entertainment interface or may speed existing or enable entirely new computer-user interactions.

#Regardless of function, each part of the brain is made up of nerve cells called neurons. As a whole, the brain is a dense network that includes about 100 billion neurons. Each of these neurons communicates with thousands of others in order to regulate physical processes and to produce thought. Neurons communicate either by sending electrical signals to other neurons through physical connections or by exchanging chemicals called neurotransmitters. When they communicate, neurons consume oxygen and glucose that is replenished through increased blood flow to the active regions of the brain.

#Advances in brain monitoring technologies allow observations of the electric, chemical, fluidic, magnetic, etc. changes as the brain processes information or responds to various stimuli. Research continues in brain computer interface (BCI) systems that could provide new communication and control options for a wide variety of users and applications. Through brain activity monitoring, a database of characteristic mental profiles may be gathered.

#FIG. 1 illustrates a system for providing psychological and sociological matching through correlation of brain activity similarities according to an embodiment;

#FIG. 2 illustrates a system that compares the brain anatomy and activity to known information and prerecorded signatures to identify and assign a probability of uniqueness to an individual, according to an embodiment;

#FIG. 3 illustrates a consumer grade wearable system for gathering BCI input, according to an embodiment;

#According to embodiments described herein, brain/skull anatomical characteristics, such as gyrification, cortical thickness, scalp thickness, etc., may be used for identification/authentication purposes. Measured stimuli/response brain characteristics, e.g., anatomic and physiologic, may be translated into specific patterns to categorize a brain for identification and/or authentication purposes. People may be correlated according to similarities in brain activity in response to stimuli. Information on other brain signatures, e.g., anatomic and physiologic, and comparisons to similar brains may be used to predict a brain response to a new stimulus and for identification and/or authentication purposes. Brain identification and/or authentication techniques in combination with other identification and/or authentication techniques, e.g., password, other biometric parameters, may be used to increase the identity/authentication sensitivity and specificity.

#More recent technological advances in sensing brain or neuronal activity signal present opportunities for the creation of more sophisticated BCI usages and systems. Based on gathered temporal and spatial patterns of biophysical signals, it is now possible to measure and identify psychological states or mental representations of a person. For example, temporal and spatial patterns of biophysical signals may be obtained through, but not limited to, electrical, fluidic, chemical, magnetic sensors.

#Examples of devices that gather electrical signals include electroencephalography (EEG). EEG uses electrodes placed directly on the scalp to measure the weak (5-100 μV) electrical potentials generated by activity in the brain. Devices that measure and sense fluidic signals include Doppler ultrasound and devices that measure chemical signals include functional near-infrared spectroscopy (fNIRS). Doppler ultrasound measures cerebral blood flow velocity (CBFV) in the network of arteries that supply the brain. Cognitive activation produces increases in CBFV within these arteries that may be detected using Doppler ultrasound. fNIRS technology works by projecting near infrared light into the brain from the surface of the scalp and measuring optical changes at various wavelengths as the light is refracted and reflected back to the surface. The fNIRS effectively measures cerebral hemodynamics and detects localized blood volume and oxygenation changes. Since changes in tissue oxygenation associated with brain activity modulate the absorption and scattering of the near infrared light photons to varying amounts, fNIRS may be used to build functional maps of brain activity. Devices that measure magnetic signals include magnetoencephalography (MEG). A MEG measures magnetic fields generated by the electrical activity of the brain. MEG enables much deeper imaging and is much more sensitive than EEG because the skull is substantially transparent to magnetic waves.