Electroencephalography (EEG) records electrical activity and brain waves using electrodes placed on the scalp. EEG is useful because it reflects how the many different neurons in the brain communicate with each other via electrical impulses. The technology may be a primary component in mapping the brain and eventually enabling a subject to control his surroundings with active brain impulses. EEG has excellent time resolution – it can take thousands of snapshots of electrical activity across multiple sensors within a single second. This renders EEG an ideal technology to study the precise time-course of cognitive and emotional processing underlying behavior. The downside of this is the sheer volume of data produced, the ability of computers to adequately represent the data, and time required for researchers to interpret it. The large number of measures reduces the overall statistical power of any analysis and increases errors. It is also very difficult to tell if data are coming from the same source (a particular neuron or even general area). If EEG systems are to be useful in real-life applications (such as in moving vehicles with operator control), there is a need for a method and device for generating a global measure for EEG analysis. Further, there is an advantage to a global measure of EEG activity with sources located by cortical structures that form cerebral networks relatable to cognitive functions.
Army researchers have developed an innovative methodology for estimating brain activity from complex and messy EEG data. The methodology uses a novel model of the human brain that simplifies a network of cerebral cortical sources as nodes and assumes that node-excitation functions can be determined from EEG measurements. The node-excitation functions are computed from independent component analysis, and also parameterized by multivariate spectrum analysis of the time-series network that is formed by the nodes of the cortical network.
A key advantage of this invention is that a global measure is generated for the node network along with a single-effect measure for each node, thereby increasing the statistical power of the application. That global measure is relatable to cognitive functions through the node effect measures. This enables a component of an eventual automated system that electronically aids tasks performed by a person, by providing an estimate of brain functions from EEG measurements.
This patent is related to US patent 9,116,835 filed Sep. 29, 2014, titled METHOD AND APPARATUS FOR ESTIMATING CEREBRAL CORTICAL SOURCE ACTIVATIONS FROM ELECTROENCEPHALOGRAMS.
- Simplifies EEG data interpretation
- Provides a basis for future control of objects via EEG activity
- Could be used as a brain function diagnostic
- US patent 9,107,595 available for licensing
- Potential for collaboration with Army researchers