SYSTEM AND METHOD FOR MODELING NEUROLOGICAL ACTIVITY

1 . A system for modeling neurological activity, the system comprising:

a display; and

a computer comprising one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors, the program instructions being configured to:

receive electroencephalogram (EEG″) data generated by an EEG device coupled to a plurality of electrodes disposed on a brain, the EEG data comprising a plurality of waveforms representative of electrical activity detected by the plurality of electrodes over a period of time;

generate a graphical brain model representative of the brain;

convert the EEG data into a graphical EEG model representative of electrical activity;

integrate the EEG model with the brain model, thereby enabling visualization of and interaction with the EEG model within the context of the brain model; and

communicate the integrated EEG and brain model to the display.

2 . The system of claim 1 wherein the brain model, the EEG model, and the integrated model comprise a three-dimensional model.

3 . The system of claim 1 , wherein the electrical activity represented by the EEG data comprises a brain seizure.

4 . The system of claim 1 , wherein the computer is configured to convert the EEG data into a graphical EEG model representative of electrical activity by generating a heat map representative of the strength of the neurological activity at different positions within the brain.

5 . The system of claim 1 , wherein the computer is configured to convert the EEG data into a graphical EEG model by:

analyzing images of an anatomy including the EEG electrodes disposed on the anatomy;

automatically detecting positions of the EEG electrodes; and

correlating positions of the EEG electrodes with the plurality of waveforms.

6 . The system of claim 5 , wherein the computer is further configured to detect anomalies in the waveforms and to correlate the anomalies to positions of the EEG electrodes.

7 . The system of claim 1 , wherein the computer is configured to receive EEG data in real time directly from the EEG device.

8 . The system of claim 1 , wherein the computer is further configured to provide a recommendation for safely placing an electrode on a skull of the brain by analyzing the brain model to determine locations of vessels and determining at least one location on the skull for placing the electrode such that the proximity between the vessels and a trajectory stemming from the location of the electrode is minimized.

9 . The system of claim 8 , wherein the computer is further configured to map a surface of the brain such that the map is indicative of the proximity between the vessels and a trajectory stemming from a location on the map.

10 . The system of claim 1 , wherein the computer comprises an artificial intelligence computer configured to learn from historical epilepsy data and to provide suggestions for future epilepsy treatment, including at least one of electrode placement and anomaly detection.

11 . A method for modeling neurological activity, comprising the steps of:

receiving electroencephalogram (EEG″) data generated by an EEG device coupled to a plurality of electrodes disposed on a brain, the EEG data comprising a plurality of waveforms representative of electrical activity detected by the plurality of electrodes over a period of time;

generating a graphical brain model representative of the brain;

converting the EEG data into a graphical EEG model representative of electrical activity;

integrating the EEG model with the brain model, thereby enabling visualization of and interaction with the EEG model within the context of the brain model; and

communicating the integrated EEG and brain model to a display.

12 . The method of claim 11 wherein the brain model, the EEG model, and the integrated model comprise a three-dimensional model.

13 . The method of claim 11 , wherein the electrical activity represented by the EEG data comprises a brain seizure.

14 . The method of claim 11 , wherein converting the EEG data into a graphical EEG model representative of electrical activity comprises generating a heat map representative of the strength of the neurological activity at different positions within the brain.

15 . The method of claim 11 , wherein converting the EEG data into a graphical EEG model comprises:

analyzing images of an anatomy including the EEG electrodes disposed on the anatomy;

automatically detecting positions of the EEG electrodes; and

correlating positions of the EEG electrodes with the plurality of waveforms.

16 . The method of claim 15 , further comprising detecting anomalies in the waveforms and to correlate the anomalies to positions of the EEG electrodes.

17 . The method of claim 11 , wherein receiving the EEG data comprises receiving the EEG data in real time directly from the EEG device.

18 . The method of claim 11 , further comprising providing a recommendation for safely placing an electrode on a skull of the brain by analyzing the brain model to determine locations of vessels and determining at least one location on the skull for placing the electrode such that the proximity between the vessels and a trajectory stemming from the location of the electrode is minimized.

19 . The method of claim 11 , further comprising mapping a surface of the brain such that the map is indicative of the proximity between the vessels and a trajectory stemming from a location on the map.

20 . The method of claim 11 , further comprising using artificial intelligence techniques to learn from historical epilepsy data and to provide suggestions for future epilepsy treatment, including at least one of electrode placement and anomaly detection.