Cognitive function assessment in a patient

1. A method for analysis of the extent of conscious awareness and/or likelihood of recovery of a patient comprising:

applying to the patient a sensory stimulus sequence generated by a stimulator;

while applying the stimulus sequence, carrying out an EEG or MEG on the patient to record waveform signals from an array of sensors on, in, or near the head of the patient;

wherein the stimulus sequence comprises a compressed stimulus sequence that elicits at least five indicators across a spectrum of brain functions comprising sensory processing, perceptual processing, attention/alerting mechanisms, memory retrieval, and language;

using software provided in a processor to process the waveform signals in order to locate waveform peaks, identify the evoked responses contained in the waveform and obtain quantitative measures of these evoked responses;

wherein the software uses an automated peak localization method that performs a mathematical decomposition of an averaged waveform to identify all peaks in the waveform signals; and wherein the software then applies identification criteria to choose a candidate peak of interest;

and using the software to generate and communicate scores based on the quantitative measures that are indicative of the extent of conscious awareness and/or likelihood of recovery of the patient.

2. The method according to claim 1 wherein the compressed stimulus sequence takes less than 5 minutes.

3. The method according to claim 1 wherein the software generates scores comprising a diagnostic score and wherein points in the diagnostic score are allotted based on said evoked responses related to said plurality of brain functions.

4. The method according to claim 1 wherein the software generates scores comprising a diagnostic score and wherein points in the diagnostic score are allotted based on the statistical assessment of differences between quantitative measures of the patient's evoked responses and normative values from a database.

5. The method according to claim 1 wherein the software generates scores comprising a prognostic score and wherein points in the prognostic score are allotted based on the statistical relationships between averaged waveform features and historical outcomes of patients diagnosed with the same condition.

6. The method according to claim 1 , wherein the peak localization method identifies all peaks by determining the zero crossings of the averaged waveform's first and second derivatives, yielding peak and inflection points of the waveform's curvature.

7. The method according to claim 1 , wherein the peak localization method employs an amplitude thresholding method whereby small peaks are rejected from further analysis.

8. The method according to claim 1 , wherein the identification criteria are applied that describe generic ERP/ERF component characteristics based on several basic features, such as experimental condition, latency, and polarity.

9. The method according to claim 1 , wherein the peak localization method takes into account the peak's shape and curvature, its relationship to peaks in other averaged waveforms for different experimental conditions, and the shape and curvature of the averaged waveform between this peak and its neighboring peak.

10. The method according to claim 1 , wherein the software uses an adaptive pattern recognition process to perform a series of automated and iterative adjustments to the previously described identification criteria to accommodate, where possible, the differences between individuals.

11. The method according to claim 1 , wherein the pattern recognition process to examine relationships with peaks in waveforms for different experimental conditions, such as relative amplitudes and crossover points between the waveforms.