Protocol and signatures for the multimodal physiological stimulation and assessment of traumatic brain injury
A brain injury and health assessment technology, applied in the fields of application, psychological devices, medical science, etc., can solve a large number of operator training, cumbersome equipment and other problems
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example 1
[0077] Example 1. Lehigh University Sports Medicine Concussion Study
[0078] In collaboration with NCAA Division 1 universities, several cohorts of subjects were enrolled under an Institutional Review Board-approved clinical protocol in which the first cohort of subjects (Cohort A) were clinically diagnosed with concussion (mTBI) or mild traumatic brain injury A second control group (Group B) was recruited without any concussion issues and served as uninjured control subjects (CTL), while other athletes from other sports were also recruited under institutional review board supervision (C group, etc.). Group B subjects were recruited within 24 hours of each Group A subject and asked to undergo the same scan sequence determined by their brain-injured teammates in time. Participants from groups A, B, C, and others were similarly scanned using an electronic REM module that included a single-electrode EEG device, as described in U.S. Patent Application Serial No. 14 / 233,292, file...
example 2
[0081] Example 2. Artifact detection preprocessing and signal processing of EEG data
[0082] EEG data were loaded into memory within MATLAB (Mathworks, Natick, MA) for preprocessing and signal processing activities.
[0083] Preprocessing occurs to remove samples containing artifacts. EEG data can be viewed as AC signals. EEG data were bandpass filtered using a least squares finite input response filter with stopband frequencies of 0.5 Hz and 42.0 Hz and passband frequencies of 1.0 Hz and 45.0 Hz. The stopband weight and passband weight are set to 1.0. The filter was applied twice to achieve a 2x attenuation and 0 phase shift, the first time in the time direction of signal collection and the second time in the reverse order of the collected data. The mean (X bars) and standard deviation (STD) of the filtered signal were calculated for all data collected in the recording session. The STD value of the signal is multiplied by a constant value set by the user or built into th...
Embodiment approach 3
[0087] Embodiment 3. Baseline Characterization
[0088] As previously mentioned, Figure 3 shows the data observed for a disclosed concussion device built into a clinical study. GSC results, SAC results, BESS results and 2x3 glance results showed wide variation according to scale type. In some cases, a lower bound (2x3 saccade error) effect as well as an upper bound (SAC) effect was observed.
[0089] Figure 4 shows the baseline characterization of EEG data in the five main frequency bands in each of the 16 tasks of the clinical protocol.
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