Systems and methods for measuring neurologic function via sensory stimulation

A computer-implemented method for training a machine learning model to identify a brain state associated with mild traumatic brain injury (mTBI) and a brain state associated with non-mTBI, the method comprising:

• obtaining EEG data from a plurality of electrodes attached to each of a first plurality of subjects and to each of a second plurality of subjects, wherein the first plurality of subjects are known to have mTBI, wherein the second plurality of subjects are known to be mTBI unafflicted, and wherein the EEG data for each respective subject in the first plurality of subjects and the second plurality of subjects comprises EEG data for each respective subject at rest and sensory evoked response EEG data for each respective subject;
• applying Principal Component Analysis (PCA) to the obtained EEG data for each respective subject in the first and second plurality of subjects, comprising combining EEG data from different electrodes attached to each respective subject at different time points and in different frequencies in order to produce a reduced amount of EEG data for each respective subject;
• collapsing the reduced amount of EEG data for each respective subject into a feature vector for each respective subject, wherein the feature vector comprises a plurality of features from the EEG data for each respective subject, and wherein each feature vector identifies that the respective subject is known to have mTBI or is known to be mTBI unafflicted;
• and
• training the machine learning model to identify a brain state associated with mTBI and a brain state associated with non-mTBI using the feature vector for each respective subject in the first and second plurality of subjects.