Abstract: System and method for digitized digit symbol substitution test (DSST) are disclosed. In an example, a display area of a digitized DSST device is partitioned into multiple bins. Further, a series of number symbol pairs is displayed as a lookup table on top of the display, termed as a lookup area. Furthermore, a question and answer (QA) pair corresponding to the series of number symbol pairs to an examinee in multiple trials. In addition, feature values for the QA pair are computed in each of the multiple bins in the trials, wherein the feature values comprise a response time and an accuracy of response by the examinee. Moreover, probabilities of the feature values are determined in each of the multiple bins. Also, an entropy value based on the probabilities of the feature values is computed in each of the multiple bins providing information on distribution.

Abstract: A method and system for determining cognitive load of a person using a modified baseline is provided. The person is asked to perform a series of activities including staying in eye closed rest state and baseline state and performing a trial state. Simultaneously, EEG signal and GSR signal of the person are captured. The EEG signal and the GSR signal are preprocessed and segmented. The EEG and GSR signals are then used to determine a first set and a second set of inactive states from the baseline interval and the rest interval. The most inactive window is then identified out of the first set of inactive states. The most inactive window is determined from the rest interval of the person. The inactive window is used as the modified baseline to measure the cognitive load of the person.

Abstract: This disclosure relates generally to stress classification and quantification, and more particularly to system and method for classification and quantitative estimation of cognitive stress from analysis of keystrokes and signals derived from physiological sensors. In one embodiment, a method includes obtaining, while a user is engaged in performance of a task, physiological signals from physiological sensors accessible to the user. Feature sets are identified from the physiological signals which correlate with cognitive stress experienced by the user. Using a regression model, a stress indicator metric comprising a quantitative estimate of the cognitive stress is predicted. The regression model is trained using the feature sets and independently determined quantitative estimates of cognitive stress used as a ground truth to output the value of the stress indicator metric.

Abstract: System and method for digitized digit symbol substitution test (DSST) are disclosed. In an example, a display area of a digitized DSST device is partitioned into multiple bins. Further, a series of number symbol pairs is displayed as a lookup table on top of the display, termed as a lookup area. Furthermore, a question and answer (QA) pair corresponding to the series of number symbol pairs to an examinee in multiple trials. In addition, feature values for the QA pair are computed in each of the multiple bins in the trials, wherein the feature values comprise a response time and an accuracy of response by the examinee. Moreover, probabilities of the feature values are determined in each of the multiple bins. Also, an entropy value based on the probabilities of the feature values is computed in each of the multiple bins providing information on distribution.

Abstract: This disclosure relates generally to stress classification and quantification, and more particularly to system and method for classification and quantitative estimation of cognitive stress from analysis of keystrokes and signals derived from physiological sensors. In one embodiment, a method includes obtaining, while a user is engaged in performance of a task, physiological signals from physiological sensors accessible to the user. Feature sets are identified from the physiological signals which correlate with cognitive stress experienced by the user. Using a regression model, a stress indicator metric comprising a quantitative estimate of the cognitive stress is predicted. The regression model is trained using the feature sets and independently determined quantitative estimates of cognitive stress used as a ground truth to output the value of the stress indicator metric.

Abstract: A method and system for determining cognitive load of a person using a modified baseline is provided. The person is asked to perform a series of activities including staying in eye closed rest state and baseline state and performing a trial state. Simultaneously, EEG signal and GSR signal of the person are captured. The EEG signal and the GSR signal are preprocessed and segmented. The EEG and GSR signals are then used to determine a first set and a second set of inactive states from the baseline interval and the rest interval. The most inactive window is then identified out of the first set of inactive states. The most inactive window is determined from the rest interval of the person. The inactive window is used as the modified baseline to measure the cognitive load of the person.