Association between Prefrontal fNIRS signals during Cognitive tasks and College scholastic ability test (CSAT) scores: Analysis using a quantum annealing approach

Academic achievement is a critical measure of intellectual ability, prompting extensive research into cognitive tasks as potential predictors. Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIRS), offer insights into brain hemodynamics, allowing understanding of the link between cognitive performance and academic achievement. Herein, we explored the association between cognitive tasks and academic achievement by analyzing prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection algorithm was applied to fNIRS data to identify cognitive tasks correlated with CSAT scores. Twelve features (signal mean, median, variance, peak, number of peaks, sum of peaks, slope, minimum, kurtosis, skewness, standard deviation, and root mean square) were extracted from fNIRS signals at two time windows (10- and 60-second) to compare results from various feature variable conditions. The feature selection results from the QA-based and XGBoost regressor algorithms were compared to validate the former's performance. In a three-step validation process using multiple linear regression models, correlation coefficients between the feature variables and the CSAT scores, model fitness (adjusted R2), and model prediction error (RMSE) values were calculated. The quantum annealer demonstrated comparable performance to classical machine learning models, and specific cognitive tasks, including verbal fluency, recognition, and the Corsi block tapping task, were correlated with academic achievement. Group analyses revealed stronger associations between Tower of London and N-back tasks with higher CSAT scores. Quantum annealing algorithms have significant potential in feature selection using fNIRS data, and represents a novel research approach. Future studies should explore predictors of academic achievement and cognitive ability.Comment: 42 pages, 11 table

EEG-based classification of visual and auditory monitoring tasks

Using EEG signals for mental workload detection has received particular attention in passive BCI research aimed at increasing safety and performance in high-risk and safety-critical occupations, like pilots and air traffic controllers. Along with detecting the level of mental workload, it has been suggested that being able to automatically detect the type of mental workload (e.g., auditory, visual, motor, cognitive) would also be useful. In this work, a novel experimental protocol was developed in which subjects performed a task involving one of two different types of mental workload (specifically, auditory and visual), each under two different levels of task demand (easy and difficult). The tasks were designed to be nearly identical in terms of visual and auditory stimuli, and differed only in the type of stimuli the subject was monitoring/attending to. EEG power spectral features were extracted and used to train linear and non-linear classifiers. Preliminary results on six subjects suggested that the auditory and visual tasks could be distinguished from one another, and individually from a baseline condition (which also contained nearly identical stimuli that the subject did not need to attend to at all), with accuracy significantly exceeding chance. This was true when classification was done within a workload level, and when data from the two workload levels were combined. Preliminary results also showed that tasks with easy and difficult trials could be distinguished from one another, each within a sensory domain (auditory and visual) as well as with both domains combined. Though further investigation is required, these preliminary results are promising, and suggest the feasibility of a passive BCI for detecting both type and level of mental workload