Reinforcing Positive Cognitive States with Machine Learning: An Experimental Modeling for Preventive Healthcare

Abstract

Societal evolution has resulted in a complex lifestyle where we give most attention to our physical health leaving psychological health less prioritized. Considering the complex relationship between stress and psychological well-being, this study bases itself on the cognitive states experienced by us. The presented research offers insight into how state-of-the-art technologies can be used to support positive cognitive states. It makes use of the brain-computer interface (BCI) that drives the data collection using electroencephalography (EEG). The study leverages data science to devise machine learning (ML) model to predict the corresponding stress levels of an individual. A feedback loop using “Self Quantification” and “Nudging” offer real-time insights about an individual. Such a mechanism can also support the psychological conditioning of an individual where it does not only offer spatial flexibility and cognitive assistance but also results in enhanced self-efficacy. Being part of quantified self-movement, such an experimental approach could showcase personalized indicators to reflect a positive cognitive state. Although ML modeling in such a data-driven approach might experience reduced diagnostic sensitivity and suffer from observer variability, it can complement psychosomatic treatments for preventive healthcare.

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“…Such an analysis can be done by considering various data sources. Analytics uses multiple machine learning algorithms for identifying diseases by identifying irregularities in physiological patterns (Arshi et al, 2022;Morande et al, 2022). Machine learning algorithms are self-learning algorithms that enhance diagnosis accuracy and play a crucial role in classifying data and identifying diseases effectively (Shanmugasundaram G & Sankarikaarguzhali.…”