Machine Learning and EEG in Assessing the Biophilic Effect: A Comprehensive Review

Abstract

The intersection of machine learning and electroencephalography (EEG) offers promising avenues for assessing the biophilic effect—an innate human inclination towards nature that can enhance psychological well-being and cognitive performance. This comprehensive review systematically examines the current literature to elucidate how machine learning algorithms have been leveraged with EEG data to quantify and understand the biophilic effect. By highlighting core methodologies, datasets, and analytical frameworks, this paper aims to identify both the potential and limitations of current practices.

In recent years, significant advancements in EEG technology and machine learning techniques have facilitated more nuanced insights into neural responses associated with exposure to natural environments. The integration of EEG, with its high temporal resolution, and machine learning models, capable of handling large and complex datasets, allows for the extraction of subtle patterns indicative of the biophilic effect. This review categorizes existing studies based on the application of various machine learning approaches, such as supervised learning, unsupervised learning, and deep learning, in processing EEG signals to assess environmental influences on brain activity.

Furthermore, the review explores the diverse range of EEG features and machine learning classifiers employed in these studies, emphasizing the importance of feature selection and model interpretation in generating reliable and actionable insights. Challenges such as data variability, model generalizability, and the interpretability of complex models are critically discussed. The paper also highlights potential research directions, including the integration of multimodal data sources and the advancement of explainable AI techniques to improve the interpretability of machine learning models in this domain.

By synthesizing current research findings, this review not only underscores the potential of machine learning and EEG in advancing our understanding of the biophilic effect but also calls for more interdisciplinary efforts to address existing challenges and enhance the applicability of these technologies in promoting human health and well-being.