Multi-Scale Time Series Prediction of Consciousness Mapping

Abstract

This paper investigates the potential for mapping human consciousness states through multi-scale time series prediction. The core idea is to analyze the inherent structure within time series data, specifically focusing on the relationships between different temporal scales, and establish a predictive model that correlates with subjective reports or physiological measures of consciousness. We propose a methodology utilizing wavelet and Fourier transforms to extract features at varying scales from the time series data. These features are then fed into a deep learning model for prediction. The predictive accuracy is subsequently assessed and correlated with the individual's reported experience or relevant physiological indicators. This approach moves beyond traditional behavioral observation methods, seeking to identify underlying information patterns associated with consciousness states. The results suggest a potential framework for quantifying and understanding the complex dynamics of human consciousness using quantitative data analysis. The key contributions of this work lie in the integration of predictive modeling techniques with consciousness research and the exploration of novel feature extraction methods applicable to diverse time series data.