Imagine you’re a detective trying to solve a complex case. You have two crucial pieces of evidence, the alpha-to-theta and theta-to-alpha band ratios found in EEG data. These bands are like two different colored lights on a crime scene. Many researchers believe they can help us understand the cognitive load we experience when we work on mental tasks. However, there’s still a need for more proof to support this claim. That’s where this study comes in. Using a dataset of raw EEG data, scientists aimed to evaluate the impact of these band ratios in creating models that can accurately measure our mental workload. They extracted features from the computed ratios over time and built models using Logistic Regression, Support Vector Machines, and Decision Trees. The models were then tested using performance measures like accuracy and precision. And guess what? The results were promising! Models trained with the alpha-to-theta ratios and theta-to-alpha ratios showed high classification accuracy in distinguishing self-reported perceptions of mental workload. So, it seems like these band ratios might hold the key to unlocking our understanding of how our brains handle mental tasks. If you’re intrigued by this research, dig deeper into the article for all the fascinating details!

Many research works indicate that EEG bands, specifically the alpha and theta bands, have been potentially helpful cognitive load indicators. However, minimal research exists to validate this claim. This study aims to assess and analyze the impact of the alpha-to-theta and the theta-to-alpha band ratios on supporting the creation of models capable of discriminating self-reported perceptions of mental workload. A dataset of raw EEG data was utilized in which 48 subjects performed a resting activity and an induced task demanding exercise in the form of a multitasking SIMKAP test. Band ratios were devised from frontal and parietal electrode clusters. Building and model testing was done with high-level independent features from the frequency and temporal domains extracted from the computed ratios over time. Target features for model training were extracted from the subjective ratings collected after resting and task demand activities. Models were built by employing Logistic Regression, Support Vector Machines and Decision Trees and were evaluated with performance measures including accuracy, recall, precision and f1-score. The results indicate high classification accuracy of those models trained with the high-level features extracted from the alpha-to-theta ratios and theta-to-alpha ratios. Preliminary results also show that models trained with logistic regression and support vector machines can accurately classify self-reported perceptions of mental workload. This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.

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