Theta to Alpha

Research on theta and alpha brainwave activity focuses on understanding their interplay in cognitive processes, particularly learning, memory, and mental workload. Current investigations utilize machine learning techniques, including deep neural networks and support vector machines, to analyze EEG data and extract meaningful features from theta and alpha band ratios, often employing hidden Markov models for improved feature engineering. These studies aim to improve the accuracy of classifying cognitive states and potentially contribute to better diagnostic tools for conditions like dyslexia, as well as more effective human-computer interfaces sensitive to mental workload.