Psychophysiological Measure

Psychophysiological measures utilize physiological signals like EEG, ECG, EDA, and facial expressions to objectively assess mental states such as workload, anxiety, and stress. Current research focuses on developing and validating these measures for applications in human-machine interfaces (e.g., automated driving) and mental health monitoring, often employing machine learning techniques like 3D convolutional neural networks and ExtraTrees classifiers for data analysis and state classification. The ability to objectively quantify these internal states holds significant promise for improving the design of technology, enhancing mental healthcare, and providing more accurate and timely diagnoses.