Understanding the relationship between rosemary odor and mental workload through deep learning.

This research explores the novel application of aromatic odors, specifically rosemary, in reducing mental workload, employing deep learning methods to analyze electroencephalogram (EEG) signals without feature extraction. Thirty volunteers participated in five neuropsychological tests while being exposed to the aroma of rosemary. The EEG signals recorded during the performance of these tasks were analyzed using deep learning methods to classify mental workload. Deep learning algorithms such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) were employed to classify mental workload directly from EEG signals. The analysis revealed that volunteers exposed to the rosemary odor showed decreased error rates and increased test success and learning scores, in comparison to a condition without odor. The classification of mental workload under rosemary odor exposure was achieved with a high accuracy rate of 97.11% in both deep learning algorithms. This study presents a novel approach by combining olfactory stimulation and EEG-based mental workload classification through deep learning. These findings suggest that rosemary odor may reduce mental workload and that raw EEG signals can be effectively analyzed using deep learning without manual feature engineering.