Sipahioglu, EmreAkbugday, BurakAkbugday, Sude PehlivanAkan, Aydin2025-01-252025-01-252024979833152981997983315298262687-7775https://doi.org/10.1109/TIPTEKNO63488.2024.10755381https://hdl.handle.net/20.500.14365/5850The impact of stress on daily life has been a subject of interest in the last decades. The utilization of numerous electrical and electronic devices as well as increased land and air transportation densities constantly create noise which is a significant contributor to stress. In this study, the relationship between environmental noise, cognitive workload, and stress is investigated. Electroencephalogram (EEG) and photoplethysmogram (PPG) signals of 30 volunteers were recorded simultaneously while performing a 2-back task with different background noise levels. Features were then extracted from the processed signals to be classified with various machine learning algorithms. Results show that medium noise levels result in increased accuracy for the 2-back task which indicates keeping the noise levels at an acceptable level would be better for work and learning environments.eninfo:eu-repo/semantics/closedAccessEegPpgMachine LearningN-Back TaskNoiseEnvironmentArtificial IntelligenceWorking MemoryConference Object10.1109/TIPTEKNO63488.2024.107553812-s2.0-85212708588