an autoencoder based emotional stress state detection approach by using electroencephalography signals

Identifying hazards from human error is critical for industrial safety since dangerous and reckless industrial worker actions, as well as a lack of measures, are directly accountable for human-caused problems. lack of sleep, poor nutrition, physical deformities, and weariness are some of the key factors that contribute to these risky and reckless behaviors that might put a person in a perilous scenario. this scenario causes discomfort, worry, despair, cardiovascular disease, a rapid heart rate, and a slew of other undesirable outcomes. as a result, it would be advantageous to recognize people's mental states in the future in order to provide better care for them. researchers have been studying electroencephalogram (eeg) signals to determine a person's stress level at work in recent years. a full feature analysis from domains is necessary to develop a successful machine learning model using electroencephalogram (eeg) inputs. by analyzing eeg data, a time-frequency based hybrid bag of features is designed in this research to determine human stress dependent on their sex. this collection of characteristics includes features from two types of assessments: time-domain statistical analysis and frequency-domain wavelet-based feature assessment. the suggested two layered autoencoder based neural networks (aenn) are then used to identify the stress level using a hybrid bag of features. the experiment uses the deap dataset, which is freely available. the proposed method has a male accuracy of 77.09% and a female accuracy of 80.93%.