A Comparative Analysis of EEG-based Stress Detection Utilizing Machine Learning and Deep Learning Classifiers with a Critical Literature Review

Background: Mental stress is considered to be a major contributor to different psychological and physical diseases. Different socio-economic issues, competition in the workplace and amongst the students, and a high level of expectations are the major causes of stress. This in turn transforms into several diseases and may extend to dangerous stages if not treated properly and timely, causing the situations such as depression, heart attack, and suicide. This stress is considered to be a very serious health abnormality. Stress is to be recognized and managed before it ruins the health of a person. This has motivated the researchers to explore the techniques for stress detection. Advanced machine learning and deep learning techniques are to be investigated for stress detection.  Methodology: A survey of different techniques used for stress detection is done here. Different stages of detection including pre-processing, feature extraction, and classification are explored and critically reviewed. Electroencephalogram (EEG) is the main parameter considered in this study for stress detection. After reviewing the state-of-the-art methods for stress detection, a typical methodology is implemented, where feature extraction is done by using principal component analysis (PCA), ICA, and discrete cosine transform. After the feature extraction, some state-of-art machine learning classifiers are employed for classification including support vector machine (SVM), K-nearest neighbor (KNN), NB, and CT. In addition to these classifiers, a typical deep-learning classifier is also utilized for detection purposes. The dataset used for the study is the Database for Emotion Analysis using Physiological Signals (DEAP) dataset. Results: Different performance measures are considered including precision, recall, F1-score, and accuracy. PCA with KNN, CT, SVM and NB have given accuracies of 65.7534%, 58.9041%, 61.6438%, and 57.5342% respectively. With ICA as feature extractor accuracies obtained are 58.9041%, 61.64384%, 57.5342%, and 54.79452% for the classifiers KNN, CT, SVM, and NB respectively. DCT is also considered a feature extractor with classical machine learning algorithms giving the accuracies of 56.16438%, 50.6849%, 54.7945%, and 45.2055% for the classifiers KNN, CT, SVM, and NB respectively. A conventional DCNN classification is performed given an accuracy of 76% and precision, recall, and F1-score of 0.66, 0.77, and 0.64 respectively. Conclusion: For EEG-based stress detection, different state-of-the-art machine learning and deep learning methods are used along with different feature extractors such as PCA, ICA, and DCT. Results show that the deep learning classifier gives an overall accuracy of 76%, which is a significant improvement over classical machine learning techniques with the accuracies as PCA+ KNN (65.75%), DCT+KNN (56.16%), and ICA+CT (61.64%)

Estudo neurofisiológico da discriminação de distância em humanos

Tactile width discrimination processing has been extensively studied in rodents and has demonstrated multiple relevant basic mechanisms. Despite this relevance, the number of studies of width discrimination in humans has been scarce. During the present dissertation, neurophysiological correlates of width discrimination were analyzed through electroencephalography recordings in participants performing a width discrimination task in active or passive modes. Analysis of power in the delta, theta, alpha, beta, and gamma frequency bands revealed differences in the power for different frequency bands and electrodes recorded. Active width discrimination processing was characterized by an increase in the power of delta, theta and gamma frequency bands in electrodes F3 and F4, and an increase in power in the gamma frequency band in T4. Passive tactile width processing was characterized by an increase in the power of delta in electrodes Fp1 and T4, and an increase in gamma frequency band in Tp10. Altogether these results suggest that active and passive tactile width discrimination processing are characterized by an asymmetrical network involving prefrontal, frontal and temporal electrodes, in delta, theta, and gamma frequency bands.O estudo do processamento tátil de distâncias encontra-se bastante desenvolvido em roedores, tendo sido útil para a demonstração de múltiplos mecanismos básicos relevantes. Apesar desta relevância, o estudo da discriminação de distâncias em humanos é ainda bastante reduzido. Durante a presente dissertação foram analisados os correlatos neurofisiológicos, através do registo de eletroencefalografia, em participantes que realizavam uma tarefa de discriminação de distância em modo ativo ou passivo. A análise da potência das bandas de frequências delta, teta, alfa, beta e gama revelou diferenças na potência do sinal para diferentes bandas de frequências e elétrodos. O processamento ativo era caracterizado por um aumento da potência nas bandas de frequências delta, teta e gama nos elétrodos F3, F4; e um aumento da atividade na banda de frequência gama no elétrodo T4. O processamento passivo era caracterizado por um aumento da potência de delta nos elétrodos Fp1 e T4 e um aumento da potência de gama em Tp10. No seu conjunto, estes resultados sugerem que o processamento ativo e passivo da distância são caraterizados por uma rede assimétrica envolvendo elétrodos pré-frontais, frontais e temporais nas bandas de frequência delta, teta e gama.Mestrado em Biomedicina Molecula