Affective recognition from EEG signals: an integrated data-mining approach

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity

Depression level estimation during covid 19

This Dataset presents data for the estimation of depression level estimation based in factor psychological and life experiences during and after pandemic Covid-19. The data contains 31 attributes and 2953 records, the records are labelled with the class variable Normal and Anormal that indicate the respective level of depression. 33% of the data was generated synthetically using the Weka tool and the SMOTE filter, 66% of the data was collected directly from users through a web platform. This data can be used to generate intelligent computational tools to identify the depression level of an individual and to build recommender systems that monitor depression levels.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Estimation of depression levels using Hamilton Test

This Dataset presents data for the estimation of depression level estimation based in factor psychological and life experiences. The data contains 23 attributes and 347records, the records are labelled with the class variable not depressed, light/minor depression, moderate depression, severe depression, very severe depression that indicate the respective level of depression. 46% of the data was generated synthetically using the Weka tool and the SMOTE filter, 54% of the data was collected directly from users through a web platform. This data can be used to generate intelligent computational tools to identify the depression level of an individual and to build recommender systems that monitor depression levels.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Depression level estimation during covid 19

This Dataset presents data for the estimation of depression level estimation based in factor psychological and life experiences during and after pandemic Covid-19. The data contains 31 attributes and 2953 records, the records are labelled with the class variable Normal and Anormal that indicate the respective level of depression. 33% of the data was generated synthetically using the Weka tool and the SMOTE filter, 66% of the data was collected directly from users through a web platform. This data can be used to generate intelligent computational tools to identify the depression level of an individual and to build recommender systems that monitor depression levels.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV