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

Application of Ni and Cu nanoparticles in transient liquid phase (TLP) bonding of Ti-6Al-4V and Mg-AZ31 alloys

NoThe transient liquid phase (TLP) bonding of Ti-6Al-4V alloy to a Mg-AZ31 alloy was performed using an electrodeposited Ni coating containing a dispersion of Ni and Cu nanoparticles. Bond formation was attributed to two mechanisms; first, solid-state diffusion of Ni and Mg, followed by liquid eutectic formation at the Mg-AZ31 interface. Second, the solid-state diffusion of Ni and Ti at the Ti-6Al-4V interface resulted in a metallurgical joint. The joint interface was characterized by scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray diffraction analysis. Microhardness and shear strength tests were used to investigate the mechanical properties of the bonds. The use of Cu nanoparticles as a dispersion produced the maximum joint shear strength of 69 MPa. This shear strength value corresponded to a 15 % enhancement in joint strength compared to TLP bonds made without the use of nanoparticles dispersion.The authors would like to acknowledge The German Jordanian University (GJU), and NSERC Canada for the financial support for this research