EEG-based Deep Emotional Diagnosis: A Comparative Study

Emotion is an important part of people's daily life, particularly relevant to the mental health of people. Emotional diagnosis is closely related to the nervous system, which can well reflect people's mental conditions in response to the surrounding environment or the development of various neurodegenerative diseases. Emotion recognition can help the medical diagnosis of mental health. In recent years, emotion recognition based on EEG has attracted the attention of many researchers accompanying with the continuous development of artificial intelligence and brain computer interface technology. In this paper, we carried out a comparison on the performance of three deep learning techniques on EEG classification, including DNN, CNN and CNN-LSTM. DEAP data set was used in our experiments. EEG signals were transformed from time domain to frequency domain first, and then features are extracted to classify emotions. From our research, it shows these deep learning techniques can achieve good accuracy on emotional diagnosis

Reliability Investigation for Encapsulated Isotropic Conductive Adhesives Flip Chip Interconnection

Isotropic conductive adhesives (ICA) are gaining more and more application interests in electronic manufacturing, however, their failure mechanism is not been fully understood. In this paper we present reliability investigations on an encapsulated ICA flip chip interconnection. Experimental work included product lifetime measurement, cross section observation, and whole module warpage scanning. Results revealed that the chip-size effect on the ICA lifetime was obvious. A theoretical analysis was conducted with Finite Element Method (FEM) simulation. Viscoelastic models for adhesives and underfill materials were employed, and the comparison with an elastic model was made. Calculated equivalent stresses S eqv and shear stress σ xy fitted well with the experimental lifetime measurement, thus a lifetime relationship similar to the Coffin-Manson formula was established to predict the thermal fatigue life of an encapsulated ICA flip chip. Furthermore, the influences of underfill properties on the ICA reliability were discussed. Copyright \ua9 2006 by ASME