MOTION ESTIMATION IN BRAIN TOPOGRAPHIC MAPS

The objective of brain mapping is to advance knowledge in understanding the brain functions with its respective structures. Current technologies and advances in brain imaging technique using EEG (electroencephalogram) allows estimation of network connection which represents activity that occur in different structures of human brain. As the EEG procedure is >imple and harmless, it is widely used to study the brain behavior and cognitive processes such iS memory, language, emotions, sensation and alertness. In past study, ten healthy subjects (five female and five male) undergo an experiment consists of 200 trials. These trials include two >timulus consist of X and 0 which appears randomly. Subjects required to respond to X and ignore 0. Signals gained from these events evaluated as topographical properties of brain aetwork. Note that, only segment of interest will be plotted on topographic maps. In present research, these topographic maps will be used to study the behavior of the brain signals due to presence of stimulus. Motion estimation is an interesting analysis of tracking movement of signals around brain region. Currently, no proper motion estimation technology is available for this purpose. Development of motion estimation system allows us to observe and study movements of brain signals. In this project, I've applied Full Search Algorithm on brain topographic. Movement of signals detected based on general matching criteria which will be further discussed in the literature review

MOTION ESTIMATION IN BRAIN TOPOGRAPHIC MAPS

The objective of brain mapping is to advance knowledge in understanding the brain functions with its respective structures. Current technologies and advances in brain imaging technique using EEG (electroencephalogram) allows estimation of network connection which represents activity that occur in different structures of human brain. As the EEG procedure is >imple and harmless, it is widely used to study the brain behavior and cognitive processes such iS memory, language, emotions, sensation and alertness. In past study, ten healthy subjects (five female and five male) undergo an experiment consists of 200 trials. These trials include two >timulus consist of X and 0 which appears randomly. Subjects required to respond to X and ignore 0. Signals gained from these events evaluated as topographical properties of brain aetwork. Note that, only segment of interest will be plotted on topographic maps. In present research, these topographic maps will be used to study the behavior of the brain signals due to presence of stimulus. Motion estimation is an interesting analysis of tracking movement of signals around brain region. Currently, no proper motion estimation technology is available for this purpose. Development of motion estimation system allows us to observe and study movements of brain signals. In this project, I've applied Full Search Algorithm on brain topographic. Movement of signals detected based on general matching criteria which will be further discussed in the literature review

A real time classification algorithm for EEG-based BCI driven by self-induced emotions

Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. Conclusions: The obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities

Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues

We examine the utility of implicit behavioral cues in the form of EEG brain signals and eye movements for gender recognition (GR) and emotion recognition (ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded (no mask) as well as partially occluded (eye and mouth masked) emotive faces. Obtained experimental results reveal that (a) reliable GR and ER is achievable with EEG and eye features, (b) differential cognitive processing especially for negative emotions is observed for males and females and (c) some of these cognitive differences manifest under partial face occlusion, as typified by the eye and mouth mask conditions.Comment: To be published in the Proceedings of Seventh International Conference on Affective Computing and Intelligent Interaction.201