Carlos Carreiras (IT)
Exploratory EEG Analysis Using Clustering and Phase-Locking Factor
Emotion recognition is essential for psychological and psychiatric applications and for improving the quality of human-machine interaction. Therefore, a simple and reliable method is needed to automatically assess the emotional state of a subject. This paper presents an application of clustering algorithms to feature spaces obtained from the acquired EEG of subjects performing a stress-inducing task. These features were obtained in three ways: using the EEG directly, using ICA to remove eye movement artifacts, and using EMD to extract data-driven modes present in the signals. From these features, we computed band-power features (BPFs) as well as pairwise phase-locking factors (PLFs), in a total of six different feature spaces. These six feature spaces are used as input to various clustering algorithms. The results of these clustering techniques show interesting phenomena, including prevalence for low numbers of clusters and the fact that clusters tend to be made of consecutive test lines.
Bio: Carlos Carreiras is currently a researcher at Instituto de Telecomunicações, where he has been working on biomedical signal processing, in particular emotion recognition from electroencephalographic (EEG) signals and human identification based on the electrocardiogram (ECG).
Carlos holds the MSc degree in Biomedical Engineering (2011) from Instituto Superior Técnico. In the master's thesis, Carlos worked on Brain-Computer Interfaces based on the imagination of motor tasks, automatically identifying them on the EEG signal.