Unsupervised Analysis of Morphological ECG Features for Attention Detection

Physiological Computing augments the information bandwidth between a computer and its user by continuous, real-timemonitoring of the user's physiological traits and responses. This is especially interesting in a context of emotional assessment during human-computer interaction. The electroencephalogram (EEG) signal, acquired on the scalp, has been extensively used to understand cognitive function, and in particular emotion. However, this type of signal has several drawbacks, being susceptible to noise and requiring the use of impractical head-mounted apparatuses. For these reasons, the electrocardiogram (ECG) has been proposed as an alternative source to assess emotion, which is continuously available, and related with the psychophysiological state of the subject. In this paper we analyze morphological features of the ECG signal acquired from subjects performing an attention-demanding task. The analysis is based on various unsupervised learning techniques, which are validated against evidence found in a previous study by our team, where EEG signals collected for the same task exhibit distinct patterns as the subjects progress in the task.