Relevance of EEG input signals in the augmented human reader

• Authors: Inês Oliveira;Ovidiu Grigore;Nuno Guimarães;Luís Duarte
• Venue: Proceedings of the 1st Augmented Human International Conference
• Year: 2010

This paper studies the discrimination of electroencephalographic (EEG) signals based in their capacity to identify silent attentive visual reading activities versus non reading states. The use of physiological signals is growing in the design of interactive systems due to their relevance in the improvement of the coupling between user states and application behavior. Reading is pervasive in visual user interfaces. In previous work, we integrated EEG signals in prototypical applications, designed to analyze reading tasks. This work searches for signals that are most relevant for reading detection procedures. More specifically, this study determines which features, input signals, and frequency bands are more significant for discrimination between reading and non-reading classes. This optimization is critical for an efficient and real time implementation of EEG processing software components, a basic requirement for the future applications. We use probabilistic similarity metrics, independent of the classification algorithm. All analyses are performed after determining the power spectrum density of delta, theta, alpha, beta and gamma rhythms. The results about the relevance of the input signals are validated with functional neurosciences knowledge. The experiences have been performed in a conventional HCI lab, with non clinical EEG equipment and setup. This is an explicit and voluntary condition. We anticipate that future mobile and wireless EEG capture devices will allow this work to be generalized to common applications.