An EEG Adaptive Information System for an Empathic Robot

作者：Heger Dominic^*; Putze Felix; Schultz Tanja

来源：International Journal of Social Robotics, 2011, 3(4): 415-425.

DOI：10.1007/s12369-011-0107-x

• Human-robot spoken interaction
• Adaptive interaction strategies
• Electroencephalography
• Mental workload recognition
• Human evaluation study

This article introduces a speech-driven information system for a humanoid robot that is able to adapt its information presentation strategy according to brain patterns of its user. Brain patterns are classified from electroencephalographic (EEG) signals and correspond to situations of low and high mental workload. The robot dynamically selects the information presentation style that best matches the detected patterns. The resulting end-to-end system consisting of recognition and adaptation components is tested in an evaluation study with 20 participants. We achieve a mean recognition rate of 83.5% for discrimination between low and high mental workload. Furthermore, we compare the dynamic adaptation strategy with two static presentation strategies. The evaluation results show that the adaptation of the presentation strategy according to workload improves over the static presentation strategy in both, information correctness and completeness. In addition, the adaptive strategy is favored over the static strategy as user satisfaction improves significantly. This paper presents the first systematic analysis of a real-time EEG-adaptive end-to-end information system for a humanoid robot. The achieved evaluation results indicate its great potential for empathic human-robot interaction.

• 出版日期2011-11