Mechanisms of unconscious communication in collaborative work environments
In recent years, physiology-based systems such as brain-computer interfaces (BCI) have lead to implicit models of interaction where user’s physiological signals, such as brainwave activity or heart rate, are monitored, mapped and transformed in commands to control devices and applications. With BCI people can simply think and thereby move cursors, browse the internet, navigate virtual environments, play movies or music, or control wheelchairs, robotic arms, and prostheses. BCI can also be used in a more passive mode, where brain activity is monitored and displayed (through sonification or/and visualization) for biofeedback training purposes. Typical BCIs measure brain activity through electrode caps placed on the scalp, and thus do not require surgery, pain, drugs, or extensive training. Hybrid BCI often combines EEG signals with other physiology inputs. BCIs aim primarily to help users with severe disabilities who otherwise cannot communicate. However, in recent years BCI technologies started to be used in the commercial applications with healthy users as gamers, soldiers or astronauts.
The interaction paradigm based on internal states of the human body has been explored by several disciplines such as cognitive psychology, neuroscience, affective and physiological computing, and Human Computer Interaction (HCI). However, most of the BCI system and physiology-based interaction studies are focused in single-user modes, while its application in collaborative scenarios or in Computer-Supported Collaborative Work (CSCW) is still scarce. The use of BCI and other physiology devices for rehabilitation in impaired patients commonly requires individual conditions of use and isolation, but its application in healthy users for communication and control is still tested under similar scenarios.
Our research line aims to develop systems for collaborative interactive experiences based on the combination of Physiology-based technology such as Brain-Computer Interfaces (BCI) featuring Electroencephalography (EEG), Electrocardiography (ECG) or Electrodermal response (EDR), and Tangible User Interface (TUI) for collaborative work, e.g. musical performance. Initial studies ( Mealla, 2010 and Mealla et al. 2011) on our BCI-reactable system showed that subjects working with a combination of implicit and explicit interaction models declared less difficulty and greater ease to solve collaborative tasks, showing higher levels of confidence and a balanced distribution of control. Participants also showed significant correlation in key aspects for collaboration, such as motivation over time. These results support the feasibility of using physiology-based interaction in Computer-Supported Collaborative Performances.