Name
Abstract | ||
|---|---|---|
The investigation of innovative Human–Computer Interfaces (HCI) provides a challenge for future interaction research and development. Brain–Computer Interfaces (BCIs) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS) or complete paralysis of all extremities due to high spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e.g., a dexterity discovery, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in EEG signals which are recorded non-invasively from the scalp. This contribution introduces the Berlin Brain–Computer Interface (BBCI) and presents set-ups where the user is provided with intuitive control strategies in plausible interactive bio-feedback applications. Yet at its beginning, BBCI thus adds a new dimension in HCI research by offering the user an additional and independent communication channel based on brain activity only. Successful experiments already yielded inspiring proofs-of-concept. A diversity of interactive application models, say computer games, and their specific intuitive control strategies are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates. BBCI is a complex distributed software system that can be run on several communicating computers responsible for (i) the signal acquisition, (ii) the data processing and (iii) the feedback application. Developing a BCI system, special attention must be paid to the design of the feedback application that serves as the HCI unit. This should provide the user with the information about her/his brain activity in a way that is intuitively intelligible. Exciting discovery applications qualify perfectly for this role. However, most of these applications incorporate control strategies that are developed especially for the control with haptic devices, e.g., joystick, keyboard or mouse. Therefore, novel control strategies should be developed for this purpose that (i) allow the user to incorporate additional information for the control of animated objects and (ii) do not frustrate the user in the case of a misclassification of the decoded brain signal. BCIs are able to decode different information types from the user's brain activity, such as sensory perception or motor intentions and imaginations, movement preparations, levels of stress, workload or task-related idling. All of these diverse brain signals can be incorporated in an exciting discovery scenario. Modern HCI research and development technologies can provide BCI researchers with the know-how about interactive feedback applications and corresponding control strategies. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.ijhcs.2006.11.010 | International Journal of Human-Computer Studies |
Keywords | Field | DocType |
corresponding control strategy,brain-computer interface,gaming,intuitive control strategy,decoded brain signal,communication channel,digital signal processing,novel control strategy,berlin brain-computer interface-the hci,specific intuitive control strategy,user adaptation,electroencephalography,control possibility,bio-feedback,machine learning,brain activity,virtual limbs,scientific discovery,feedback application,control strategy,application development,communication channels,feedback control,software systems,data processing,haptic device,proof of concept,brain computer interface,human computer interface,brain computer interfaces | Artificial life,Computer science,Brain–computer interface,Communication channel,Distributed algorithm,Human–computer interaction,Human communication,Joystick,User interface,Haptic technology | Journal |
Volume | Issue | ISSN |
65 | 5 | 1071-5819 |
Citations | PageRank | References |
12 | 0.91 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roman Krepki | 1 | 65 | 6.02 |
| Gabriel Curio | 2 | 1220 | 201.67 |
| B. Blankertz | 3 | 2918 | 334.21 |
| Klaus-Robert Müller | 4 | 12756 | 1615.17 |