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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ball, Tonio Knorr, Alexander Fiederer, Lukas D.J. Lampe, Thomas Riedmiller, Martin Voelker, Martin |
| Abstract | We present an Internet-based brain-computer interface (BCI) for controlling an intelligent robotic device with autonomous reinforcement-learning. BCI control was achieved through dry-electrode electroencephalography (EEG) obtained during imaginary movements. Rather than using low-level direct motor control, we employed a high-level control scheme of the robot, acquired via reinforcement learning, to keep the users cognitive load low while allowing control a reaching-grasping task with multiple degrees of freedom. High-level commands were obtained by classification of EEG responses using an artificial neural network approach utilizing time-frequency features and conveyed through an intuitive user interface. The novel ombination of a rapidly operational dry electrode setup, autonomous control and Internet connectivity made it possible to conveniently interface subjects in an EEG laboratory with remote robotic devices in a closed-loop setup with online visual feedback of the robots actions to the subject. The same approach is also suitable to provide home-bound patients with the possibility to control state-of-the-art robotic devices currently confined to a research environment. Thereby, our BCI approach could help severely paralyzed patients by facilitating patient-centered research of new means of communication, mobility and independence. |
| Starting Page | 83 |
| Ending Page | 88 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450321846 |
| DOI | 10.1145/2557500.2557533 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-02-24 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Camera-based uis Semi-autonomous systems Machine learning and data mining Robots |
| Content Type | Text |
| Resource Type | Article |
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