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People with partial or complete paralysis are usually dependent on nursing staff to cope with their daily activities. Alternatively, an assistance robotics system serves as a substitute for the lost motor skills and enables an improvement in the quality of life. With the help of a human-machine interface, the user can operate the system and initiate autonomous processes. If the system consists of a robot arm, the disabled person is able to grab and move objects. In the case of an error caused by the system, the user should be able to control the robot arm directly. With the interface developed in this work, a human being can open or close as well as move the robot gripper into any position and orientation. Since the target group of the system is people with very limited body movements, a Brain-Computer Interface (BCI) is used for communication. It establishes a communication channel between the human brain and the controlled robot arm. A special helmet is used for recording the brain signals, which, in addition to the measuring electrodes, has a stimulator in the peripheral field of vision of the user. This stimulator has four stimuli by which an evocation of event-related and visual potentials are provided. By detecting Steady State Visual Evoked Potentials (SSVEPs) and P300 potentials, the system can determine which stimulus the user has focused on. With this, four discrete commands are provided for the user to control the robot. For switching the stimulator on and off motor imaginations are used. A state machine with a control group for each translational and rotational movement is used to control the robot. A stepwise control, in which the gripper performs discrete steps, and a speed-based control, in which the movement of the gripper is started and stopped has been implemented. Switching between groups is performed by the sequential sending of two commands. In each group, the user can perform a movement in the positive and negative direction of the selected axis, change the step width or speed, and leave the group. Stopping at the target position and in dangerous situations occurs by closing the eyes and the thereby occurring artifacts and alpha waves in the signals. Both control concepts were tested in a study and evaluated with objective and subjective criteria. The experiments have shown that BCI-based robot arm control is feasible and useful.

Eye tracking technology has opened up a new communication channel for people with very restricted body movements. These devices had already been successfully applied as a human computer interface, e.g. for writing a text, or to control different devices like a wheelchair. This thesis proposes a Human Robot Interface (HRI) that enables the user to control a robot arm in 3-Dimensional space using only 2-Dimensional gaze direction and the states of the eyes. The introduced interface provides all required commands to translate, rotate, open or close the gripper with the definition of different control modes. In each mode, different commands are provided and direct gaze direction of the user is applied to generate continuous robot commands. To distinguish between natural inspection eye movements and the eye movements that intent to control the robot arm, dynamic command areas are proposed. The dynamic command areas are defined around the robot gripper and are updated with its movements. To provide a direct interaction of the user, gaze gestures and states of the eyes are used to switch between different control modes. For the purpose of this thesis, two versions of the above-introduced HRI were developed. In the first version of the HRI, only two simple gaze gestures and two states of the eye (closed eyes and eye winking) are used for switching. In the second version, instead of the two simple gestures, four complex gaze gestures were applied and the positions of the dynamic command areas were optimized. The complex gaze gestures enable the user to switch directly from initial mode to the desired control mode. These gestures are flexible and can be generated directly in the robot environments. For the recognition of complex gaze gestures, a novel algorithm based on Dynamic Time Warping (DTW) is proposed. The results of the studies conducted with both HRIs confirmed their feasibility and showed the high potential of the proposed interfaces as hands-free interfaces. Furthermore, the results of subjective and objective measurements showed that the usability of the interface with simple gaze gestures had been improved with the integration of complex gaze gestures and the new positions of the dynamic command areas.