Call2 Monitoring BRACOG

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TASK 3 + TASK 4: We finished the verification of object selection paradigms for grasp initiation. The P300 and SSVEP paradigms differ in the number of alternatives as well as trial durations, but reveal comparable detection rates and information transfer rates (bit/min, see Figure 1). We prefer the P300 over the SSVEP...
[Last edited Aug 3, 2012 ]
TASK 3: We further investigated the motor imagery (MI) paradigm in more detail by evaluating the ability of 17 subjects to control the grasp initiation of a virtual arm. Our results indicate that good MI control in our BCI critically depends on the presence of µ‑rhythms Subjects who did not exhibit the rhythm could not...
[Last edited Jun 22, 2012 ]
TASK 3: We finished the test of the first version of the SSVEP based object selection task with 19 participants. The maximum recognition rate was 91.7% (guessing level 25%). So far the SSVEP-paradigm provided the best results among the tested algorithms. TASK 4: We found a significant improvement to using the object’s...
[Last edited Apr 13, 2012 ]
TASK 3: Figure 1 depicts the achieved rates of correctly detected objects in the 4 target SSVEP selection task for all eleven participants so far. This shows the successful application of the task in the majority of subjects. Fig. 1 Average detection rate of single subjects. Error bars indicate the standard error across...
[Last edited Feb 10, 2012 ]
TASK 3, 4 So far our tests in a movement imagery task revealed huge performance differences between subjects. In our MEG setup we found that magnetic activity of the heart is the source of another important artefact. The main work in this project phase will be the adjustment of the algorithms to allow for online removal of...
[Last edited Dec 20, 2011 ]
In the last bi-monthly report period we were able to assemble the Mitsubishi RV-E2 with the SCHUNK SDH Gripper (T8) and transferred the final CAD-geometry models to our VR-tool. Fig. 1 Final geometry model of the manipulator and first test scenario Fig. 3: Generated grasps in the new scenario all classified as force closure...
[Last edited Oct 20, 2011 ]
In Brain-Computer-Interface settings the task of the subjects is to deliberately modify brain activation to communicate. The imagery of motor actions and to focus attention to different flickering objects are two communication strategies implemented in the first period. We intend to select an object to be grasped by...
[Last edited Aug 22, 2011 ]
We started to implement an automated error correction. The detection of an error we aim to decode from brain signals that are evoked after an unexpected feedback. We succeeded to complete the steps from object recognition to a successful grasp of the robotic gripper. In the following we present the underlying tool-chain. The...
[Last edited Oct 18, 2012 ]
We tested our system with a couple of natural objects (telephone headset, cup, tea box, and ball) which are more or less good-natured but which can neither be called artificial nor simplified. Although there are big gaps in the surface reconstruction no problematic artifacts could be observed that prevented the successful...
[Last edited Jan 3, 2013 ]