Name
Abstract | ||
|---|---|---|
For the patients with limb disorder to control the home robot movement, the human intention sensing and encoding are the two important tasks. This paper focuses on a new augmented reality brain computer interface (ARBCI) of the stable state visual evoked potential (SSVEP), the human intention recognition algorithms using SSVEP and Electra-hologram (EOG) respectively, and the encoding design of the human intentions. Firstly, the new ARBCI is developed which includes the SSVEP collector and a specific environment augmented reality stimulator of the symbols of the robot operations. Furthermore, the robot control instructions are encoded. Secondly, the sliding window superposition-average algorithm (SWSA) is proposed for human intention recognition on the basis of SSVEP. The stimulation frequency feature from the augmented reality stimulator is extracted by using SWSA to control the power supply and the robot speed. Thirdly, the intentional blinking EOG threshold is defined according to the experiments. Then, a fusion recognition (FR) algorithm of amplitude and sampling time is developed on the basis of EOG, which is for the home robot direction controls. It is experimentally proved that the ARBCI improves the eye comfort compared with that of the original BCI stimulator. Besides, the SWSA can save 4 s to sensing a SSVEP intention meanwhile keep the same recognition accuracy compared with the traditional superposition-average method. In addition, the human intention sensing accuracy can reach 100% by using ARBCI and SWSA and the FR if the sensing time is adequate. A EOG intention sensing time is about 0.5 s by using the FR algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11042-017-4871-y | Multimedia Tools Appl. |
Keywords | Field | DocType |
BCI, Feature extraction, Recognition, Robot, Control | Computer vision,Robot control,Sliding window protocol,Computer science,Brain–computer interface,Feature extraction,Augmented reality,Phone,Artificial intelligence,Robot,Encoding (memory) | Journal |
Volume | Issue | ISSN |
77 | 9 | 1380-7501 |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mei Wang | 1 | 10 | 1.93 |
| Wubing Qu | 2 | 1 | 0.35 |
| Wen-Yuan Chen | 3 | 82 | 8.54 |