Abstract
Brain computer interfaces (BCI) that use the steady-state-visual-evoked-potential (SSVEP) as a neural source are very promising communication restoration technologies because they require shorter calibration and can yield higher information transfer rates compared to BCIs based on other neural sources. SSVEPs elicited by high frequency (higher than 30 Hz) repetitive visual stimulation are more comfortable, safer, and less prone to cause visual fatigue. However, there is a practical limitation in the high frequency range, because only a few frequencies can be used for BCI purposes. This limitation can be overcome by using repetitive visual stimuli having the same frequency but different phases. Detecting the phase of the SSVEP from the electroencephalogram is possible through a convenient signal processing approach which concatenates spatial filtering and phase synchrony analysis. This approach is illustrated with an actual BCI implementation which was tested on 15 participants and resulted on an average bit rate of 33.2 bits-per minute and an average accuracy of 92 %. These results are comparable to state-of-the-art SSVEP-BCI performance. In this approach, however, the user experiences minimal annoyance from attending to visual stimulation.
Keywords
- Probabilistic Neural Network
- Brain Computer Interface
- Visual Discomfort
- Information Transfer Rate
- Steady State Visual Evoke Potential
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgements
The authors would like to express their gratitude to Dr. Vojkan Mihajlovic for his valuable suggestions to improve the quality of this chapter.
The research leading to these results has received funding from the European Community’s Seventh Framework Programme under grant agreement number 224156.
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Garcia-Molina, G., Zhu, D. (2012). Phase Detection of Visual Evoked Potentials Applied to Brain Computer Interfacing. In: Allison, B., Dunne, S., Leeb, R., Del R. Millán, J., Nijholt, A. (eds) Towards Practical Brain-Computer Interfaces. Biological and Medical Physics, Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29746-5_14
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