Emotiv EPOC BCI with Python on a Raspberry pi
DOI:
https://doi.org/10.18046/syt.v14i36.2217Keywords:
BCI, EEG, EPOC, Python, Raspberry Pi, support vector machineAbstract
The hybrid Brain-Computer Interface [BCI] system gives an insight on the development of useful interfaces for users with different backgrounds, from medical applications to video games, where standalone and wearable means accessibility for the user. Systems such as EPOC offers a simple solution for acquiring electroencephalography and electromyography signals with low price and fast setup, compared to high tech medical equipment. From the processing point of view, a computer always offers the main foundation for solving any issue, as the Raspberry Pi [RPi] does, which provides the sufficient computational power for a BCI to be implemented and an open source operating system such as Raspbian. Certainly a wireless communication is a must between the robot and the RPi, where an Xbee module gives a simple bidirectional connection. Python is the principal tool used in the project with multiple libraries for the processing of brain and muscular signals not only for the preparation of them but classification as well, from multithreading functions, feature extraction such as power spectral density and Hjorth parameters, and a support vector machine classifiera.References
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Systems, Man and Cybernetics (SMC), (pp. 1088-1094). IEEE. doi:10.1109/SMC.2014.6974059.
Tahmasebzadeh, A., Bahrani, M., & Setarehdan, S. K. (2013). Development of a robust method for an online P300 speller brain computer interface. In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), (pp. 1070-1075). IEEE. doi: 10.1109/NER.2013.6696122.
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Upton, L. (2015). Benchmarking raspberry Pi 2 [blog]. Retrieved from: https://www.raspberrypi.org/blog/benchmarking-raspberry-pi-2/
Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), 22-30. doi:10.1109/MCSE.2011.37.
Wang, Q., & Sourina, O. (2013). Real-time mental arithmetic task recognition from EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering 21(2), 225-232. doi: 10.1109/TNSRE.2012.2236576.
Yao, L., Meng, J., Zhang, D., Sheng, X., & Zhu, X. (2014). Combining motor imagery with selective sensation toward a hybrid-modality BCI. IEEE Transactions on Biomedical Engineering, 61(8), 2304-2312. doi: 10.1109/TBME.2013.2287245.
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2016-03-30
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