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Piccini L, Parini S, Maggi L, et al. A Wearable Home BCI system: preliminary results with SSVEP protocol. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:5384-7doi: 10.1109/IEMBS.2005.1615699.
Piccini, L., Parini, S., Maggi, L., & Andreoni, G. (2005). A Wearable Home BCI system: preliminary results with SSVEP protocol. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 20055384-7. https://doi.org/10.1109/IEMBS.2005.1615699
Piccini, Luca, et al. "A Wearable Home BCI system: preliminary results with SSVEP protocol." Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference vol. 2005 (2005): 5384-7. doi: https://doi.org/10.1109/IEMBS.2005.1615699
Piccini L, Parini S, Maggi L, Andreoni G. A Wearable Home BCI system: preliminary results with SSVEP protocol. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:5384-7. doi: 10.1109/IEMBS.2005.1615699. PMID: 17281469.
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Conf Proc IEEE Eng Med Biol Soc. 2005;2005:5384-7. doi: 10.1109/IEMBS.2005.1615699.

A Wearable Home BCI system: preliminary results with SSVEP protocol.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

Luca Piccini, Sergio Parini, Luca Maggi, Giuseppe Andreoni

PMID: 17281469 DOI: 10.1109/IEMBS.2005.1615699

Abstract

This paper presents and discusses the realization and the performances of a wearable system for EEG-based BCI applications. The system (called Kimera) consists of a two-layer hardware architecture (the wireless acquisition and transmission board based on a Bluetooth ® ARM chip, and a low power miniaturized biosignal acquisition analog front end) together with a software suite (called Bellerophonte) for the Graphic User Interface management, protocol execution, data recording, transmission and processing. The implemented BCI system was based on the SSVEP protocol, applied to a two state selection by using standards display/monitor with a couple of high efficiency LEDs. The frequency features of the signal were computed and used in the intention detection. The BCI algorithm is based on a supervised classifier implemented through a multi-class Canonical Discriminant Analysis (CDA) with a continuous realtime feedback based on the mahalanobis distance parameter. Five healthy subjects participated in the first phase for a preliminary device validation. The obtained results are very interesting and promising, being lined out to the most recent performance reported in literature with a significant improvement both in system and in classification capabilities. The user-friendliness and low cost of the Kimera& Bellerophonte platform make it suitable for the development of home BCI applications.

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