A robust and self-paced BCI system based on a four class SSVEP paradigm: algorithms and protocols for a high-transfer-rate direct brain communication

Authors:
Sergio Parini;Luca Maggi;Anna C. Turconi;Giuseppe Andreoni
Affiliations:
Bioengineering Department, Politecnico di Milano University, Milan, Italy;INDACO Department, Politecnico di Milano University, Milan, Italy;IRCCS Eugenio Medea "La Nostra Famiglia", Lecco, Italy;INDACO Department, Politecnico di Milano University, Milan, Italy
Venue:
Computational Intelligence and Neuroscience
Year:
2009

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Abstract

In this paper, we present, with particular focus on the adopted processing and identification chain and protocol-related solutions, a whole self-paced brain-computer interface system based on a 4-class steady-state visual evoked potentials (SSVEPs) paradigm. The proposed system incorporates an automated spatial filtering technique centred on the common spatial patterns (CSPs) method, an autoscaled and effective signal features extraction which is used for providing an unsupervised biofeedback, and a robust self-paced classifier based on the discriminant analysis theory. The adopted operating protocol is structured in a screening, training, and testing phase aimed at collecting user-specific information regarding best stimulation frequencies, optimal sources identification, and overall system processing chain calibration in only a few minutes. The system, validated on 11 healthy/pathologic subjects, has proven to be reliable in terms of achievable communication speed (up to 70 bit/min) and very robust to false positive identifications.