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

Sergio Parini,Anna C Turconi,Giuseppe Andreoni,Luca Maggi

doi:10.1155/2009/864564

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.

Highlights

A brain-computer interface (BCI) is a system dedicated at providing its users with a new and alternative communication channel totally independent from the traditional output pathways of the nervous system such as peripheral nerves and muscles [1]
In this paper we present, focusing on the adopted algorithmic and protocol-related solutions, a whole braincomputer interface system based on the steady-state visual evoked potentials (SSVEPs) paradigm
In a BCI system, SSVEPs are used by simultaneously displaying several stimuli flickering at different frequencies

Summary

Introduction

A brain-computer interface (BCI) is a system dedicated at providing its users with a new and alternative communication channel totally independent from the traditional output pathways of the nervous system such as peripheral nerves and muscles [1]. A BCI system achieves this goal by directly interfacing the cerebral activity, being it evoked or self-induced, with a common personal computer which nowadays represents a powerful and affordable platform for productivity, entertainment, worldwide communication, and remote control. In this paper we present, focusing on the adopted algorithmic and protocol-related solutions, a whole braincomputer interface system based on the steady-state visual evoked potentials (SSVEPs) paradigm. A user is able to select one specific stimulus by focusing on it leading to an increased amplitude localized on those frequency bands related to the flickering frequency of the stimulus itself

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Conclusion