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

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.

An independent SSVEP-based brain–computer interface in locked-in syndrome

“…The learning effect on communication performances, mental workload and user satisfaction should also be part of future researches. The accuracies obtained in patients are lower than those presented by (1) Parini et al [35] on patients with Duchenne muscular dystrophy using a four-class overt SSVEP-BCI and (2) Combaz et al [36] using an overt SSVEP speller in patients with incomplete LIS. Kübler and Birbaumer tested a visual P300 speller with patients [37].…”

Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces

“…There had been many supervised classification methods, such as DLSVQ, LAS and joint time-frequency analysis (JTFA) (Müller-Putz et al, 2005;Parini et al, 2009), proposed and utilized for SSVEPbased BCI systems. The recognition accuracy by our approach was nearly equal to those of these algorithms.…”

“…This signal is often extracted noninvasively from electroencephalography (EEG) for brain computer interfaces (BCIs). Recent studies indicated that SSVEPs had higher classification accuracy than other EEG patterns, such as P300 and event-related desynchronization/synchronization (ERD/ERS) Parini et al, 2009;Guger et al, 2012). And a great number of SSVEP-based BCIs had been developed for human-computer communication (Wolpaw et al, 2000(Wolpaw et al, , 2002Moore, 2003;Kelly et al, 2005;Xia et al, 2013).…”

Brain–computer interfacing in disorders of consciousness

“…Progressive improvements in the design have produced systems that allow for impressive rate of communication. Parini et al [97] showed performance results from a SSVEP-based BCI that employed four cubic LED stimuli mounted at each side of a display. Seven healthy participants and four patients affected by muscular dystrophy at different stages were able to successfully use this system.…”