TY - JOUR
T1 - A robust and self-paced BCI system based on a four class SSVEP paradigm
T2 - Algorithms and protocols for a high-transfer-rate direct brain communication
AU - Parini, Sergio
AU - Maggi, Luca
AU - Turconi, Anna C.
AU - Andreoni, Giuseppe
PY - 2009
Y1 - 2009
N2 - 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 70bit/min) and very robust to false positive identifications.
AB - 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 70bit/min) and very robust to false positive identifications.
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U2 - 10.1155/2009/864564
DO - 10.1155/2009/864564
M3 - Article
C2 - 19421416
AN - SCOPUS:67449112974
VL - 2009
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
SN - 1687-5265
M1 - 864564
ER -