Steady-state visually evoked potential (SSVEP)-based brain-computer interface (BCI): a low-delayed asynchronous wheelchair control system

Authors:
Zhuo Xu;Jie Li;Rong Gu;Bin Xia
Affiliations:
School of Electronics and Information Engineering, Tongji University, China;School of Electronics and Information Engineering, Tongji University, China;School of Electronics and Information Engineering, Tongji University, China;Institute of Information Engineering, Shanghai Maritime University, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Year:
2012

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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

Computational Intelligence and Neuroscience

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Abstract

The aim of the present study was to propose an effective and low-delayed asynchronous SSVEPs-based BCI system for practical wheelchair control. The paradigm was based on the discrimination of Steady-state visually evoked potential (SSVEP) which is widely applied to various audiences. Bayesian Classifier and a low-delayed asynchronous detection mechanism were devised and integrated to enable the user to control the wheelchair flexibly. In particular, comparing with the traditional method using a fix threshold or a simple classification model to distinguish idle state and task state, our detection mechanism exhibited higher accuracy and possessed a better performance for wheelchair. Five subjects took part in our offline task and two of them continued the on-line task on a real wheelchair. In average, we achieved a classification accuracy of 87.17% in task state and 92.70% in idle state and two subjects accomplished on-line task using 187 s and 298 s, respectively.