Fast Multi-command SSVEP Brain Machine Interface without Training

Authors:
Pablo Martinez Vasquez;Hovagim Bakardjian;Montserrat Vallverdu;Andrezj Cichocki
Affiliations:
Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi Saitama, Japan;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi Saitama, Japan;Universidad Politécnica de Cataluña, Barcelona, Spain;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi Saitama, Japan
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

Citing 2
Cited 2

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Fully online multicommand brain-computer interface with visual neurofeedback using SSVEP paradigm

Computational Intelligence and Neuroscience - EEG/MEG Signal Processing

A survey of stimulation methods used in SSVEP-based BCIs

Computational Intelligence and Neuroscience
An open source stimulator for SSVEP-based BCIs

Proceedings of the 50th Annual Southeast Regional Conference

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Abstract

We propose a new multi-stage procedure for a real time brain machine/computer interface (BMI) based on the Steady State Visual Evoked Potentials (SSVEP) paradigm elicited by means of flickering checkerboards. The developed system work in asynchronous mode and it does not require training phase and its able to detect fast multiple independent visual commands. Up to 8 independent commands, were tested at the presented work and the proposed BMI system could be extended to more independent commands easily. The system has been extensively experimented with 4 young healthy subjects, confirming the high performance of the proposed procedure and its robustness in respect to artifacts.