Jianwen Sun

MSc Semester Project


Detection and Analysis of steady state visual evoked potentials for BCI applications


Jianwen Sun


Prof. Dr. Touradj Ebrahimi

Assistant: Ashkan Yazdani


June 11, 2010




The steady state visual evoked potential (SSVEP) is the oscillatory wave appearing in the occipital leads of the electroencephalogram (EEG) in response to a visual stimulus modulated at a certain frequency (e.g. pattern reversed checkerboard, flickering LEDs). The frequency of the SSVEP matches that of the stimulus or its harmonics. In SSVEP based BCIs, visual stimulus modulated at different frequencies are simultaneously presented to the user. Each pattern is associated with an action in an output (active) device. When the user focuses his/her attention on a certain pattern, the corresponding stimulating frequency (or its harmonics) dominantly appears in the spectral representation of the EEG signals recorded at occipital sites. The action associated to the dominant frequency is then performed.
In this project, a simple visual stimuli presentation protocol will be used and after data acquisition, the feasibility and accuracy of SSVEP detection for different frequencies will be studied. The outcome of this study allows selection of the most appropriate flickering frequencies for SSVEP based BCIs. More specifically, the goal of this project is to study different approaches for SSVEP signal detection, feature extraction and classification. To this end, the following tasks have to be performed:

  • Studying different signal acquisition paradigms for SSVEP.
  • Reviewing the state of the art of EEG signal processing and existing literature on SSVEP signal detection and analysis and selection of appropriate methods for signal enhancement, feature extraction, feature classification.
  • Implementation of the selected methods and testing it on existing data.
  • Data acquisition using a simple visual stimuli presentation algorithm.
  • Analysis of the acquired data and assessment of the feasibility and accuracy of detecting the correct stimuli frequency by means of data processing.
    • Studying the performance of the system for different flickering frequencies.
  • Studying the speed of the system i.e. how long it takes until the system recognizes the correct flicker frequency.
  • Assess and compare the performance of different approaches for feature extraction.