关键词: 改进的希尔伯特-黄变换, 高频组合编码, 脑-机接口, 稳态视觉诱发电位, 智能轮椅

Abstract: A new steady-state visual evoked potential(SSVEP) paradigm for brain computer interface system(BCIs) is proposed in order to solve the problems of lower stability and information transfer rate(ITR), duing to the shortages of target number less, easy to make subjects fatigue and increase the risk of photosensitive epileptic seizures in the traditional low frequency SSVEP-BCIs. The new paradigm is time series combination coding-based high-frequency SSVEP(CCH-SSVEP). The CCH-SSVEP paradigm produces n with n high stimulation frequencies through time series combination code in order to solve the problem of few targets in traditional SSVEP-BCIs utilizing single frequency to encode each target. Furthermore, an improved Hilbert-huang transform(IHHT)-based variable frequency EEG feature extraction method and a local spectrum extreme target identification algorithm are adopted to extract time-frequency feature of the proposed CCH-SSVEP response. Linear predictions and fixed sifting(iterating) 10 times are used to overcome the shortages of end effect and stopping criterion of empirical mode decomposition(EMD) in the processing of variable frequency EEG data, generalized zero-crossing(GZC) is used to compute the instantaneous frequency of the proposed SSVEP respondent signals, the differentiation combination method is proposed to select the combination coding sequence in order to increase the recognition rate, in the result, the ITR and the stability of the CCH-SSVEP-BCI system are Improved. What is more, SSVEPs evoked by high-frequency stimuli(beyond 25 Hz) minimally diminish subjects fatigue and prevent safety hazards linked to photo-induced epileptic seizures, utilizing the flicker fusion effect. Six stimulus targets are presented with three high frequencies through CCH-SSVEP, as a contrast, three stimulus targets are presented with three low frequencies through traditional SSVEP, the above two kinds of different contrast experiments are applied to intelligent wheelchair navigation control in order to verify the technical advantage of the proposed method and ensure the CCH-SSVEP-based intelligent wheelchair navigation system efficiency and undamaging.

Key words: Brain-computer interfaces(BCI), Improved Hilbert-huang transform(IHHT), Intelligent wheelchair, Steady-state visual evoked potentials(SSVEP), Time series combination coding-based high-frequency(CCH)