For decades, a great deal of interest in investigating brain network functional connective features has arisen in brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). Traditional decoding algorithms, for example, canonical correlation analysis (CCA), only consider the inherent properties of each channel in terms of feature extraction for the single channel electroencephalogram (EEG) signal, with inadequate features that cannot fully utilize the information transmitted by the brain.

This paper proposes a fused feature extraction method, CCA-DTF, which combines CCA with a direct transfer function (DTF) to construct local brain network features with seven leads in the occipital region. First, the features extracted by the CCA algorithm were combined with these features extracted by DTF to analyze the EEG data from 20 subjects. Then, two methods, support vector machine (SVM) and random forest (RF), were used in constructing the classifiers for the four tasks classification of the SSVEP-BCI.

The experimental results showed that incorporating local network features (extracted from DTF or Pearson correlation coefficient) can effectively improve the classification average accuracy and the information transfer rate (ITR) of SSVEP, not only for SVM but also for the ensemble method RF. In particular, CCA-DTF plus SVM obtained a 94.52% classification average accuracy and a 49.23 bits/min ITR in a time window of 2 seconds. The performance was 5.57% and 8.01 bits/min higher than those of traditional CCA plus SVM, respectively.

The proposed feature extraction method based on local network features is robust for improving the performance of SSVEP-BCI significantly, which has a perspective of being used in neural rehabilitation engineering field.