OBJECTIVE: The current state-of-the-art methods significantly improve the detection performance of the steady-state visual evoked potentials (SSVEPs) by using the individual calibration data. However, the time-consuming calibration sessions limit the number of training trials and may give rise to visual fatigue, which weakens the effectiveness of the individual training data. For addressing this issue, this study proposes a novel inter- and intra-subject maximal correlation (IISMC) method to enhance the robustness of SSVEP recognition via employing the inter- and intra-subject similarity and variability. Through efficient transfer learning, similar experience under the same task is shared across subjects. METHODS: IISMC extracts subject-specific information and similar task-related information from oneself and other subjects performing the same task by maximizing the inter- and intra-subject correlation. Multiple weak classifiers are built from several existing subjects and then integrated to construct the strong classifiers by the average weighting. Finally, a powerful fusion predictor is obtained for target recognition. RESULTS: The proposed framework is validated on a benchmark data set of 35 subjects, and the experimental results demonstrate that IISMC obtains better performance than the state of the art task-related component analysis (TRCA). SIGNIFICANCE: The proposed method has great potential for developing high-speed BCIs.
OBJECTIVE: The current state-of-the-art methods significantly improve the detection performance of the steady-state visual evoked potentials (SSVEPs) by using the individual calibration data. However, the time-consuming calibration sessions limit the number of training trials and may give rise to visual fatigue, which weakens the effectiveness of the individual training data. For addressing this issue, this study proposes a novel inter- and intra-subject maximal correlation (IISMC) method to enhance the robustness of SSVEP recognition via employing the inter- and intra-subject similarity and variability. Through efficient transfer learning, similar experience under the same task is shared across subjects. METHODS: IISMC extracts subject-specific information and similar task-related information from oneself and other subjects performing the same task by maximizing the inter- and intra-subject correlation. Multiple weak classifiers are built from several existing subjects and then integrated to construct the strong classifiers by the average weighting. Finally, a powerful fusion predictor is obtained for target recognition. RESULTS: The proposed framework is validated on a benchmark data set of 35 subjects, and the experimental results demonstrate that IISMC obtains better performance than the state of the art task-related component analysis (TRCA). SIGNIFICANCE: The proposed method has great potential for developing high-speed BCIs.