Inter- and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-Based BCIs.

OBJECTIVE: Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver a high information transfer rate (ITR) usually require subject's calibration data to learn the class- and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration. This study presents a transfer learning scheme to substantially reduce the calibration effort. METHODS: Inspired by the parameter-based and instance-based transfer learning techniques, we propose a subject transfer based canonical correlation analysis (stCCA) method which utilizes the knowledge within subject and between subjects, thus requiring few calibration data from a new subject. RESULTS: The evaluation study on two SSVEP datasets (from Tsinghua and UCSD) shows that the stCCA method performs well with only a small amount of calibration data, providing an ITR at 198.18±59.12 (bits/min) with 9 calibration trials in the Tsinghua dataset and 111.04±57.24 (bits/min) with 3 trials in the UCSD dataset. Such performances are comparable to those from using the multi-stimulus CCA (msCCA) and the ensemble task-related component analysis (eTRCA) methods with the minimally required calibration data (i.e., at least 40 trials in the Tsinghua dataset and at least 12 trials in the UCSD dataset), respectively. CONCLUSION: Inter- and intra-subject transfer helps the recognition method achieve high ITR with extremely little calibration effort. SIGNIFICANCE: The proposed approach saves much calibration effort without sacrificing the ITR, which would be significant for practical SSVEP-based BCIs.

Spatial Filtering in SSVEP-Based BCIs: Unified Framework and New Improvements.

OBJECTIVE: In the steady-state visual evoked potential (SSVEP)-based brain computer interfaces (BCIs), spatial filtering, which combines the multi-channel electroencephalography (EEG) signals in order to reduce the non-SSVEP-related component and thus enhance the signal-to-noise ratio (SNR), plays an important role in target recognition. Recently, various spatial filtering algorithms have been developed employing different prior knowledge and characteristics of SSVEPs, however how these algorithms interconnect and differ is not yet fully explored, leading to difficulties in further understanding, utilizing and improving them. METHODS: We propose a unified framework under which the spatial filtering algorithms can be formulated as generalized eigenvalue problems (GEPs) with four different elements: data, temporal filter, orthogonal projection and spatial filter. Based on the framework, we design new spatial filtering algorithms for improvements through the choice of different elements. RESULTS: The similarities, differences and relationships among nineteen mainstream spatial filtering algorithms are revealed under the proposed framework. Particularly, it is found that they originate from the canonical correlation analysis (CCA), principal component analysis (PCA), and multi-set CCA, respectively. Furthermore, three new spatial filtering algorithms are developed with enhanced performance validated on two public SSVEP datasets with 45 subjects. CONCLUSION: The proposed framework provides insights into the underlying relationships among different spatial filtering algorithms and helps the design of new spatial filtering algorithms. SIGNIFICANCE: This is a systematic study to explore, compare and improve the existing spatial filtering algorithms, which would be significant for further understanding and future development of high performance SSVEP-based BCIs.

Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs.

OBJECTIVE: Latest target recognition methods that are equipped with learning from the subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. APPROACH: A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. MAIN RESULTS: Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. SIGNIFICANCE: A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs.