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SSVEP-BCIs have attracted extensive attention because of high information transfer rate. High-speed BCIs need to collect sufficient user's own data to train optimal subject-specific parameters. However, one of the challenges which limits the real-life application of BCIs is the time-consuming and tiring calibration process. This study developed two cross-subject frameworks. One of them uses data from all training subjects to train task-related component analysis based spatial filters (all-to-one, A2O), and the other uses data from each training subject to train task-related component analysis based spatial filters (one-to-one, O2O). Both of them do not need calibration process for a new user. The study further proposed O2O with threshold (O2O-Thr) to increase the reliability of recognition process. The proposed strategies can exploit information from existing subjects' SSVEP data and transfer it to new users. The performance of these methods was compared using an 8-class SSVEP dataset recorded from 10 subjects. O2O-Thr achieves average accuracy of 94.6% with data length of 1.5 seconds. The proposed methods have great potential for building subject-independent BCI that do not require any calibration data from new users, which make BCI more practical and user-friendly.

The role of detecting work/idle state in asynchronous Steady-state visual evoked potential (SSVEP) Brain-computer interface (BCI) or a self-paced SSVEP BCI has received increased attention in recent years. This study proposed a tree structure method which identifies the work/idle state based on the frequency recognition to detect work/idle state. Firstly, a frequency recognition estimated with task-related component analysis (TRCA). Then, the work/idle state is classified with step-wise linear discriminant analysis (SWLDA) using the data fusion of TRCA scores and power spectral density (PSD) as features. This method was evaluated by Electroencephalography (EEG) data from fourteen healthy participants with eight frequencies as work states and three idle state conditions. The averaged AUC of this method achieved 0.89 with data lengths of one second, which was significantly higher than that of the conventional power spectrum-based algorithm. The proposed method could identify the work/idle state fast and accurately, making the SSVEP BCI better suited for practical application.

Steady-State Visual Evoked Potentials (SSVEP) based Brain-Computer Interface (BCI) has achieved very high information transmission rate (ITR), but its portability and fundamental interactions with the surrounding environment were limited. The combination of Augmented Reality (AR) and BCI is expected to solve these problems. In this paper, we combined AR with the SSVEP-BCI to build a more portable and natural BCI system in Microsoft HoloLens. We designed the AR-BCI system and studied the influence of different algorithms on the system performance. The analysis of SSVEP signals collected in AR environment shows that the extended filter bank canonical correlation analysis was better than task-related component analysis. The average recognition accuracy and ITR obtained by using Electroencephalography (EEG) data of 1s, 1.5s, and 2s length were 87.7%,95.4%, 97.6% and 64.6 bit/min, 62.9 bit/min, 55.6 bit/min, respectively. Compared with the existing AR-BCI studies, the ITR has been greatly improved in this study.

• An efficient cross-subject transfer method for SSVEP-BCI was proposed for reducing calibration effort. • Minimal data from a target subject was used for SSVEP similarity calculation between the target subject and scource subjects. • A transferability evaluation model was developed to assign weights to source subjects based on similarities. • Cross-subject transfer SSVEP decoding performance using the proposed method were evaluated. • The proposed method reduced calibration time and improved cross-subject transfer performance for SSVEP decoding. Cross-subject steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have attracted increasing attention in recent years due to their potential to reduce calibration efforts and improve usability. This study proposed an efficient cross-subject transfer method for SSVEP-BCI, which significantly reduced the calibration effort and enhanced system performance. By employing a minimal dataset obtained from the target subject, specifically, a single trial for each frequency, we calculated the similarity between the SSVEPs of the target subject and the source subjects. Based on the similarities, a transferability evaluation model was constructed, which was then used to assign weights to the source subjects. The features calculated from the source subjects were then weighted and summed to obtain the frequency recognition results. The proposed cross-subject transfer method was evaluated in two pipelines based on the ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA), respectively. The proposed method in eTRCA-pipeline initially achieving accuracies of 96.98 ± 3.66 % and 88.97 ± 14.66 % in our dataset and the Benchmark dataset, respectively, at a data length of 1 s. After fusing the features of eTRCA with those of the extended canonical correlation analysis (eCCA), the method exhibited enhanced performance, elevating accuracies to 98.23 ± 2.68 % and 92.83 ± 10.75 %. Similarly, in TDCA-pipeline, accuracies initially reached 97.47 ± 3.88 % and 93.53 ± 10.22 % in our dataset and the Benchmark dataset, respectively, and after fusing TDCA features with eCCA features, they remarkably increased to 98.13 ± 2.89 % and 95.00 ± 8.43 %, respectively. This study offers a promising solution for the fast and effective calibration of SSVEP-BCI, paving the way for more practical and accessible BCI systems. [ABSTRACT FROM AUTHOR]