Search

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.

High-Definition transcranial direct current stimulation (HD-tDCS) is a noninvasive brain stimulation technique that can improve the performance of working memory (WM). However, the current researches have focused on the effects of stimulation, while ignored the process of stimulus and the neural mechanism. The targets of this study were to explore the effects of different stimulus categories (active or sham) applied on left dorsolateral prefrontal cortex (LDLPFC) on WM training, as well as the physiological changes in the brain after training. Behavioral and electroencephalography (EEG) results of 20 participants showed that HD-tDCS significantly enhanced training effects in the later training period. Furthermore, WM ability benefited from training combined with HD-tDCS, and active group found the time-dependent desynchronization (ERD) weakened in α and β band, while sham group increased. The results supported the viewpoint that HD-tDCS can shorten the training time and alter neurons rhythm, it may be used as psychotherapy for the patients with brain injury.

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.