Daniel Leong

Beining Cao, Xiaowei Jiang, Charlie Li-Ting Tsai et al.

Steady-state visual evoked potential (SSVEP) is widely used in brain-computer interfaces (BCIs) due to its reliability. With the integration of augmented reality (AR), AR-SSVEP enables more intuitive interaction by embedding visual stimuli into real-world environments. However, unlike conventional computer screen-based SSVEP (CS-SSVEP) systems with stable visual conditions, AR-SSVEP performance is influenced by real-world scene factors, such as luminance and color, which degrade stimulus perception and weaken SSVEP elicitation. Nevertheless, existing studies primarily focus on offline analyses of SSVEP-related factors in indoor settings, while online adaptive optimization for outdoor AR-SSVEP remains limited. Therefore, a scenario-aware spatial layout optimization (SASLO) system for AR-SSVEP is proposed, which jointly considers scene luminance and inter-stimulus distance (ISD) for adaptive stimulus layout optimization. Scene luminance is estimated using an RGB-CIE based method, and the extracted context is incorporated into a linear contextual bandit (LCB) model to recommend optimized spatial layouts. Two pilot single-factor experiments are conducted to characterize the effects of luminance and ISD on SSVEP performance and to construct reliable rewards for model training. An outdoor online experiment with ten subjects further validates the proposed joint optimization method, achieving an average accuracy of 0.89 and an information transfer rate of 35.74 bits/min with a 3 s input window, and consistently outperforming two baseline methods. Overall, the proposed SASLO system is shown to improve the robustness of AR-SSVEP in real-world outdoor environments.

Jinzhao Zhou, Zehong Cao, Yiqun Duan et al.

This paper explores silent speech decoding in active brain-computer interface (BCI) systems, which offer more natural and flexible communication than traditional BCI applications. We collected a new silent speech dataset of over 120 hours of electroencephalogram (EEG) recordings from 12 subjects, capturing 24 commonly used English words for language model pretraining and decoding. Following the recent success of pretraining large models with self-supervised paradigms to enhance EEG classification performance, we propose Large Brain Language Model (LBLM) pretrained to decode silent speech for active BCI. To pretrain LBLM, we propose Future Spectro-Temporal Prediction (FSTP) pretraining paradigm to learn effective representations from unlabeled EEG data. Unlike existing EEG pretraining methods that mainly follow a masked-reconstruction paradigm, our proposed FSTP method employs autoregressive modeling in temporal and frequency domains to capture both temporal and spectral dependencies from EEG signals. After pretraining, we finetune our LBLM on downstream tasks, including word-level and semantic-level classification. Extensive experiments demonstrate significant performance gains of the LBLM over fully-supervised and pretrained baseline models. For instance, in the difficult cross-session setting, our model achieves 47.0\% accuracy on semantic-level classification and 39.6\% in word-level classification, outperforming baseline methods by 5.4\% and 7.3\%, respectively. Our research advances silent speech decoding in active BCI systems, offering an innovative solution for EEG language model pretraining and a new dataset for fundamental research.