Jiazhen Hong

Jiazhen Hong, Weinan Wang, Laleh Najafizadeh

P300 speller BCIs allow users to compose sentences by selecting target keys on a GUI through the detection of P300 component in their EEG signals following visual stimuli. Most P300 speller BCIs require users to spell words letter by letter, or the first few initial letters, resulting in high keystroke demands that increase time, cognitive load, and fatigue. This highlights the need for more efficient, user-friendly methods for faster sentence composition. In this work, we introduce ChatBCI, a P300 speller BCI that leverages the zero-shot learning capabilities of large language models (LLMs) to suggest words from user-spelled initial letters or predict the subsequent word(s), reducing keystrokes and accelerating sentence composition. ChatBCI retrieves word suggestions through remote queries to the GPT-3.5 API. A new GUI, displaying GPT-3.5 word suggestions as extra keys is designed. SWLDA is used for the P300 classification. Seven subjects completed two online spelling tasks: 1) copy-spelling a self-composed sentence using ChatBCI, and 2) improvising a sentence using ChatBCI's word suggestions. Results demonstrate that in Task 1, on average, ChatBCI outperforms letter-by-letter BCI spellers, reducing time and keystrokes by 62.14% and 53.22%, respectively, and increasing information transfer rate by 198.96%. In Task 2, ChatBCI achieves 80.68% keystroke savings and a record 8.53 characters/min for typing speed. Overall, ChatBCI, by employing remote LLM queries, enhances sentence composition in realistic scenarios, significantly outperforming traditional spellers without requiring local model training or storage. ChatBCI's (multi-) word predictions, combined with its new GUI, pave the way for developing next-generation speller BCIs that are efficient and effective for real-time communication, especially for users with communication and motor disabilities.

Jiazhen Hong, Geoffrey Mackellar, Soheila Ghane

Electroencephalogram (EEG) signals generally exhibit low signal-to-noise ratio (SNR) and high inter-subject variability, making generalization across subjects and domains challenging. Recent advances in deep learning, particularly self-supervised learning with Transformer-based architectures, have shown promise in EEG representation learning. However, their quadratic computational complexity increases memory usage and slows inference, making them inefficient for modeling long-range dependencies. Moreover, most existing approaches emphasize either explicit window segmentation of the temporal signal or spectral-only input embedding while neglecting raw temporal dynamics. In this paper, we propose EEGM2, a self-supervised framework that overcomes these limitations. EEGM2 adopts a U-shaped encoder-decoder architecture integrated with Mamba-2 to achieve linear computational complexity, thereby reducing memory usage and improving inference speed. Meanwhile, the selective information propagation mechanism of Mamba-2 enables the model to effectively capture and preserve long-range dependencies in raw EEG signals, where traditional RNN or CNN architectures often struggle. Moreover, EEGM2 employs a self-supervised pre-training objective that reconstructs raw EEG using a combined L1 and spectral (Fourier-based) loss, enhancing generalization by jointly preserving temporal dynamics and spectral characteristics. Experimental results demonstrate that EEGM2 achieves state-of-the-art performance in both short- and long-sequence modeling and classification. Further evaluations show that EEGM2 consistently outperforms existing models, demonstrating strong generalization across subjects and tasks, as well as transferability across domains. Overall, EEGM2 offers an efficient and scalable solution suitable for deployment on resource-constrained brain-computer interface (BCI) devices.

Jiazhen Hong, Wei Qian, Yudong Chen et al.

\kmeans clustering is a fundamental problem in many scientific and engineering domains. The optimization problem associated with \kmeans clustering is nonconvex, for which standard algorithms are only guaranteed to find a local optimum. Leveraging the hidden structure of local solutions, we propose a general algorithmic framework for escaping undesirable local solutions and recovering the global solution or the ground truth clustering. This framework consists of iteratively alternating between two steps: (i) detect mis-specified clusters in a local solution, and (ii) improve the local solution by non-local operations. We discuss specific implementation of these steps, and elucidate how the proposed framework unifies many existing variants of \kmeans algorithms through a geometric perspective. We also present two natural variants of the proposed framework, where the initial number of clusters may be over- or under-specified. We provide theoretical justifications and extensive experiments to demonstrate the efficacy of the proposed approach.