arXiv subjects

Guang Ouyang

Publications and source records attributed to Guang Ouyang.

3 recordsLinked to original sources

arXiv2025

Handwriting Imagery EEG Classification based on Convolutional Neural Networks

Handwriting imagery has emerged as a promising paradigm for brain-computer interfaces (BCIs) aimed at translating brain activity into text output. Compared with invasively recorded electroencephalography (EEG), non-invasive recording offers a more practical and feasible approach to capturing brain signals for BCI. This study explores the limit of decoding non-invasive EEG associated with handwriting imagery into English letters using deep neural networks. To this end, five participants were instructed to imagine writing the 26 English letters with their EEG being recorded from the scalp. A measurement of EEG similarity across letters was conducted to investigate letter-specific patterns in the dataset. Subsequently, four convolutional neural network (CNN) models were trained for EEG classification. Descriptively, the EEG data clearly exhibited letter-specific patterns serving as a proof-of-concept for EEG-to-text translation. Under the chance level of accuracy at 3.85%, the CNN classifiers trained on each participant reached the highest limit of around 20%. This study marks the first attempt to decode non-invasive EEG associated with handwriting imagery. Although the achieved accuracy is not sufficient for a usable brain-to-text BCI, the model's performance is noteworthy in revealing the potential for translating non-invasively recorded brain signals into text outputs and establishing a baseline for future research.

eess.SP↗

arXiv2023

A Mixed-Membership Model for Social Network Clustering

We propose a simple mixed membership model for social network clustering in this paper. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies based on synthetic networks for further assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly.

stat.AP↗

arXiv2018

Clique-based Method for Social Network Clustering

In this article, we develop a clique-based method for social network clustering. We introduce a new index to evaluate the quality of clustering results, and propose an efficient algorithm based on recursive bipartition to maximize an objective function of the proposed index. The optimization problem is NP-hard, so we approximate the semi-optimal solution via an implicitly restarted Lanczos method. One of the advantages of our algorithm is that the proposed index of each community in the clustering result is guaranteed to be higher than some predetermined threshold, $p$, which is completely controlled by users. We also account for the situation that $p$ is unknown. A statistical procedure of controlling both under-clustering and over-clustering errors simultaneously is carried out to select localized threshold for each subnetwork, such that the community detection accuracy is optimized. Accordingly, we propose a localized clustering algorithm based on binary tree structure. Finally, we exploit the stochastic blockmodels to conduct simulation studies and demonstrate the accuracy and efficiency of our algorithms, both numerically and graphically.

cs.SI↗