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123 results on '"Li, Jingcong"'

1. A Novel Self-Supervised Learning Method for Sleep Staging and its Pilot Study on Patients with Disorder of Consciousness

Author

Li, Jingcong, Chen, Quanlin, Pan, Jiahui, and Huang, Haiyun

Subjects
Artificial Intelligence, Consciousness, Sleep, Electroencephalography (EEG), Neural Networks
Abstract

Sleep staging holds significant importance in clinical medicine, aiding in the diagnosis of various disorders related to sleep and cognition. However, manually annotating a large amount of sleep data is time-consuming and labor-intensive, making it difficult to achieve. Efficiently utilizing these unannotated data poses a challenging task. We propose a novel self-supervised learning method with Temporal-split Contrastive and Electrode Autoencoder (TsC-EA) for sleep staging. We demonstrate that our method achieves state-of-the-art performance in self-supervised learning on SleepEDF and MASS-SS3. Moreover, experimental results indicate that our method can surpass the performance of supervised learning methods using only 10% of labeled data. Additionally, we explore the application of self-supervised learning in patients with disorder of consciousness. It can assist in diagnosing the severity of DoC through analysis of sleep staging. Staging the sleep patterns of patients with disorders of consciousness can help in diagnosing the severity of their condition.

Published
2024

2. An Investigation on EEG-based Prognosis Prediction of Patients with Disorders of Consciousness

Author

Li, Jingcong, Huang, Biao, Pan, Jiahui, and Wang, Fei

Subjects
Artificial Intelligence, Cognitive Neuroscience, Consciousness, Electroencephalography (EEG), Neural Networks
Abstract

Prognostic assessment of patients with disorders of consciousness (DoC) remains one of the most challenging problems in contemporary medicine. The long treatment cycle and high costs of treatment are heavy burdens to our society. In this paper, we use deep network to investigate potential indicators of consciousness within brain signals of DoC patients. In the experiments, we study P300 and resting-state Electroencephalogram (rs-EEG) signals of 22 DoC patients to investigate neural correlation between brain signals and the improvement of consciousness. Synergistic integration of P300 and rs-EEG signals demonstrated superior predictive proficiency for cross-subject and cross-paradigm prognosis in DoC, achieving an accuracy of 81.1%. Our investigation is the first known to the literature to combine P300 and rs-EEG signals for analyzing DoC. This novel approach leverages advanced neural network models to elucidate the complex neural patterns associated with DoC, setting a precedent for future research in the field.

Published
2024

3. A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface

Author

Li, Jingcong, Wang, Fei, Huang, Haiyun, Qi, Feifei, and Pan, Jiahui

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Brain-computer interface (BCI) provides a direct communication pathway between human brain and external devices. Before a new subject could use BCI, a calibration procedure is usually required. Because the inter- and intra-subject variances are so large that the models trained by the existing subjects perform poorly on new subjects. Therefore, effective subject-transfer and calibration method is essential. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer learning in BCIs. The proposed SSML learns a meta model with the existing subjects first, then fine-tunes the model in a semi-supervised learning manner, i.e. using few labeled and many unlabeled samples of target subject for calibration. It is significant for BCI applications where the labeled data are scarce or expensive while unlabeled data are readily available. To verify the SSML method, three different BCI paradigms are tested: 1) event-related potential detection; 2) emotion recognition; and 3) sleep staging. The SSML achieved significant improvements of over 15% on the first two paradigms and 4.9% on the third. The experimental results demonstrated the effectiveness and potential of the SSML method in BCI applications.

Published
2022

11. ST-SCGNN: A Spatio-Temporal Self-Constructing Graph Neural Network for Cross-Subject EEG-Based Emotion Recognition and Consciousness Detection

Author

Pan, Jiahui, Liang, Rongming, He, Zhipeng, Li, Jingcong, Liang, Yan, Zhou, Xinjie, He, Yanbin, and Li, Yuanqing

Abstract

In this paper, a novel spatio-temporal self-constructing graph neural network (ST-SCGNN) is proposed for cross-subject emotion recognition and consciousness detection. For spatio-temporal feature generation, activation and connection pattern features are first extracted and then combined to leverage their complementary emotion-related information. Next, a self-constructing graph neural network with a spatio-temporal model is presented. Specifically, the graph structure of the neural network is dynamically updated by the self-constructing module of the input signal. Experiments based on the SEED and SEED-IV datasets showed that the model achieved average accuracies of 85.90% and 76.37%, respectively. Both values exceed the state-of-the-art metrics with the same protocol. In clinical besides, patients with disorders of consciousness (DOC) suffer severe brain injuries, and sufficient training data for EEG-based emotion recognition cannot be collected. Our proposed ST-SCGNN method for cross-subject emotion recognition was first attempted in training in ten healthy subjects and testing in eight patients with DOC. We found that two patients obtained accuracies significantly higher than chance level and showed similar neural patterns with healthy subjects. Covert consciousness and emotion-related abilities were thus demonstrated in these two patients. Our proposed ST-SCGNN for cross-subject emotion recognition could be a promising tool for consciousness detection in DOC patients.

Published
2024
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24. sj-pdf-1-ccx-10.1177_10732748211048292 – Supplemental Material for Fecal CEA Has an Advantage in the Diagnosis of Colorectal Cancer at Early Stage

Author

Li, Linfang, Xing, Shan, Wu, Miantao, Ao, Yufeng, Zheng, Xin, Cai, Rongzeng, Han, Runkun, Li, Jingcong, Li, Xiaohui, and Zeng, Qiuyao

Subjects
FOS: Clinical medicine, 111299 Oncology and Carcinogenesis not elsewhere classified
Abstract

Supplemental Material, sj-pdf-1-ccx-10.1177_10732748211048292 for Fecal CEA Has an Advantage in the Diagnosis of Colorectal Cancer at Early Stage by Linfang Li, Shan Xing, Miantao Wu, Yufeng Ao, Xin Zheng, Rongzeng Cai, Runkun Han, Jingcong Li, Xiaohui Li and Qiuyao Zeng in Cancer Control

Published
2021
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