Jiafa Chen

Prof. Jiafa Chen

orcid

Research Focus & Metrics

Academic Career Length

3Years (2024-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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31 March 2024 in Electronics
Verified
Journal Article
Open access
PDF file
JCJiafa Chen
Jiafa Chen
KYKaiwei Yu
Kaiwei Yu
Fei WangFei Wang
Fei Wang
ZZZhengxian Zhou
Zhengxian Zhou
YBYifei Bi
Yifei Bi
DZDawei Zhang
Dawei Zhang
Emotion recognition remains an intricate task at the crossroads of psychology and artificial intelligence, necessitating real-time, accurate discernment of implicit emotional states. Here, we introduce a pioneering wearable dual-modal device, synergizing functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to meet this demand. The first-of-its-kind fNIRS-EEG ensemble exploits a temporal convolutional network (TC-ResNet) that takes 24 fNIRS and 16 EEG channels as input for the extraction and recognition of emotional features. Our system has many advantages including its portability, battery efficiency, wireless capabilities, and scalable architecture. It offers a real-time visual interface for the observation of cerebral electrical and hemodynamic changes, tailored for a variety of real-world scenarios. Our approach is a comprehensive emotional detection strategy, with new designs in system architecture and deployment and improvement in signal processing and interpretation. We examine the interplay of emotions and physiological responses to elucidate the cognitive processes of emotion regulation. An extensive evaluation of 30 subjects under four emotion induction protocols demonstrates our bimodal system’s excellence in detecting emotions, with an impressive classification accuracy of 99.81% and its ability to reveal the interconnection between fNIRS and EEG signals. Compared with the latest unimodal identification methods, our bimodal approach shows significant accuracy gains of 0.24% for EEG and 8.37% for fNIRS. Moreover, our proposed TC-ResNet-driven temporal convolutional fusion technique outperforms conventional EEG-fNIRS fusion methods, improving the recognition accuracy from 0.7% to 32.98%. This research presents a groundbreaking advancement in affective computing that combines biological engineering and artificial intelligence. Our integrated solution facilitates nuanced and responsive affective intelligence in practical applications, with far-reaching impacts on personalized healthcare, education, and human–computer interaction paradigms.

16 October 2024 in Brain Sciences
Verified
Journal Article
Open access
PDF file
JCJiafa Chen
Jiafa Chen
KYKaiwei Yu
Kaiwei Yu
YBYifei Bi
Yifei Bi
XJXing Ji
Xing Ji
DZDawei Zhang
Dawei Zhang
Background: Recent years have seen a surge of interest in dual-modality imaging systems that integrate functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to probe brain function. This review aims to explore the advancements and clinical applications of this technology, emphasizing the synergistic integration of fNIRS and EEG. Methods: The review begins with a detailed examination of the fundamental principles and distinctive features of fNIRS and EEG techniques. It includes critical technical specifications, data-processing methodologies, and analysis techniques, alongside an exhaustive evaluation of 30 seminal studies that highlight the strengths and weaknesses of the fNIRS-EEG bimodal system. Results: The paper presents multiple case studies across various clinical domains—such as attention-deficit hyperactivity disorder, infantile spasms, depth of anesthesia, intelligence quotient estimation, and epilepsy—demonstrating the fNIRS-EEG system’s potential in uncovering disease mechanisms, evaluating treatment efficacy, and providing precise diagnostic options. Noteworthy research findings and pivotal breakthroughs further reinforce the developmental trajectory of this interdisciplinary field. Conclusions: The review addresses challenges and anticipates future directions for the fNIRS-EEG dual-modal imaging system, including improvements in hardware and software, enhanced system performance, cost reduction, real-time monitoring capabilities, and broader clinical applications. It offers researchers a comprehensive understanding of the field, highlighting the potential applications of fNIRS-EEG systems in neuroscience and clinical medicine.

6 August 2024 in Medical & Biological Engineering & Computing
Verified
Journal Article
Closed access
PDF file
KYKaiwei Yu
Kaiwei Yu
JCJiafa Chen
Jiafa Chen
XDXian Ding
Xian Ding
DZDawei Zhang
Dawei Zhang

10 April 2024 in PLOS ONE
Verified
Journal Article
Open access
PDF file
JCJiafa Chen
Jiafa Chen
Fei WangFei Wang
Fei Wang
DZDawei Zhang
Dawei Zhang
JLJinsong Liu
Jinsong Liu
HWHuaxia Wu
Huaxia Wu
TTTianchen Tang
Tianchen Tang
This paper presents a groundbreaking Ku-band 20W RF front-end power amplifier (PA), designed to address numerous challenges encountered by satellite communication systems, including those pertaining to stability, linearity, cost, and size. The manuscript commences with an exhaustive discussion of system design and operational principles, emphasizing the intricacies of low-noise amplification, and incorporating key considerations such as noise factors, stability analysis, gain, and gain flatness. Subsequently, an in-depth study is conducted on various components of the RF chain, including the pre-amplification module, driver-amplification module, and final-stage amplification module. The holistic design extends to the inclusion of the display and control unit, featuring the power-control module, monitoring module, and overall layout design of the PA. It is meticulously tailored to meet the specific demands of satellite communication. Following this, a thorough exploration of electromagnetic simulation and measurement results ensues, providing validation for the precision and reliability of the proposed design. Finally, the feasibility of that design is substantiated through systematic system design, prototype production, and exhaustive experimental testing. It is noteworthy that, in the space-simulation environmental test, emphasis is placed on the excellent performance of the Star Ku-band PA within the 13.75GHz to 14.5GHz frequency range. Detailed power scan measurements reveal a P1dB of 43dBm, maintaining output power flatness < ± 0.5dBm across the entire frequency and temperature spectrum. Third-order intermodulation scan measurements indicate a third-order intermodulation of ≤ -23dBc. Detailed results of power monitoring demonstrate a range from +18dBm to +54dBm. Scans of spurious suppression and harmonic suppression, meanwhile, show that the PA evinces spurious suppression ≤ -65dBc and harmonic suppression ≤ -60dBc. Rigorous phase-scan measurements exhibit a phase-shift adjustment range of 0° to 360°, with a step of 5.625°, and a phase-shift accuracy of 0.5dB. Detailed data from gain-scan measurements show a gain-adjustment range of 0dB to 30dB, with a gain flatness of ± 0.5dB. Attenuation error is ≤ 1%. These test parameters perfectly align with the practical application requirements of the technical specifications. When compared to existing Ku-band PAs, our design reflects a deeper consideration of specific requirements in satellite communication, ensuring its outstanding performance and uniqueness. This PA features good stability, high linearity, low cost, and compact modularity, ensuring continuous and stable power output. These features position the proposed system as a leader within the market. Successful orbital deployment not only validates its operational stability; it also makes a significant contribution to the advancement of China’s satellite PA technology, generating positive socio-economic benefits.

9 February 2026 in Biomedical Signal Processing and Control
Verified
Journal Article
Closed access
XJXing Ji
Xing Ji
ZYZhong Yin
Zhong Yin
YBYifei Bi
Yifei Bi
KYKaiwei Yu
Kaiwei Yu
YLYize Li
Yize Li
DZDawei Zhang
Dawei Zhang