ACS Nano
·
2025
·
IF 16
Jing-Hui Mao,Zhong-Hui Shen,Jian Wang,Run-Lin Liu,Xiao-Fei Liu,Ying Lan,Mengjun Zhou,Jian-Yong Jiang,Yang Shen,Ce-Wen Nan
Emotion monitoring plays a crucial role in mental health management. However, traditional methods of emotion recognition predominantly rely on subjective questionnaires or facial expression analyses, which are often inadequate for continuous and highly accurate monitoring. In this study, we propose a high-precision, fine-grained emotion recognition system based on multisource throat physiological signals. The system collects signals through optimized flexible multiporous skin sensors and analyzes them using machine learning models capable of efficiently capturing complex feature interactions. First, we adopt a two-step cross-linking strategy to modulate the porous structure of the sensitive layer to enable accurate detection of the diverse and weak physiological signals in the throat. By extracting four-dimensional features from the input of 7025 samples, the platform based on the Light Gradient Boosting Machine (LightGBM) efficiently captures their nonlinear interactions, ultimately achieving precise classification of five emotional states (relaxation, surprise, disgust, fear, and neutral) with an accuracy of 98.9%. Further validation on an independent data set reveals an average emotion recognition accuracy of 99.3%, demonstrating the system’s robustness and reliability in real-world applications. This work provides a viable technological solution for real-time and continuous emotion monitoring, offering significant potential in mental health management and related fields.
研究领域:
生物医学工程
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人工智能
·
心理健康
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产品关联
DOI
PubMed
高影响因子
ADVANCED FUNCTIONAL MATERIALS
·
2024
·
IF 18.5
Mengdi Hu,Jing Ren,Yue Pan,Liping Cheng,Xin Xu,Chee Leong Tan,Huabin Sun,Yi Shi,Shancheng Yan
The advancement in brain–computer interface technology has facilitated real-time communication between the brain and external devices, rapidly expanding invasive brain–computer interfaces. Nevertheless, little progress has been made in improving the one-way, noninvasive brain–computer interface technology using conductive methods, necessitating enhancements to the current paste contact electrodes. In this paper, a captivating spherical electrode is fabricated that exhibits noncytotoxic properties, combining alginate hydrogel with indium tin oxide conductor components. The electrode's remarkable and consistent electrical conductivity enables it to generate stable adaptive deformations that conform to diverse scalp topographies. Most impedance values fall below 10 kΩ, indicating heightened hydrophilicity compared to traditional conductive pastes, as evidenced by contact angle measurements. An empirical electroencephalography data collection study confirms its conductivity and signal transmission reliability. The acquired electroencephalography signals are utilized for emotion recognition utilizing band energy ratio analysis and support vector machine (SVM) algorithms. The operation of this system is both straightforward and convenient, requiring minimal preparation before and after the collection of electroencephalography signals. Utilizing an extensive range of raw materials and straightforward preparatory procedures enhances the potential industrial application of hydrogel electrodes, thereby significantly contributing to advancing civilian electroencephalography equipment and investigating depression-related pathologies.
研究领域:
神经科学
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生物医学工程
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材料科学
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产品关联
DOI
高影响因子
Journal of Materials Chemistry C
·
2023
·Vol. 11
·期 10
·3468-3479
·
IF 6.4
Yixin Cao,Chun Zhao,Tianshi Zhao,Yi Sun,Zhengjun Liu,Xianyao Li,Li Yin,Jiangmin Gu,Hao Ren,Xianwei Geng,Jian Yao,Lixing Kang
In the new generation of brain-like optoelectronic visual signal processing and artificial perception systems, floating-gate artificial synaptic devices based on two-dimensional materials represent a feasible route. However, the traditional optoelectronic floating-gate artificial synapse is hindered from further development due to complicated processes, such as the need to fabricate additional tunneling layers and photoresponsive layers. Here, an optoelectronic artificial synapse device based on an MXene floating-gate is fabricated. The self-grown TiO2 layer on the ‘111’ surface of MXene is a natural tunneling layer, enhancing the optical response intensity and memory time of postsynaptic currents. The device responds to photoelectric pulses and simulates basic synaptic properties, including excitatory postsynaptic current, paired-pulse facilitation, and short-term/long-term plasticity. In addition, the neural network based on the properties of the device was used to simulate the process of recognizing and classifying objects in the brain after receiving visual signals. Most importantly, as a proof of concept, simulation analyzes the brain wave signals when the brain receives different external stimuli and identifies the subject's mood by classifying the signals. These results indicate that the optoelectronic artificial synapse based on an MXene floating-gate is promising in the application of next-generation brain-like artificial synapse neuromorphic devices.
研究领域:
神经形态计算
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材料科学
·
脑科学
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DOI
高影响因子
FOOD QUALITY AND PREFERENCE
·
2020
·
IF 4.842
Ruicong Zhi,Chenyang Wang,Xin Hu,Caixia Zhou,Mengyi Liu,Jingru Zhao,Yiping Zuo
The emotional reactions that certain products evoke (like or dislike) play a key role in the user acceptance or preference toward these products and influence the choice behaviors. Facial expressions constitute a primary channel to express and communicate emotions. In particular, the facial reactions evoked by certain stimuli are generated subconsciously, and they can be easily captured by video recording. However, it is challenging to quantify and objectively describe facial expressions. Until now, most implicit measurements based on facial expression analyses have been implemented considering “basic emotions”, which cannot directly represent like/dislike. How can we identify the affective state of consumers directly by using facial expression analysis technology? In this study, hedonic rating categories are defined based on the hedonic scale, and automatic facial expression analysis technology is applied to evaluate the suitability of the defined categories. Moreover, a visual multimodal facial database (RGB, depth, and infrared images) for the hedonic recognition of Asian individuals is constructed. A hybrid deep learning framework is employed to exploit the spatiotemporal information of the collected data to realize hedonic recognition. Early and late fusion strategies are used to investigate the suitability of the individual modalities and their complementarity. The experimental results indicate that the late fusion of the three modalities yielded the highest recognition accuracy compared to those obtained using individual modalities and the early fusion scheme. These results demonstrate the effectiveness of fusing different visual facial data and indicate that the use of multimodality-based facial expression analyses could enhance the accuracy of hedonic recognition.
研究领域:
计算机科学
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人机交互
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情感计算
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DOI
高影响因子
ADVANCED MATERIALS
·
2024
·Vol. 36
·期 31
·2403411
·
IF 27.4
Xiaojia Du,Hai Wang,Yunfei Wang,Zhiqiang Cao,Leyi Yang,Xiaohu Shi,Xiaoxu Zhang,Chengzhi He,Xiaodan Gu,Nan Liu
Understanding psychology is an important task in modern society which helps predict human behavior and provide feedback accordingly. Monitoring of weak psychological and emotional changes requires bioelectronic devices to be stretchable and compliant for unobtrusive and high-fidelity signal acquisition. Thin conductive polymer film is regarded as an ideal interface; however, it is very challenging to simultaneously balance mechanical robustness and opto-electrical property. Here, a 40 nm-thick film based on photolithographic double-network conductive polymer mediated by graphene layer is reported, which concurrently enables stretchability, conductivity, and conformability. Photolithographic polymer and graphene endow the film photopatternability, enhance stress dissipation capability, as well as improve opto-electrical conductivity (4458 S cm −1 @>90% transparency) through molecular rearrangement by π–π interaction, electrostatic interaction, and hydrogen bonding. The film is further applied onto corrugated facial skin, the subtle electromyogram is monitored, and machine learning algorithm is performed to understand complex emotions, indicating the outstanding ability for stretchable and compliant bioelectronics.
研究领域:
材料科学
·
生物医学工程
·
心理学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）