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1. Cold pressor pain assessment based on EEG power spectrum

   https://doi.org/10.1007/s42452-020-03822-8  

   Wang, Li; Xiao, Yan; Urman, Richard D.; Lin, Yingzi (December 2020, SN Applied Sciences)

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2. COMPASS App: A Patient-centered Physiological based Pain Assessment System

   https://doi.org/10.1177/21695067231192200  

   Zhu, Wenchao; Liu, Chang; Yu, Haoxiang; Guo, Yikang; Xiao, Yan; Lin, Yingzi (September 2023, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Chronic pain patients lack at-home pain assessment and management tools. The existing chronic-pain mobile applications are either solely relying on self-report pain levels or restricted to formal clinical settings. Our app, abbreviated from an NSF-funded project entitled Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), is a multi-dimensional pain app that collects physiological signals to predict objective pain levels and trace daily at-home activities by incorporating a daily check-in section. We conducted a usability test with 33 healthy participants under pain conditions. The results provided initial support for the validity of the signals in predicting internalizing pain levels among the participants. With further development and testing, we believe the COMPASS app system has the potential to be used by both patients and clinicians as an additional tool to better assess and manage pain, especially for mobile healthcare applications. 

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3. EEG-based tonic cold pain assessment using extreme learning machine

   https://doi.org/10.3233/IDA-184388  

   Yu, Mingxin; Yan, Hao; Han, Jing; Lin, Yingzi; Zhu, Lianqing; Tang, Xiaoying; Sun, Guangkai; He, Yanlin; Guo, Yikang (February 2020, Intelligent Data Analysis)

   null (Ed.)
4. A Novel Labeling Method of Physiological-based Pressure Pain Assessment Among Patients With and Without Chronic Low Back Pain

   https://doi.org/10.1177/10711813241284506  

   Zhu, Wenchao; Xiao, Yan; Lin, Yingzi (October 2024, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Pain, especially chronic pain, is a complicated and subjective experience, threatening global healthcare as one of the most severe health problems. Traditionally, pain is assessed by Visual Analog Scale to indicate the pain intensity by the patient’s self-report, causing them to become biased by various psychosocial factors. In this study, we performed two distinct labeling methods to assess the pressure pain in Quantitative Sensory Testing and to differentiate healthy controls and chronic low back pain patients: time period labels and percentage timestamp labels. Physiological signals such as blood volume pulse and galvanic skin response were collected. The time period labeling method was to segment via fixed time windows. The percentage timestamp labeling method was to select the timestamp labels based on the percentage of the threshold or the tolerance time. Both methods demonstrate different advantages when visualizing the information of different pain states and different participant groups. 

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5. Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals

   https://doi.org/10.3389/fnins.2022.831627  

   Lin, Yingzi; Xiao, Yan; Wang, Li; Guo, Yikang; Zhu, Wenchao; Dalip, Biren; Kamarthi, Sagar; Schreiber, Kristin L.; Edwards, Robert R.; Urman, Richard D. (February 2022, Frontiers in Neuroscience)

   Optimization of pain assessment and treatment is an active area of research in healthcare. The purpose of this research is to create an objective pain intensity estimation system based on multimodal sensing signals through experimental studies. Twenty eight healthy subjects were recruited at Northeastern University. Nine physiological modalities were utilized in this research, namely facial expressions (FE), electroencephalography (EEG), eye movement (EM), skin conductance (SC), and blood volume pulse (BVP), electromyography (EMG), respiration rate (RR), skin temperature (ST), blood pressure (BP). Statistical analysis and machine learning algorithms were deployed to analyze the physiological data. FE, EEG, SC, BVP, and BP proved to be able to detect different pain states from healthy subjects. Multi-modalities proved to be promising in detecting different levels of painful states. A decision-level multi-modal fusion also proved to be efficient and accurate in classifying painful states. 

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