Decoding subject-invariant emotional information from cardiac signals detected by photonic sensing system
从光子传感系统检测到的心脏信号中解码主体不变的情绪信息
光子センシングシステムで検出された心臓信号から被験者不変の感情情報をデコードする
광자 감지 시스템으로 감지된 심장 신호에서 주제 불변 감정 정보 해독하기
Decodificación de información emocional invariante al sujeto a partir de señales cardíacas detectadas por un sistema de sensores fotónicos
Décodage des informations émotionnelles invariantes par sujet à partir des signaux cardiaques détectés par un système de détection photonique
Декодирование эмоциональной информации, инвариантной к субъекту, из кардиальных сигналов, обнаруженных фотонной сенсорной системой
Yukun Long ¹ ² ³, Rui Min ³ ⁴ ⁵, Kun Xiao6, Zhuo Wang ⁶, Lanfang Liu ³ ⁴ ⁵, Yifan Sun ⁷, Xiaoli Li ³ ⁴ ⁵, Zhaohui Li ⁸ ⁹, Zeev Zalevsky ¹⁰
¹ Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
中国 珠海 北京师范大学珠海校区文理学院
² School of Systems Science, Beijing Normal University, Beijing 100875, China
中国 北京 北京师范大学系统科学学院
³ Department of Psychology, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
中国 珠海 北京师范大学珠海校区文理学院心理学系
⁴ Faculty of Psychology, Beijing Normal University, Beijing 100875, China
中国 北京 北京师范大学心理学部
⁵ Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai 519087, China
中国 珠海 北京师范大学认知神经工效研究中心，认知神经科学与学习国家重点实验
⁶ Department of Physics, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China
中国 珠海 北京师范大学珠海校区文理学院物理系
⁷ Service OPERA-Photonique, Université libre de Bruxelles, B-1050 Brussels, Belgium
⁸ Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems and School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
中国 广州 广东省光电信息处理芯片与系统重点实验室 中山大学电子与信息工程学院
⁹ Southern Laboratory of Ocean Science and Engineering, Zhuhai 519000, China
中国 珠海 南方海洋科学与工程广东省实验室(珠海)
¹⁰ Faculty of Engineering and Nano Technology Center, Bar-Ilan University, Ramat Gan 5290002, Israel
Emotion recognition systems hold significant practical value due to the vital role emotions play in daily human life. Since cardiac activities are critically involved in the process of emotional arousal, developing emotion recognition systems based on cardiac signals is of great importance. However, inter-subject variability causes a major challenge for cross-subject emotion recognition and remains a key bottleneck for the practical application of emotion recognition systems.
Here we report a photonic cross-subject emotion recognition system (PCERS) based on seismocardiography (SCG) signals, leveraging machine learning techniques driven by complex network feature engineering to decode subject-invariant emotional information from signals. In the cardiac activity monitoring component, we developed a photonic system for SCG signal detection and implemented a sample entropy-based signal processing pipeline. These designs enable precise cardiac activity monitoring as the foundation for emotion recognition.
In the emotion recognition component, the complex network features extracted from SCG signals show significant differences between different emotional states, but no significant differences across subjects. Incorporating these features into the machine learning pipeline enables efficient cross-subject emotion recognition, achieving accuracies 81.25% in leave one-out (LOO) subject-independent emotion recognition. Results in this work suggested that PCERS has potential to contribute meaningfully to practical, real-life emotion recognition applications.
