EEG 신호 기반의 특징벡터 형성 및 감정인식을 위한 기계학습
The study of feature vector generation and emotion recognition using EEG signals
0
조회
0
다운로드
5
인용
0
참고문헌
키워드
초록
최근 인공지능 기술의 발달과 더불어 감정인식은 중요한 연구 분야로 대두되고 있다. 기존의 음성, 얼굴을 기반으로 한 감정연구에서 최근에는 보다 객관적인 뇌파 등과 같은 생체신호를 활용하는 연구로 확대되고 있다. 본 연구에서는 뇌파 데이터를 활용하여 긍/부정에 대한 뇌파에 특징요소를 추출하고, 대표적인 기계학습 분류기인 SVM(Support Vector Machine) 분류기를 활용하여 감정분류를 진행하였다. 뇌파 신호에 대한 전처리를 통하여 몸과 눈의 움직임(eye-blick)과 같은 방해파를 제거하였다. 특징요소 추출을 위하여 전처리된 뇌파 데이터를 10ms당 데이터의 평균값을 생성하고, 이를 50%로 중첩하는 방법으로 1ms 단위의 이미지를 형성하였다. 추출된 특징요소는 대표적인 기계학습 분류기인 SVM을 사용하여 감정분류를 진행하였다. 그 결과, 3가지 감정에 대하여 평균 94.6%의 결과를 도출하였다. 본 연구의 결과는 감정분류에 있어서 시계열 데이터를 이미지화하는 방법으로 특징을 추출하였다. 향후 시계열 데이터 처리에 있어서 새로운 특징요소 추출방법으로 활용 가능하며, 또한, 감정을 세분화하여 다양한 기계학습 알고리즘에 적용할 수 있는 새로운 접근법을 제안한 것에 의의를 둔다.
Abstract
According to the development of artificial intelligence, emotion recognition has emerged as an important element from psychology to engineering. In the previous research, there are various of materials such as voice, motion, and behaviors. However, these responses have a weakness to identify real emotional states because of making the faking voice or hiding facial expression unlike their emotion as intended by oneself. Some studies related to emotion recognition have extended to study emotion recognition using physiological signals such as electroencephalogram (EEG), electrocardiogram (ECG), skin conductance (SKT) and respiration. In this study, we performed the emotion recognition using SVM (Support Vector Machine) with public open database. The EEG signals conducted the preprocessing for removal to artifacts such as muscle and eye blicks. In the feature extraction, the window size is 10 seconds and 50% overlap and the output image data is produced per 1ms. As a result, we achieved the classification average accuracy of 94.6% among three emotion, positive, negative and calm. In conclusion, this proposed method is a novel approach for deal with time-series data such as voice and physiological signals. Also, we proposed that feature extraction relates to emotion recognition for machine learning.
참고문헌 (26)
- Yoon, Shin-ae, "Fear emotion classification in speech by acoustic and behavioral cues", Multimedia Tools and Applications, 78(2), pp.2345~2366 (2019)
- Tursunov, Anvarjon, "Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features", Applied Sciences, 9(12), pp.2470~ (2019)
- Reddy, Chirra Venkata Rami, "Facial Emotion Recognition Using NLPCA and SVM", Traitement du Signal, 36(1), pp.13~22 (2019)
- Sun, Jian-Ming, "Facial emotion recognition in modern distant education system using SVM", 2008International Conference on Machine Learning and Cybernetics, 6 (2008), IEEE
- Jerritta, S., "Physiological signals based human emotion recognition: a review", 2011 IEEE 7th International Colloquium on Signal Processing and its Applications. IEEE (2011)
- Hooker, C. I., "Mentalizing about emotion and its relationship to empathy", Social cognitive and affective neuroscience, 3(3), pp.204~217 (2008)
- Takahashi, Kazuhiko, "Remarks on SVM-based emotion recognition from multi-modal biopotential signals", RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759). IEEE (2004)
- Nie, D., "EEG-based emotion recognition during watching movies", 2011 5th International IEEE, 2011, ~
- 이충헌, "뇌파기반 집중도 전송 및 BCI 적용에 관한 연구", 전자공학회논문지 - SC, 46(2), pp.41~46 (2009), 대한전자공학회
- Ekman, P., "Universals and cultural differences in the judgments of facial expressions of emotion", Journal of personality and social psychology, 53(4), pp.712~ (1987)
- Russell, James A., "A circumplex model of affect", Journal of personality and social psychology, 39(6), pp.1161~ (1980)
- 이현주, "뇌파를 활용한 사용자의 감정 분류 알고리즘", 한국통신학회논문지C, 39(2), pp.122~129 (2014), 한국통신학회
- Delorme, Arnaud, "EEGLAB:an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis", Journal of neuroscience methods, 134(1), pp.9~21 (2004)
- Zhang, J., "ReliefF-based EEG sensor selection methods for emotion recognition", Sensors, 16(10), pp.1558~ (2016)
- Li, Mu, "Emotion classification based on gamma-band EEG", 2009Annual International Conference of the IEEE Engineering in medicine and biology society. IEEE (2009)
- Ali, M., "A novel EEG-based emotion recognition approach for e-healthcare applications", Proceedings of the 31st Annual ACM Symposium on Applied Computing (2016)
- A. Viet Hoang, "A real-time model based support vector machine for emotion recognition through EEG", 2012 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE (2012)
- Savran, A., "Emotion detection in the loop from brain signals and facial images", Proceedings of the eNTERFACE 2006 Workshop (2006)
- Lang, Peter J., "International affective picture system (IAPS): Instruction manual and affective ratings", The center for research in psychophysiology, University of Florida (1999)
- Yik, Michelle, "A 12-point circumplex structure of core affect", Emotion, 11(4), pp.705~ (2011)
- Wang, Xiao-Wei, "Emotional state classification from EEG data using machine learning approach", Neurocomputing, 129, pp.94~106 (2014)
- Suykens, Johan AK, "Least squares support vector machine classifiers", Neural processing letters, 9(3), pp.293~300 (1999)
- Hu, X., "EEG correlates of ten positive emotions", Frontiers in human neuroscience, 11, pp.26~ (2017)
- 류제우, "EEG 기반 감정인식을 위한 주석 레이블링과 EEG Topography 레이블링 기법의 비교 고찰", 한국차세대컴퓨팅학회 논문지, 15(3), pp.16~24 (2019), 한국차세대컴퓨팅학회[DOI]
- 권은, "Empirical Assessment of Indirect Human-Robot Interaction", 한국차세대컴퓨팅학회 논문지, 7(2), pp.50~65 (2011), 한국차세대컴퓨팅학회
- 이호준, "원격지 다중 사용자와 감정 교환을 위한 MPEG-V 기반의 원격 존재 플랫폼", 한국차세대컴퓨팅학회 논문지, 12(5), pp.87~93 (2016), 한국차세대컴퓨팅학회