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Investigation of Brain-based Individual Differences for Classifying Syntactic Violation from Language-related ERP Component

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 8th International Conference on Next Generation Computing 2022 (2022.10)바로가기
  • 페이지
    pp.183-186
  • 저자
    Wonyoung Lee, Guiyoung Son
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A419770

원문정보

초록

영어
Event-related potential (ERP) has been widely utilized to detect cognitive behaviors. Recently, second language (L2) processing has been proven to detect brain responses similar to native (L1) from language-related ERP components such as N400/P600. In this study, we aim to detect syntactic violations from individual differences based on ERP components. To this end, we conduct experiments to discriminate detected ERP components using machine learning by proficiency levels. We recorded EEG signals from 22 participants, German L2 as Korean, while reading sentences, some of which included syntactically anomalous sentences. After data preprocessing, we evaluate the individual differences in discriminating the syntactic violation: ERP component analysis, classifying syntactic violations and comparing proficiency levels. As a result, we confirm the detection of the P600 component from advanced learners but not beginners. Performance evaluation shows that the Long-Short Term Memory (LSTM) based on a subject-independent approach has the highest accuracy of 68.85% for beginners and 69.99% for advanced learners, respectively. Additionally, we also achieve the best accuracy of 76.66% using the LSTM regarding the proficiency levels. In conclusion, we confirm that L2 learners are more similar to natives as their proficiency levels increase. Furthermore, we will closely investigate the brain responses between L1 and L2 to determine whether achieving the same brain response as a native when language comprehension is possible.

목차

Abstract
I. INTRODUCTION
II. MATERIALS
A. Institutional Review Board (IRB)
B. Subjects
C. Materials
D. Experimental procedure
E. EEG recording and preprocessing
III. METHODS
A. Classification models
B. Feature extraction
C. Experimental results
IV. EXPERIMENTAL RESULTS
A. Event-related potential analysis
B. Comparison of classification based on syntactic violation(correct vs. incorrect) with subject-independent
C. Comparison of classification based on proficiency level (Beginner vs. Advanced learners)
V. CONCLUSION
REFERENCES

키워드

Language comprehension Syntactic violations Electroencephalogram (EEG) Event-related potential(ERP) Long-Short Term Memory(LSTM)

저자

  • Wonyoung Lee [ Vision AI Business Team LG CNS Seoul, South Korea ]
  • Guiyoung Son [ Dept. Software Sejong University Seoul, South Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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