EEG 신호 간 유사도 분석을 위한 DTW-N 기법 적용 연구

• Published in: Journal of biomedical engineering research Vol. 46; no. 2; pp. 208 - 214
• Main Authors: 이가흔, 임철기, 백종화, 전성찬, 이성한, 서현, GaHeun Lee, Cheolki Im, Jonghwa Paik, Sung Chan Jun, Sunghan Lee, Hyeon seo
• Format: Journal Article
• Language: Korean
• Published: 대한의용생체공학회 01.04.2025
• Subjects: 의공학; Dynamic time warping-normalization; Similarity assessment; User identification; EEG; Euclidean distance
• ISSN: 1229-0807; 2288-9396

Although Euclidean Distance (ED) has limitations in fully capturing the inherent similarity between signals, it has demonstrated higher accuracy in personal identification than Dynamic Time Warping (DTW) when applied in Electroencephalogram (EEG) signal-based authentication systems. In this study, we aim to compare the performance of ED, DTW, and DTW-Normalization (DTW-N) algorithms in assessing EEG signal similarity. Furthermore, this study evaluates the effects of normalization on similarity measurement across different channels, participants, and signal counts. EEG data were collected from ten participants during speech tasks with auditory stimuli, and all 32 EEG channels were analyzed. The α is an indicator used to quantitatively evaluate the difference between signals from the same subject and different subjects; a higher value indicates a greater difference in signal similarity. DTW-N achieved the highest α values compared to ED and DTW. Across all channels, DTW-N showed the highest α values, with the FC1 channel having the highest average αDTW-N value of 3.4110 × 10-2. Additionally, α for participants 3 and 9 reached 4.7225 × 10-5, approximately 55.79% higher than the DTW-N mean, while α for participants 7 and 8 was the lowest at 4.722× 10-5. As the number of signals increased, the α values decreased. The DTW-N algorithm effectively addressed temporal distortion and amplitude variations in EEG signals, making it highly effective for distinguishing individuals based on EEG patterns. Future research will explore optimal representative metrics for EEG data and enhance individual identification performance using DTW-N-based classification models.