Dynamic time warping: A single dry electrode EEG study in a self-paced learning task
ACII(2015)
摘要
This study investigates dynamic time warping (DTW) as a possible analysis method for EEG-based affective computing in a self-paced learning task in which inter- and intra-personal differences are large. In one experiment, participants (N=200) carried out an implicit category learning task where their frontal EEG signals were collected throughout the experiment. Using DTW, we measured the dissimilarity distances of EEG signals between participants and examined the extent to which a k-Nearest Neighbors algorithm could predict self-rated feelings of a participant from signals taken from other participants (between-participants prediction). Results showed that DTW provides potentially useful characteristics for EEG data analysis in a heterogeneous setting. In particular, theory-based segmentation of time-series data were particularly useful for DTW analysis while smoothing and standardization were detrimental when applied in a self-paced learning task.
更多查看译文
关键词
DTW, Self-paced learning, Neurosky MindWave
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络