Arousal Recognition Using EEG Signals
As an indicator of emotion intensity, arousal plays an important role in emotion recognition. However, the accuracy rate-based EEG signals have been far away from human’s satisfactory due to the lack of effective methods. In this paper, we propose a novel framework for recognizing arousal levels by using EEG signals. Instead of using time domain feature and frequency domain feature of EEG, we select the EEG feature directly from a large number of EEG signals by using the feature selection method after data standardization. Based on our method, feature with most distinguished ability has been found. The experimental results on the open data set DEAP show that the arousal accuracy has been significantly improved by using our method.
KeywordsArousal recognition Affective computing EEG feature Feature selection
This paper is supported by Beijing Nova Program (Z181100006218041) and National Key R&D Program of China (2017YFC0820106).
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