Engagement Recognition Using a Multi-Domain Feature Extraction Method Based on Correlation-Based Common Spatial Patterns

Abstract

Engagement ability plays a fundamental role in allocating attentional resources and helps us perform daily tasks efficiently. Therefore, it is of great importance to recognize engagement level. Electroencephalography is frequently employed to recognize engagement for its objective and harmless nature. To fully exploit the information contained in EEG signals, an engagement recognition method integrating multi-domain information is proposed. The proposed method extracts frequency information by a filter bank. In order to utilize spatial information, the correlation-based common spatial patterns method is introduced and extended into three versions by replacing different correlation coefficients. In addition, the Hilbert transform helps to obtain both amplitude and phase information. Finally, features in three domains are combined and fed into a support vector machine to realize engagement recognition. The proposed method is experimentally validated on an open dataset composed of 29 subjects. In the comparison with six existing methods, it achieves the best accuracy of 87.74±5.98% in binary engagement recognition with an improvement of 4.03%, which proves its efficiency in the engagement recognition field.