Affiliations: [a] Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing 100015, China | [b] Emergency and Critical Care Center, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China | [c] School of Life Sciences, Beijing Institute of Technology, Beijing 100086, China | [d] Intelligent Human-Machine Systems Lab, College of Engineering, Northeastern University, Boston, MA 02115, USA
Correspondence:
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Corresponding authors: Yingzi Lin, Intelligent Human-Machine Systems Lab, College of Engineering, Northeastern University, 360 Huntington Ave., Boston, MA 02148, USA. Tel.: +1 6173738610; Fax: +1 6173732921; E-mail: [email protected]. Lianqing Zhu, Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, 6 Hongxia Road, Chaoyang District, Beijing 100015, China. Tel.: +86 13901168502; Fax: +86 10 68918820; E-mail: [email protected].
Abstract: The purpose of this study is to present a novel method which can objectively identify the subjective perception of tonic pain. To achieve this goal, scalp EEG data are recorded from 16 subjects under the cold stimuli condition. The proposed method is capable of classifying four classes of tonic pain states, which include No pain, Minor Pain, Moderate Pain, and Severe Pain. Due to multi-class problem of our research an extended Common Spatial Pattern (ECSP) method is first proposed for accurately extracting features of tonic pain from captured EEG data. Then, a single-hidden-layer feedforward network is used as a classifier for pain identification. With the aid of extreme learning machine (ELM) algorithm, the classifier is trained here. The advantages of ELM-based classifier can obtain an optimal and generalized solution for multi-class tonic cold pain. Experimental results demonstrate that the proposed method discriminates the tonic pain successfully. Additionally, to show the superiority for the ELM-based classifier, compared results with the well-known support vector machine (SVM) method show the ELM-based classifier outperform than the SVM-based classifier. These findings may pay the way for providing a direct and objective measure of the subjective perception of tonic pain.
