Title Single-trial laser-evoked potentials feature extraction for prediction of pain perception
نویسندگان
چکیده
Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.
منابع مشابه
A novel approach to predict subjective pain perception from single-trial laser-evoked potentials
Pain is a subjective first-person experience, and self-report is the gold standard for pain assessment in clinical practice. However, self-report of pain is not available in some vulnerable populations (e.g., patients with disorders of consciousness), which leads to an inadequate or suboptimal treatment of pain. Therefore, the availability of a physiology-based and objective assessment of pain ...
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