Validation of Cross-Individual Pain Assessment with Individual Recognition Model from Electroencephalogram

Cross-individual pain assessment models based on electroencephalography (EEG) allow pain assessment in individuals who cannot report pain (e.g., unresponsive patients). The main obstacle to the generalisation of pain assessment models is the individual variation of brain responses to pain. Hence, we took the individual variation into account in cross-individual model development. We developed two convolutional neural networks (CNN) sharing an encoder architecture. One CNN predicts pain, while the other predicts the identity of an individual. We performed a leave-one-out (LOO) test with the exclusion of each subject and applied evidence accumulation to it for validating the pain prediction model’s performance, where the binary classifier involves the states of pain (Hot) and resting state (Eyes-open). The mean accuracy produced by the LOO tests was 57.81% (max: 73.33%), and the mean accuracy of evidence accumulation achieved 69.75% (max: 100.00%). The individual recognition model achieved an accuracy of 99.63%. Nevertheless, when we acquired the most similar subject to a novel subject using the individual recognition model, where the most similar subject was used to train a subject-wise pain prediction model. The accuracy of predicting the pain-related conditions of the novel subject by the subject-wise model was only 53.73% (max: 79.50%). Therefore, the approach to utilising the features related to individual variation extracted by the CNN model needs more investigation for improving cross-individual pain assessment.Clinical relevance— This model can be applied to assess pain from EEG signals at the bedside with future improvement, which can help caretakers of unresponsive patients.