EEG based brain computer interface using multinomial regressive transformer learning for chronic neuropathic pain detection

Chronic neuropathic pain is a stressful health problem caused by a dysfunction of the nervous system severely impacts a patient’s life. Early chronic neuropathic pain detection model utilizes the deep learning and machine learning techniques. However, traditional methods faced challenges in achieving accurate and timely detection.

To improve the accuracy of chronic neuropathic pain detection, a novel Multinomial Regressive Stochastic Gradient Optimized Transformer Learning (MRSGOTL) model is developed. EEG signal pre-processing is performed to improve the signal quality using Hilbert–Huang transform. Then, the deep transformer learning is performed for chronic neuropathic pain detection.

Transformer learning uses Multilayer Perceptron (MLP) classifier model to classify the different degrees of neuropathic pain severity with less error through the Stochastic Gradient grasshopper optimization algorithm. The model achieved superior performance metrics, including 96% accuracy, 0.97 of precision, 0.98 of recall, 0.97 of F1-score, along with the time of 151 s, significantly outperforming traditional models. The finds highlights that the proposed model supports clinicians in early diagnosis and making effective treatment decisions.

The authors thank Anna University for providing the necessary support to carry out this research. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, Tamilnadu, India Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, 641032, Tamilnadu, India The authors declare no competing interests.

The study utilized a publicly available secondary dataset obtained from the Mendeley Data repository https://data.mendeley.com/datasets/yj52xrfgtz/4. As this study involved secondary analysis of anonymized data, additional ethical approval and informed consent were not required. All procedures were conducted in accordance with the Declaration of Helsinki.

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