Abstract
Rationale In recent times, we have witnessed a push towards restoring sensory perception to upper-limb amputees, which includes the whole spectrum from gentle touch to noxious stimuli. These are essential components for body protection as well as for restoring the sense of embodiment. Despite the considerable advances that have been made in designing suitable sensors and restoring tactile perceptions, pain perception dynamics and how to decode them using effective bio-markers are still not fully understood. Methods Here, we used electroencephalography (EEG) recordings to identify and validate a spatio-temporal signature of brain activity during innocuous, moderately more intense, and noxious stimulation of an amputee's phantom limb using transcutaneous nerve stimulation (TENS). Results Based on the spatio-temporal EEG features, we developed a system for detecting pain perception and reaction in the brain, which successfully classified three different stimulation conditions with a test accuracy of 94.66%, and we investigated the cortical activity in response to sensory stimuli in these conditions. Our findings suggest that the noxious stimulation activates the pre-motor cortex with the highest activation shown in the central cortex (Cz electrode) between 450 and 750 ms post-stimulation, whereas the highest activation for the moderately intense stimulation was found in the parietal lobe (P2, P4, and P6 electrodes). Further, we localized the cortical sources and observed early strong activation of the anterior cingulate cortex (ACC) corresponding to the noxious stimulus condition. Moreover, activation of the posterior cingulate cortex (PCC) was observed during the noxious sensation. Conclusion Overall, although this is a single case study, this work presents a novel approach and a first attempt to analyze and classify neural activity when restoring sensory perception to amputees, which could chart a route ahead for designing a real-time pain reaction system in upper-limb prostheses.
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Acknowledgements
The authors would like to thank Stefan Ehrlich, Dr. Emmanuel Dean, Nicolas Berberich, and Constantin Uhde for the fruitful discussion. We would also like to thank the Statistical Consulting Service at the Technical University of Munich (TUM) for consultation on our statistical analysis and results. This work was supported in part by Ph.D. grant of the German Academic Exchange Service (DAAD).
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Tayeb, Z., Bose, R., Dragomir, A., Osborn, L.E., Thakor, N.V., Cheng, G. (2021). EEG Decoding of Pain Perception for a Real-Time Reflex System in Prostheses. In: Guger, C., Allison, B.Z., Gunduz, A. (eds) Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-79287-9_5
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