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  1. 1. EEG nanosensor system for objective analysis of nociception Nanotechnologyand Nanosensors Course Reshma Bhatnagar IN ASSOCIATION WITH Kevin Cando, Virginia Nykänen and Aniruddh Sharma
  2. 2. 2 Table of Contents ABSTRACT: 2 1. INTRODUCTION: 2 2. LITERATURE REVIEW: 3 3. PROJECT DESCRIPTION: 4 4. CONCLUSIONS AND RECOMMENDATIONS: 6 5. REFERENCES: 7 Abstract: Nociception is an important sensory mechanism enabling organisms to detect the presence of abnormalities, noxious substances and other harmful stimuli. It aids in the identification of potential problems and diagnoses of ailments. However, objectively measuring pain, and indeed describing it, especially by “lay” people is often imprecise and difficult, often leading to misdiagnosis. This paper aims at creating an nano-sensor system based on electroencephalography (EEG) and wireless transmission technology to transmit raw neural data to be classified and interpreted by suitable algorithms in order to facilitate the identification of source, underlying disease and potential treatment for the pain. Moreover, it is hoped that the system may be utilized to give expression to the voiceless, enabling these individuals to communicate their discomfort. 1. Introduction: The aim of this paper was to design a nanoparticle system to sense pain, identify its source and detect underlying pathophysiology. The need for such a system is to allow the objectification and quantification of pain to enhance the accuracy of diagnosis and enable expression of discomfort from the voiceless, including infants and the paralysed. The proposed system consists of nano-electrodes, linked to wireless transmitters to transmit electrical signals from the somatosentory cortices to a remote computer, where pain data can be analysed and interpreted.
  3. 3. 3 2. Literature Review: While the sensation of pain is almost universally an unpleasant experience, its importance to the organism is obvious in that it acts as an alarm system to notify the body that something is wrong. The sensation of pain is facilitated by distinct nociceptive pathways, which transmit signals from noxious stimuli via specialized nerve fibres. Pain pathways can be divided into two categories: sensory and discriminatory pathways, which detect noxious stimuli, and the affective and motivational pathways, that trigger unpleasant sensations and the consequent responses. As the aim of this paper is to objectively identify and classify pain, only the sensory and discriminatory pathway was considered, whose terminating nuclei at the highest level are the primary and secondary somatosensory cortices (Purves et al, 2012). The subjective sensation and expression of individual pain poses several challenges in correct diagnosis and treatment identification. Thus, efforts have been made to objectively analyse pain using EEG recordings. The targets for recording pain sensation are generally the somatosensory cortices (S1 and S2 regions of the cortex), whose electrical activity may be recorded using EEG electrodes on the scalp (Dowman et al, 2008; Sarnthein et al, 2006; Mahmood et al, 2012). Interpretation of raw EEG signals, however, is crucial due to the low spatial resolution of EEG signals for which computational/statistical processing is essential. There is much work on creating signal processing and classification methods to facilitate the same (Blankertz et al, 2004). There has been extensive research on the utilization of nano-electrodes for reading EEG data due to its excellent temporal resolution, relative inexpensiveness, non-invasiveness, and long-life recording potential. EEG nanotechnology has found application in monitoring traumatic injuries in soldiers on the battlefield (Watkin et al, 2009), concussions in football players (Ramasamy et al, 2015) and to monitor the alerntness of drivers (Ramasamy et al, 2014). Materials used as EEG nano-sensors include conductive carbon
  4. 4. 4 nanotubes, which have been assessed for safety by human trials and have provided results comparable to conventional state of the art EEG electrodes (Ruffini et al, 2007). Carbon nanotubes are versatile nanomaterials that act as excellent electrodes. Indeed, they have been recently utilized to create a biocompatible, waterproof, self-adhesive, epidermal film, which was used as an electrocardiography (ECG) sensor system allowing long-term signal recording. ECG recordings were transmitted wirelessly using integrated Bluetooth transmitters (Lee et al, 2014). 3. Project Description: The project consists of three components: 1. EEG electrodes, consisting of a carbon nanotube sensor array on a polydimethylsiloxane (PDMS) layer. 2. Bluetooth wireless transmitter 3. Remote analysis computer system A representation of the system is illustrated in Figure 3.1 Figure 3.1: EEG information flow and interpretation Nano electrode • Reads raw electric signals of the brain from the scalp Bluetooth module • Transmission of the signal to a remote computer Computer • Signal processing to identify and then analyse pain data
  5. 5. 5 The ECG electrode and Bluetooth system is based on the method described by Lee et al. (2014). The technique used by them for ECG recording would be adapted for EEG measurement. Briefly, a silicon wafer deposited with a gold/titanium (Au/Ti) system, to enable easy detachment of the carbon nanotubule (CNT) array, would be prepared. Onto the wafer, PDMS would be spin coated and oven cured. A hydrophilic surface would be created using treatment by oxygen. A polyimide (PI) layer would be spin-coated and defined using UV irradiation, over which an Au/Ti layer would be deposited using electron beam evaporation. Another PI insulating layer followed by nickel (Ni) plating for the terminals would be undertaken. After these steps of preparation, a layer for the epidermal casting would then be placed on the electrode on to which uncured CNT/PDMS would be poured. The casting layer would be removed, and the CNT/PDMS cured in an oven, into which 3 –Aminoproplydimethylethoxysilane (aPDMS) would be injected once an OHP film is used to cover the adhesive side of the electrode. After curing, the electrode can be cut and attached to the skin surface (scalp). This would, however, necessitate the shaving of the hair over the portion of interest. The raw data of the EEG electrode would be transmitted wirelessly using a Bluetooth module. On acquisition of data, the signal would be processed for noise reduction, and pattern recognition. The raw signal would also be utilized for the characterization of the nanosensor, with pin-prick pain responses in volunteers (after receiving ethical approval), being plotted against the voltage change detected by the electrode. The algorithms used for the same would include independent component analysis and linear discriminant analysis to help identify the source of pain and potentially help in diagnosis. Linear discriminant analysis of EEG has shown good accuracy when evaluated on the basis of error rates (Blankertz et al, 2003). Independent component analysis also seems a good approach to analyse EEG data in that it reduces the statistical dependence of
  6. 6. 6 signals, making them as independent as possible (Lee et al, 1999), an important consideration as only the cortical outputs are being measured. 4. Conclusions and Recommendations: An EEG based nano-sensor for objective analysis of pain can potentially improve diagnoses and enhance expression of pain. The components for the physical implementation of this system are well defined and characterized; there is exhaustive literature on the utilization of carbon nanotubes as electrodes and the material properties of the same have been characterized for toxicity, electronic and mechanical properties (Lee et al, 2014). There is also extensive literature on the utilization of EEG devices to process signals including pain, though not all of them use nano-electrodes (Dowman et al, 2008). Several algorithms on EEG processing gave also been proposed (Blankertz et al, 2003). Thus, the feasibility of this project is quite high. The main challenge in implementing this system is, however, the signal processing aspect. EEG is a technique with low spatial resolution and high noise (Nunez and Pilgreen, 1991). Hence, identification of the source, indeed of nociception itself requires extensive processing. Extensive validation, however, would be required, which may necessitate super computing facilities. With cheap cloud computing facilities, however, this should not be an impossible or expensive task. Future implementation of the system could then be based on pattern creation and recognition by a remote super computing cloud giving real time output to a mobile phone or personal computer. The processing of intimate data by a remote computer however leads to ethical considerations that would need to be thoroughly evaluated before a system of this kind can be implemented.
  7. 7. 7 5. References: Blankertz, B.; Muller, K.; Curio, G.; Vaughan, T.M.; Schalk, G.; Wolpaw, J.R.; Schlogl, A.; Neuper, C.; Pfurtscheller, G.; Hinterberger, T.; Schroder, M.; Birbaumer, N., "The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials," Biomedical Engineering, IEEE Transactions on , vol.51, no.6, pp.1044,1051, June 2004 doi: 10.1109/TBME.2004.826692 Dowman, R., Rissacher, D., & Schuckers, S. (2008). EEG INDICES OF TONIC PAIN- RELATED ACTIVITY IN THE SOMATOSENSORY CORTICES. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, 119(5), 1201–1212. doi:10.1016/j.clinph.2008.01.019 Lee, S. M., Byeon, H. J., Lee, J. H., Baek, D. H., Lee, K. H., Hong, J. S., & Lee, S. H. (2014). Self-adhesive epidermal carbon nanotube electronics for tether-free long-term continuous recording of biosignals. Scientific reports, 4. Lee, T. W., Girolami, M., & Sejnowski, T. J. (1999). Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural computation, 11(2), 417-441. Mahmood, N., Mahmood, A., Iqbal A., Hafeez, Z. (2012) Methodology for EEG Based System Development to Detect Objective Pain in Human Body. International Journal of Scientific & Engineering Research, 3(11). November- 2012 Nunez, P. L., & Pilgreen, K. L. (1991). The spline-Laplacian in clinical neurophysiology: a method to improve EEG spatial resolution. Journal of Clinical Neurophysiology, 8(4), 397-413. Purves D, Augustine GJ, Fitzpatrick D, et al., editors. Neuroscience. 5th edition. Sunderland (MA): Sinauer Associates; 2012.
  8. 8. 8 Ramasamy, M., Oh, S., Harbaugh, R., & Varadan, V. K. (2014). Real Time Monitoring of Driver Drowsiness and Alertness by Textile Based Nanosensors and Wireless Communication Platform. Ramasamy, Mouli, Robert E. Harbaugh, and Vijay K. Varadan. "Wireless nanosensors for monitoring concussion of football players." SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring. International Society for Optics and Photonics, 2015. Ruffini, G., Dunne, S., Fuentemilla, L., Grau, C., Farres, E., Marco-Pallarés, J., Silva, S. R. P. (2008). First human trials of a dry electrophysiology sensor using a carbon nanotube array interface. Sensors and Actuators A: Physical,144(2), 275- 279. Sarnthein J, Stern J, Aufenberg C, Rousson V, Jeanmonod D. Increased EEG power and slowed dominant frequency in patients with neurogenic pain. Brain. 2006 Jan;129(Pt 1):55-64. Epub 2005 Sep 23. PubMed PMID: 16183660. Watkin, K. L., Iyer, R., Karbalczyk, Z., Sanders, W., & Patel, J. (2009). Helmet Integrated Nanosensors, Signal Processing and Wireless Real Time Data Communication for Monitoring Blast Exposure to Battlefield Personnel. ILLINOIS UNIV AT URBANA SAVOY.