EEG nanosensor system for objective
analysis of nociception
Nanotechnologyand Nanosensors Course
IN ASSOCIATION WITH
Kevin Cando, Virginia Nykänen and Aniruddh Sharma
Table of Contents
1. INTRODUCTION: 2
2. LITERATURE REVIEW: 3
3. PROJECT DESCRIPTION: 4
4. CONCLUSIONS AND RECOMMENDATIONS: 6
5. REFERENCES: 7
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.
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
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
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
• Reads raw
signals of the
of the signal
to a remote
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
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.
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