A LOW COST ERGONOMIC EEG SENSOR

FOR PREDICTING MENTAL ILLNESS

Dennis Majoe

, Jurg Gutknecht

and Hong Peng

Native Systems Group, ETH Zurich, Clausiusstrasse, Zurich, Switzerland

School of Information Science & Engineering, Lan Zhou University, Lan Zhou, China

Keywords: Mental Health, Depression, EEG Sensing, Wearable Smart Sensor.

Abstract: The EEG recording of a person has been considered as one potential component within an overall wearable

sensor system that predicts the onset of mental health problems. Such a smart EEG sensor should provide

detailed sensory information, be easy to use, and to put on and take off and whilst being very ergonomic the

design should aim at a very low final end user cost to ensure the widest possible take up by the e-Health

community. The work reported here describes the design of such a sensor, the performance and its use

during extensive clinical trials aimed to establish the rules that link physiology sensing to mental health

prediction.

1 INTRODUCTION

This work relates to a 5 electrode Electro

Encephalogram (EEG) sensor developed within the

EU research project OPTIMI. The project’s aim is to

provide on-line predictive tools for the early

identification and intervention during the onset of a

mental illness, in particular depression, following

the inadequate coping with day to day stress.

The EEG sensor is part of a set of wearable

sensors to be worn by a large number of volunteers

(130) during two phases of trials. The EEG sensor

will be used to measure Alpha band brain activity

and EEG signal C0 complexity. These

measurements will be calibrated and used in a data

fusion process to predict the onset of depression.

During the trials the EEG sensors must be used

every day for a period of around five minutes. In

order to ensure that volunteers comply with the daily

testing procedures, that will take place every day for

4 weeks, this daily activity must be as quick and

simple to perform as possible. At the same time high

EEG recording accuracy is necessary. If there are

difficulties or discomfort when using the sensor,

volunteers may drop out of the trials and the longer

term viability of the commercialisation of the sensor

will be left in doubt. If the sensor is not placed well

on the head the quality of the data may be

compromised. Therefore ergonomic design must be

considered at each stage of the development process.

Since a very large number of EEG sensors, as

high as 55 units, are required to meet the needs of

the trials, a low cost sensor is needed to ensure the

project keeps within its limited budget. Likewise, in

order to allow such technology to become

mainstream and used by large numbers of citizens,

low unit cost must be a central design criteria.

The sensor must provide equal data quality and

resilience to noise as the commercial off the shelf

EEG sensors that the OPTIMI psychologists currently

use in the laboratory and medical clinics and the

sensor data should be easily integrated into the

OPTIMI data processing systems that include

wearable Electrocardiograph and Actigraphy sensors.

This paper describes the need, usage, design,

development and testing of the EEG sensor covering

the electronics, software, ergonomics, economics and

accuracy.

Section 2 describes the OPTIMI architecture and

the EEG usage. Section 3 describes the development

of the sensor hardware and section 4 discusses the

results obtained during laboratory and the first phase

of OPTIMI trials.

2 SENSOR ARCHITECTURE

The OPTIMI project incorporates a number of smart

wearable sensors. The following summarizes the

sensors and their target function:

164

Majoe D., Gutknecht J. and Peng H..

A LOW COST ERGONOMIC EEG SENSOR FOR PREDICTING MENTAL ILLNESS.

DOI: 10.5220/0003732901640173

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2012), pages 164-173

ISBN: 978-989-8425-88-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

• EEG for stress and depression

• Activity Sensor for ambulatory activity

• ECG for Heart Rate and HR Variability

• Sub Dermal Cortisol Level Sampler

• Speech Analysis for depression score

The ECG and Activity sensors are worn 24/7

while the EEG sensor is to be worn once a day in the

evening for a few minutes in order to perform an

EEG recording and then removed. Data from all the

sensors is received wirelessly from each sensor by a

netbook computer called the HomePC. The HomePC

encrypts the data and transmits it to a central server

which processes the results.

Trials are to be conducted in Switzerland, Spain

and China over 4 weeks in order to collect

physiological data, volunteers’ self-reported data

and therapist interview data. By analysing the data

the plan is to derive heuristic rules linking

physiology to mental health prediction. When the

clinicians are satisfied with the rules the sensors will

be used as part of a Cognitive Behavioural Therapy

based on-line system. This system will be tested as

part of resilience treatment trials to be conducted in

the UK and Spain using the same sensors.

2.1 The Role of the EEG Sensor

The EEG is a record of the electrical potential

gradient oscillating around the brain and recorded

from electrodes on the human scalp and is often

labeled according to apparent frequency ranges

detected in the EEG signal power spectrum: delta (1-

4Hz), theta (4-8Hz), alpha (8-13Hz), beta (13-

20Hz), and gamma (roughly >20Hz). The scalp

electric potential amplitude is typically 20 to 100 μV

with a specific shape depending on the subject’s

state of relaxation (Srinivasan, 2006). Recent studies

have suggested that alpha rhythm is an oscillatory

component of the human EEG and applied widely in

many application fields, such as mental illness,

biometric identification, E-learning, etc. Both wake

and sleep EEG can provide biomarkers of depression

and anti-depressive therapy, respectively (Tang,

2009). Within OPTIMI the resting EEG is recorded

at three points on the forehead (FP1, FP2 and FPZ)

referenced to the two ear lobes (A1 and A2).

The majority of studies targeting depression

report characteristic differences in EEG asymmetry,

especially in the alpha band, recorded from

electrodes placed on frontal locations. In particular

the study performed by Miguel A. Diego supports

previous findings that indicate that greater relative

left frontal EEG alpha activity is evident in people

with depressive symptomology (Diego et al., 2002).

In addition the findings of Vuga et al support the

view that resting frontal EEG asymmetry reflects a

moderately stable individual difference in adults,

irrespective of sex and history of depression. [4]

Another good candidate for predicting

depression is the measurement of EEG signal

complexity. J L Nandrino et al. found that the EEG

dynamics of major depressive subjects is more

predictable, that is less complex, than that of control

subjects(Nandrino et al., 1994). Tang et al. showed

that alpha rhythm entropy in depressives is increased

during resting compared to when perfroming mental

arithmetic and when compared to healthy control

group (Tang, 2009).

Within OPTIMI the two above measures, Alpha

asymmetry and C0 Complexity, will be of particular

interest as well as Beta/Alpha and Alpha/Theta power

band ratios which can reflect a person’s mood.

The EEG sensor must reliably record the user’s

EEG signals over periods of approximately 90

seconds in order to obtain sufficient data for the

above algorithms. To achieve this the sensor must

meet certain electrical specifications which will be

described later. Additionally the sensor should

provide a means to grade the quality of contact

between the electrodes and the skin on the forehead

since this contact quality must be sufficient if the

raw data is to be useful.

Artefacts due to eye movements and forehead

muscle movements are often detected in EEG raw

data. Both 50Hz noise from electrical power lines

and artefacts must be removed by a denoising

algorithm to provide pure data before the algorithms

are applied.

At first the algorithms and de-noising will be

computed on the HomePC with the sensor purely

acting as a wireless EEG recorder controlled by the

HomePC. However once the results of the

calibration trials are fully interpreted the aim is to

move many of these data processing tasks to the

sensor, including a neuro feedback mode, making it

less platform dependent and highly portable.

3 SENSOR HARDWARE

The sensor electronics is depicted in Figure 1. The 5

electrodes on the left are connected first to a signal

conditioning circuit to clamp any voltage spikes that

may arise due to electrostatic discharge. In addition

passive filtering is performed to remove 100Hz or

higher input noise.

The electrodes connect to A1, the left ear lobe,

FP1, the left hand side of the mid forehead, FP Zero,

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165

Figure 1: EEG Sensor, primary hardware components.

the middle of the forehead, FP2 the right hand side

of the middle forehead and A2 the right earlobe.

These signals are used to drive the 8 channels of

the ADS 1298 instrumentation amplifier ADC from

Texas Instruments (Texas Instruments). This device

provides 8 high impedance separate inputs for multi-

channel amplification and multiplexing and digitizing

any selected channel to a resolution of 24 bits.

The 5 input leads are inter-connected in a manner

so as to obtain the following channel combinations:-

• FP1 relative to A1

• FP0 relative to A1

• FP2 relative to A1

• FP1 relative to A2

• FP2 relative to A2

• FP1 relative to FP2

At the centre of the sensor is a STM32 F101CB,

(from ST Microelectronics) (ST Microelectronics )

which is an ARM 32-bit Cortex™-M3 CPU with a

maximum 36 MHz clock, 1.25 DMIPS/MHz

(Dhrystone 2.1) performance. It includes 128 Kbytes

of Flash memory, 16 Kbytes of SRAM, two SPI data

buses and a generous number of general purpose I/O

lines.

The CPU communicates with the ADS 1298 via

a dedicated SPI bus configured to operate at the

highest speed available. The second SPI bus is

connected to a block of flash memory and an RF

communications front end.

The 32MB of flash memory provides the main

storage for the sensor. The amount of memory is

only restricted by PCB real estate and memory cost.

The RF frontend allows the CPU to accept

commands from the HomePC as well as exchange

EEG raw data or processed results. In order to

maintain compatibility with other sensors in the

OPTIMI project, the RF front end is based on the

nRF24L01+ low power 2.4GHz ISM (Industrial,

Scientific and Medical) band RF Transceiver from

Nordic Semiconductor (Nordic Semiconductor WEB

Site). This method of communications was preferred

over traditional methods such as Bluetooth because

the hardware platform is extremely accessible and

allowed one to develop energy efficient

communications protocols.

The standard EEG channel specifications

required by such typical applications are mentioned

in the review of Tan, Ibrahim and Moghavvemi (Tan

et al., 2007) in which they suggest:-

• Input impedance: 47Mohms

• Bandwidth: 1 to 30Hz.

• CMRR: 80dB to 90dB

• Gain: 100,000

• Power: 3mW per channel

Commercial products such as the Nexus-4 (from

Mindmedia) (Mind Media, WEB Site) and the

ENOBIO (from Starlabs) (Starlabs WEB Site) quote

similar or better figures.

The ADS1298 is derived from a family of

multichannel, simultaneous sampling, 24-bit, delta-

sigma analogue-to-digital converters with built-in

programmable gain amplifiers, internal reference,

and an on-board oscillator. This component has an

extremely low input bias current of 200pA (typical)

and an input-referred noise of 4μVPP (typical). The

CMRR is about -115dB, 500Megohm input

impedance, effective gain of 1,000,000 and the

power is very low in the order of 0.75mW/Channel.

The data rate is from 250SPS to 32kSPS. The chip is

9mm x 9mm and requires few external components.

Therefore a very compact design may be achieved.

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In this sensor, the gain is set to 6, the reference

voltage at 2.4V and the sampling rate is 260Hz

which can also be easily set to other rates if

necessary.

The sensor incorporates a 470mAh Lithium

Polymer single cell battery. This allows the sensor to

be used continuously for approximately 6 hours.

Since the trials are planned to include twenty four

sessions each lasting 5 minutes a single full charge

should ensure the sensor never needs to be recharged

during the whole trial period.

3.1 Firmware Functionality

The sensor software currently comprises a number

of critical firmware functions. These include:-

• Accepting commands from the HomePC over

the wireless communications link.

• Performing a signal quality check and relaying

this to the HomePC

• Recording a specified length of EEG data into a

simple multiple file structure

• Streaming raw EEG data for test purposes

• Downloading data files to the HomePC over the

RF link

• Erasing data files

An important feature of the sensor is the ability

to perform a signal quality check. This feature is

necessary to provide the HomePC application a

means of verifying that the user has put the sensor

onto their head, in a manner that ensures the data

recorded is true EEG data with little noise.

When any EEG sensor is used to record very low

voltage brain waves activity, care must be taken to

ensure the electrodes are in good contact with the

skin such that the electrode to skin impedance is as

low as possible. This ensures an optimal detection of

the brain potentials. In addition it ensures that any

ambient electrical potential, such as generated by

nearby electrical lamps and office machines is less

likely to interfere due to the fact the electrodes

measure the potential originating in the human body

rather than the potential gradient in air space.

The signal quality check is performed in a fully

standalone manner by the sensor following the

reception of a GetSignalQuality command by the

HomePC. It first records 4 seconds of EEG data and

then carries out a detailed analysis in which the

sensor determines the Variance in the time series

data as well a power spectral analysis.

A value for signal quality is calculated based

upon:

• the variance in the signal which should be

between a set of typical limits

• the spectral power at 50/60 Hz

• the brain activity in the Alpha band

• the brain activity in the Delta, Alpha and

Beta bands as compared to the Gamma and

higher frequencies to 60Hz.

By a combination of ratios and limits a signal

quality value is calculated between 0 and 100 and

this value is sent to the HomePC which instructs the

user to check or reposition the sensor if the quality

falls below 70.

3.2 Choice of Electrodes

In recent years a great deal of work has been applied

to developing active dry EEG electrode technologies

both in the research and commercial domains [12.

The aim has been to achieve a number of advantages

over the traditional approach. In the traditional

approach a conductor such as a stainless steel or gold

plated electrode is brought near the surface of the

skin. Then a wet gel such as NUPREP™ EEG skin

prepping gel is used to lower the skin conductance.

The gel is rather messy and is particularly annoying if

it gets into ones hair. Normally the electrodes are

connected to relatively long and heavily shielded

expensive cables which go off towards the amplifier

device next to a patient’s bed.

As a result research teams have aimed to place

active electronics into the electrodes so that they

could pre-amplify and match impedance close to the

skin and remove the need for a gel as well as avoid

the need for active shielded cables.

In the commercial solutions now available and

known to the authors, it would appear that active

electrodes result in a more expensive design due to

the higher complexity, additional amplifiers and

power. The electrodes are also larger than the

smallest passive electrodes.

For economic and ergonomic reasons the sensor

developed here did not incorporate active electrodes

and instead makes use of very low cost disposable

skin friendly solid gel pads. Solid gel pads provide

all the electrical benefits of wet gel and do not leave

any gel or fluid on the skin which is often the

complaint during EEG usage. When the recording is

completed the pads are simply removed and

discarded, providing a higher level of hygiene.

3.3 Ergonomics and Economics

From the very start of the OPTIMI sensor

development, two things were made very clear to the

designers. Firstly, the EEG sensor’s final assembled

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167

and tested unit cost would have to be below 90

Euros. Secondly the design would have to pass a

usability acceptance rate of 39 out of 40 users

invited to use it as deployed in the trials. That meant

that only 1 in 40 subjects should be likely to refuse

to use the sensor on ergonomic grounds. This set

extremely difficult economic and ergonomic

constraints in addition to the normal functional

design constraints of the sensor.

Economics

In order to achieve low cost the designers reviewed

the state of the art and determined different cost

scenarios for alternative designs, manufacturing

methods, battery charging methods, user interface

and packaging. A core base functionality was

necessary and was achieved through the value line

STM32 F101 device, a limited 32MB of serial flash

memory and the compatible nRF24L01+ RF

transceiver.

After studying different options it became clear

that the single ADS 1298 device, encapsulating all

the functionality of the EEG front end and ADC,

would save money over the use of discrete

components and the associated assembly cost.

Active dry electrode sensors were considered in

the design. However since it became clear that the

sensor would be worn on the forehead, with very

short electrode cables there was no technical reason

to implement this higher cost solution. In addition it

was decided that disposable solid gel pads, which

cost a few cents each, were to be used to optimise

skin conductance while maximising hygiene.

Battery charging is performed using a non-

contact inductive method. This is done to maintain

compatibility with all OPTIMI sensors which must

be hermetically sealed, as they are worn for long

times near the body.

The final packaging of the device is kept very

simple, with the overall PCB being coated in an

encapsulating two part epoxy resin (ALH Systems

Ltd., U.K.) that has been chosen to provide

maximum water resistance and maintain hardness to

over 80 degrees C. The encapsulated board is then

primed and coated with a thin conductive coating

paint and then varnished. This conductive layer

helps to reduce the effects of ambient electrical

potentials being detected. In fact due to the small

size of the final sensor and direct coupling of the

ADS 1298 to the shielded electrode cables, the

effects of ambient noise are surprisingly small.

The sensor is worn on a low cost head band

which was chosen for economic but also strong

ergonomic reasons.

Ergonomics

To assist in the design of an ergonomic sensor a

usability study was commissioned to run in parallel

with the R&D work. This study consisted of a

developers’ session, two focus group sessions and a

detailed survey conducted in each of the trial sites.

To start with the sensor design and developers

group were invited to highlight concerns and

constraints of developing these sensors. Then in the

first focus group session the OPTIMI philosophy

and mock up sensors were presented to 14

volunteers 7 male, 7 female with age ranges from 18

to 64.

Figure 2: Early mock up of the EEG sensor.

By studying the response to the mock ups and

scenarios of use, the concerns of the developers were

matched against the concerns of the focus group to

highlight important design goals and constraints.

Following this, design specifications were

created and prototype work commenced. During the

second focus group, working prototypes were

presented to 18 volunteers who were asked to wear

and use the sensors. As a result further feedback was

obtained, prioritised and several design changes

resulted.

Finally questionnaires were sent out to 50 people

representing typical volunteers in each trial site.

These questionnaires further clarified if there were

significant cultural adjustments that needed to be

made given that the focus groups were held only in

Switzerland.

As a result of this usability study a large number

of design modifications were made to the developers

original design ideas. In the end it was felt the

design satisfied the specific needs of the people in

the focus groups as well as the points derived from

the regional surveys. The following describes a few

of the important conclusions coming from this

usability study:

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Figure 3: EEG sensor, from front, from behind. Note the electrodes are carried on the black elastic headband. The weight of

the electronics is supported on a solid band worn as eyeglasses.

Figure 4: Sensor worn by a volunteer.

• 10 seconds to put on or remove

• Quick Recharge every 1 to 2 months

• All cables must be short and ultra-flexible

• Very light pressure, light weight ear clips

• Light weight headband and small electrodes

• Electronics separated from the band

• High preference for disposable solid gel

pads

• No skin allergy reaction

• Use soft feel hook and loop straps

• Lady’s long hair must never get caught

• LEDs to tell what the sensor is doing

• Simplest HomePC application

4 PERFORMANCE RESULTS

The EEG sensors developed and finally

manufactured have recently begun deployment in

large numbers (45 currently) in the calibration trials

in Spain, Switzerland and China.

Before this the sensors were rigorously tested

and data from the sensors was used in laboratory

experiments to ensure the EEG data obtained was of

the highest quality for use in the trials.

For this paper the performance will be described

by first showing the time and frequency domain

results obtained under different conditions. Then a

comparison will be presented between the OPTIMI

EEG sensor and a well-known commercial product.

Finally some initial results obtained from the on-

going calibration trials will be used to indicate the

ergonomic performance and acceptance by users.

Time and Frequency Domain

In order to compare the sensor under different noise

conditions recordings were made of volunteers in the

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169

resting, eyes closed position.

Two environments were tested. First the

recordings were taken in a public wilderness park

near Zurich where there are no visible sources of

electrical power usage. With several volunteers to

record, this was simpler, faster and less costly

exercise when compared to using a dedicated Faraday

cage system. Secondly a typical office environment

was arranged with several running desk top

computers, lamps, printers and copiers in the room.

Raw EEG data was gathered using the sensor

alongside a PC based test application software. The

raw data was then processed using MATLAB for

visualisation and for the time and frequency domain

analysis (TD and FD).

Analysis of Raw Data

Figures 5 to 12 relate to the recorded signal from a

single channel (FP1 relative to A1). Figure 5 shows

the TD signal with the sensor left to rest in free

space and not placed on the head of the volunteer.

Figure 5: Forest, Off head, Noise, Time Domain.

The peak amplitude is 9uV with an RMS value

of 4uV. This noise includes white noise in the

environment as well as sensor produced noise.

Figure 6: Forest, Off head, Noise, Frequency Domain.

In Figure 6, the FD power spectrum for the

signal is shown. In general the noise power is

distributed over all frequencies, with some

amplification at around 8Hz with no frequency

exceeding 0.6uV.

In Figure 7, the sensor is worn on the head, in the

forest environment. The volunteer keeps their eyes

closed and rests in a calm state of mind and body. The

primary aim is to verify the typical EEG pattern in a

noise free environment. The signal peaks at 60uV

with a typical RMS value of 25uV. We assume 4uV

RMS of noise is embedded in this signal.

Figure 7: Forest, On head, Resting Eyes Closed, Time

Domain.

Figure 8 shows an excellent and typical noise

free EEG resting eyes closed FD spectral plot. With

the eyes closed the FD plot clearly shows the Alpha

Band peaking around 10Hz, with additional Theta

band activity and low Beta band activity.

Figure 8: Forest, On head, Resting Eyes Closed,

Frequency Domain.

In the office with the sensor left to rest in free

space, the sensor records airborne electrical potential

gradients from the office machines. Figure 9 shows

the TD signal, peaking at 200uV with a typical RMS

of 170uV. Figure 10 shows the FD plot clearly

indicating that the major energy is detected at the

50Hz band.

In the office with the sensor left to rest in free

space, the sensor records airborne electrical potential

gradients from the office machines. Figure 11 shows

the TD signal, peaking at 200uV with a typical RMS

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of 170uV. Figure 7 shows the FD plot clearly

indicating that the major energy is detected at the

50Hz band.

Figure 9: Office, Off head, Noise, Time Domain.

Figure 10: Office, Off head, Noise, Frequency Domain.

Figure 11: Office, On head, Resting Eyes Closed, Time

Domain.

Figure 11 and 12 show the results of recording in

the office with the sensor worn correctly in the eyes

closed resting position. What is now clear is that the

office noise at around 50Hz is very apparent within

the EEG FD plot. However the noise in the office

has not in any way compromised the response at the

frequencies of interest namely the Alpha and Theta

bands. The 50 Hz noise measured here is largely due

to the potential gradient set up in the human body

and measured between the two electrodes. These

gradients are due to tiny electrical currents arising

from the capacitively coupled voltages on equipment

in the nearby environment entering the body and

passing through it towards the earth or another

nearby equipment. Since these are truly human body

voltages there is no real method to supress them

other than to add a 50Hz band stop filter on the

processing stage of the raw data.

Figure 12: Office, On head, Resting Eyes Closed,

Frequency Domain.

Comparison with Commercial Eeg

In order to determine if the sensor was performing as

well as an off the shelf commercial sensor, a test was

carried out in which a volunteer wore both the OPTIMI

sensor and a NEXUS-4 EEG sensor from Mindmedia

(Mind Media, WEB Site). The NEXUS-4 device is a

very popular wireless Bluetooth based device used in

many research groups and clinical settings and has an

impressive specification. It is rather larger than the

OPTIMI sensor and cannot be worn as ergonomically.

It is limited to 4 electrodes and would be harder to

integrate seamlessly into the OPTIMI wireless system.

It is priced at around 3000 Euros.

Figure 13 shows the volunteer wearing the

OPTIMI EEG headband on her forehead. Above the

band can be seen the single electrode from the

NEXUS-4 EEG sensor stuck near the OPTIMIT

EEG sensor electrodes. Lower down one can see the

NEXUS-4 processing unit being held in her hand.

Figure 14 to 16 show the time domain and

frequency domain results comparing the signals

recorded from the two sensors. In Figure 14 and 15

is shown the time domain raw data plot. In the first

figure one can see that the general amplitude, DC

drift and general shape of the two waveforms are

more or less identical. On close inspection one can

see that the phase of the two signals is very

synchronised while the amplitude in some cases

varies significantly. Since the two sensors do not

share the exact same physical location and

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171

impedance contact with the skin, some differences

are likely to occur.

Figure 13: Comparison of NEXUS-4 and OPTIMI EEG

Sensor.

Figure 14: Drift comparison with NEXUS-4.

Figure 15: Phase Amplitude Comparison with NEXUS-4.

From the frequency response shown in Figure 16,

one can see that both sensors manage to provide

similar

FD plots, especially highlighting the peaks in

Alpha, Theta and Beta bands. The amplitude across the

spectrum is very close suggesting very similar input

response functions.

Figure 16: Frequency Response Comparison with

NEXUS-4.

Ergonomics and User Acceptance

Approximately 45 EEG sensors are now deployed in

three trial sites around the world. In certain cases the

sensor is shared between two or more volunteers in

dormitory scenarios. Over the past 5 weeks 52

volunteers have now used the sensor as required. We

can report that to date not a single user has found

any difficulty in putting on the sensor and

performing a recording. Some difficulties have been

experienced in operating the HomePC application to

start and end the recording. In a specific case of our

experiments, in which audio clips were played to

volunteers during the recording period, the

volunteers were required to wear headphones at the

same time. This lead to the user dislodging the

reference ear clip from the ear lobe and resulting

loss of data. As a result adhesive dry gel pads are

used behind the ear at M1 and M2. In some cases the

ear clip itself was broken as users pulled on the

cables to remove the electrode. The ear clip has

therefore been redesigned.

5 FURTHER WORK

The current version of EEG sensor will be modified

to include the noise and eye movement filtering that

is established post calibration trials.

In addition it is planned to add a neurological

feedback functionality in which the sensor will

identify the current level of mental stress of the user.

This level will be presented on a simple level

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indicator, such as an audio signal. The sensor may

then be used as a simple assistive tool to practice

Cognitive Behavioural Therapy based biofeedback

aimed at relaxation before going to sleep.

The sensor will also be used as a low cost

diagnostic tool by project partners to evaluate other

forms of assistive diagnosis of stress and anxiety.

6 CONCLUSIONS

During the first year of the OPTIMI project a low

cost ergonomic sensor has been developed and

manufactured, from the bottom up, to meet the needs

of a large e-health trial.

The need to ensure volunteer compliance and

tight economic controls has been met to a high

degree in the work reported. This has been achieved

by designing the sensor in parallel with the guidance

of a user needs analysis that includes the views of

developers, the views of user represented by focus

groups and the views of typical end users in the

country of deployment.

Economic targets have been met by using state of

the art solutions combined with sophisticated

embedded software. The sensor is now in use in high

numbers and will soon be adapted to meet the needs

of customised usage scenarios by firmware upgrade.

ACKNOWLEDGEMENTS

We acknowledge the co-funding received under the

EU project OPTIMI www.optimiproject.eu .

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