Localized Electroencephalography Sensor

and Detection of Evoked Potentials

Tiago Ara

ujo

, Neuza Nunes

, Carla Quint

and Hugo Gamboa

CEFITEC, Physics Department, New University of Lisbon, 2829-516, Caparica, Portugal

PLUX - Wireless Biosignals, S.A., Av. 5 de Outubro nr.70 6th, 1050-059, Lisbon, Portugal

Abstract. The limitations of current electroencephalographs are unanimous and

relate primarily to its low spatial density and assembly complexity for certain

applications. In this paper, we introduce an innovative technique, capable to an-

swer to some challenges created by those limitations. A localized, miniaturized

and user-friendly electroencephalography (EEG) sensor was developed for signal

acquisition in a bipolar montage, to be placed anywhere on the scalp. The low

consumption, small size and high spatial resolution, compared with the standard

systems, are the main features of the EEG sensor presented. With this sensor we

acquired and analyzed spontaneous EEG activity and auditory evoked potentials

which are relevant for the cognitive activity analysis. The developed sensor is an

important unobtrusive tool for applications of mental health evaluation.

1 Introduction

Electroencephalography (EEG) is a clinical tool with high functional relevance in the

diagnosis of various diseases. Despite its wide use and good temporal resolution, the

EEG has some clinical and research limitations comparing to other techniques. Its lim-

itations are unanimous and relate often to low density spatial records and the sheer

assembly complexity for certain applications (typically 19 electrodes over the scalp),

which causes a lengthly experiment preparation and a discomfort to the subject.

Various solutions in order to overcome those technical limitations have been ex-

plored, like the study of high density EEG [1][2] and the EEG integration with different

acquisition techniques, such as the EEG-fRMN [3], however non of those solutions an-

swer to the lack of ergonomics in current systems and there are vast applications for

which a smaller and localized tool would be useful.

The EEG is typically described in terms of rhythmic or spontaneous activity, which

is composed by rhythms which refer to the subject’s condition, and transients or evoked

potentials that reﬂects the brain response to a stimulus.

An evoked potential is an electrical potential recorded from the nervous system af-

ter a stimulus. After the application of a stimulus, a speciﬁc neuron population activates

simultaneously or sequentially, creating evoked potentials that can be measured at the

scalp. Evoked potentials amplitudes tend to be low comparing to spontaneous poten-

tials, ranging from less than a microvolt to a few microvolts [4][5]. To resolve these

Araújo T., Nunes N., Quintão C. and Gamboa H..

Localized Electroencephalography Sensor and Detection of Evoked Potentials.

DOI: 10.5220/0003891300410046

In Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health (MindCare-2012), pages 41-46

ISBN: 978-989-8425-92-8

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

low-amplitude potentials, the stimulus should be repeated 50 to 200 times and a sig-

nal averaging should be performed. This procedure distinguishes the evoked potentials

from the noise provoked by the spontaneous potentials which are stimulus independent

[6].

The depolarization of a stimuli response can be positive (e.g. P300) or negative

(e.g. N200) and with varied latency. The potentials are gradually more complex with

the increasing latency from stimulus to response. Therefore, the potentials with small

latency rely on the physiological perception of stimuli while components with bigger

latency essentially depend on cognitive mechanisms like attention or expectation. The

P300 wave, for example, links mainly to the stimulus classiﬁcation, while the N200

wave associates with perception and discrimination of a stimulus [7].

In this paper we introduce an innovative technique, capable to answer to some chal-

lenges created by the limitations of current electroencephalographs. A localized, minia-

turized and user-friendly EEG sensor was developed for signal acquisition in a bipolar

montage anywhere on the scalp. The developed sensor was used to collect and ana-

lyze EEG auditory evoked potentials. The acquisition of evoked potentials allowed us

to visualize and study stimuli such as N100, N200 and P300, relevant markers for the

analysis of cognitive activity.

The low consumption, small size and higher spatial resolution, compared with the

standard systems, represents a solution of high applicability in unobtrusive long-term

cognitive activity monitoring.

2 Localized EEG Development

In this section we describe the steps of the EEG sensor design and development, point-

ing its main characteristics.

2.1 Electronics

The EEG sensor has three channels of acquisition and the signal of each channel is

acquired by two electrodes assembled in a differential conﬁguration which measure the

electrical activity at two nearby points of the cortex. As the signal variation between two

points tend to zero with the proximity, a high ampliﬁcation is needed for this sensor. Our

sensor has a total gain of 40000, which is four times superior to the gain of common

electroencephalographs, leading to a high sensitivity in the signal acquisition. This high

sensitivity requires an efﬁcient ﬁlter chain.

The EEG sensor circuit has three ampliﬁcation and two ﬁlter levels as shown in

Figure 1.

The ﬁrst ampliﬁcation stage has a small ﬁltering step to remove the high frequency

noise at the acquisition entrance. This is a radio frequency interference ﬁlter (RFI) with

a cut frequency superior to the remainder circuit’s bandwidth. The low-pass and high-

pass ﬁlters cancel the high frequency noise and emphasize the frequency band of inter-

est. The cut frequencies of each ﬁlter are 0,79Hz (high-pass) and 48,22Hz (low-pass),

which are the standard values for the EEG signal’s bandwidth. Each ﬁlter has a real

pole in the cut frequency which gives an attenuation of -3dB for decade. As we have

Fig. 1. Circuit of one channels of the EEG sensor.

two high-pass and two low-pass ﬁlters, the poles are duple, so the real attenuation is

-6dB in relation to the maximum for the two cut frequencies.

The ﬁnal version of the EEG sensor is a closed loop of three acquisition channels,

in which each channel is the circuit described above (and presented in Figure 1).

2.2 Characteristics of the Sensor

The EEG analog to digital conversion and Bluetooth transmission to the computer was

performed using a bioPLUX research [8] signal acquisition system, which has a 12 bit

ADC and a sampling frequency of 1000 Hz.

The circuit was encapsulated in a structure designed to support all the electronic and

enable the contact of the scalp, containing three conductive electrodes. The developed

sensor is portable and miniaturized, with 43mm of diameter and 50g of weight. An

illustrative photograph of the developed sensor is presented in Figure 2.

The standard procedure for this sensor is to use Ag/AgCl pre-gelled electrodes and

a band strap over the sensor to enable an appropriate setting and contact with the scalp.

The ground electrode sticked to a bone surface, behing the ear.

Fig. 2. Photograph of the localized EEG sensor.

3 Acquisition of Evoked Potentials

This section describes the procedure used to induce the evoked potentials and the pro-

cessing tools needed to visualize those potentials.

3.1 Firmware

The auditory stimuli was generated by a microcontroller which synchronized the sig-

nal acquisition with the stimuli application (through headphones), marking the stimuli

occurrence in the collected EEG signal. Those markers were different to each stimulus.

The infrequent stimulus had a sound frequency of 1900Hz and the frequent stimulus

900Hz. Both stimuli had an intensity of 81dB and last for 50ms. The stimulus type and

frequency are based on a distribution of pseudo-random numbers. The infrequent stim-

uli occur with 15 % of probability, following an uniform distribution of 100 numbers,

and the time between stimuli also follows an uniform distribution between 1100ms and

1400ms, to raise the expectation level of the subject.

3.2 Methods

EEG data was recorded from a healthy volunteer with no history of hearing, neurolog-

ical, learning or stress problems. The subject was sitting in a relaxed state in a quiet

environment with minimum distracting elements. The subject was equipped with head-

phones in mono conﬁguration which received auditory stimuli from the microcontroller

and was asked to count the rare sounds that appeared in a series of frequent sounds.

The EEG sensor was positioned at the left occipital region of the head, located

near the F3 electrode of the 10-20 EEG standard system [9]. We used AgCl pre-gelled

disposable dry detection surfaces ﬁxed with an elastic band strap.

3.3 Signal Processing

After the acquisition procedure, we used Python to visualize and process the EEG data.

As the evoked potentials amplitudes tend to be low, a signal processing code was devel-

oped to enhance the evoked potentials over the other EEG tracings. The ﬁrst step was to

go through the signal and ﬁnd the markers left by the microcontroller after a stimulus.

The 800ms following the position deﬁned by the marker were saved as infrequent or

frequent values, depending on the marker left by the microcontroller.

This process was repeated to each stimulus and the average of each frequent and

infrequent vectors was computed.

4 Results

Figure 3 presents the results obtained for 3 tests performed on the subject, who remained

with his eyes closed.

In ﬁgure 3 a) (ﬁrst test) we can identify the N200/P300 complex in the infrequent

stimuli graphic. This complex begins with a negative depolarization near 200ms, fol-

lowed by a positive component near 300ms. The noise present in the infrequent stim-

uli is due to the low repetition rate (19 times). In ﬁgure 3 b) is also noticeable the

N200/P300 complex in the infrequent records, with a noise level smaller due to the

higher number of stimuli. In the third test (ﬁgure 3 c) ) the component P300 appears

with much less intensity. The N200 component is still visible in the infrequent waves

graphic.

Fig. 3. Subject’s Evoked Potentials: a) ﬁrst test with 19 infrequent and 751 frequent stimuli; b)

second test with 51 infrequent and 1187 frequent stimuli; c) third test with 25 infrequent and

1132 frequent stimuli.

The N100 complex is also noticeable specially in the frequent responses, although

with less deﬁnition.

5 Discussion and Conclusions

The N100 potential appears with low deﬁnition in all tests, which can be explained for

the position where the sensor was placed. The F3 site favors the acquisition of N200

and P300 potentials and attenuates the N100 components. The N200/P300 complex

emerges in all tests, but in the third the N200 component is much more pronounced than

the P300. That happens because in the third test the subject was already accustomed to

the task and was performing the test with less expectation than the ﬁrst times - hence

the distinctness of N200 which associates with sound perception over the P300 which

mingle with attention and expectations. The habituation factor decreases the impact of

the test than when done for the ﬁrst time.

A prototype version of this sensor was previously presented with the detection of

EEG alpha rhythm and processment of its latency [10]. The acquisition of evoked po-

tentials, because of its low amplitude values, represents a more effective valitation to

evaluate the high sensibility of the sensor. The development procedure denoted a high

efﬁciency level for this sensor, in accordance with the user-friendly, signal quality and

ergonomics requirements, taking into account that the results were achieved directly

with the raw signals without any ﬁltering process.

A sensor with this speciﬁcations has high applicability in various scenarios, since it

could allow to record huge amounts of data related to everyday and ambient assisted liv-

ing activities. This sensor could also be meaningful for analysis of sleep disorders, as a

sensor like this has minimum inﬂuence to the patients sleep given its ergonomics char-

acteristics. Another possible application is the monitoring of epileptic seizures, since

these appear uniformly through the brain and its signal can be acquired in any point of

the scalp. In a more futuristic view, we can afﬁrm that a localized sensor like the one we

present can contribute in areas like brain computer interfaces (BCI). In this ﬁeld is con-

venient to monitor a speciﬁc brain structure and associate it to an interface control, so

our sensor represents a big contribution through its localized acquisition characteristics.

Future Work

In future steps of this project we intend explore and validate the sensor’s capacity to

map the cerebral activity.

In order to further reduce external noise, we intend to eliminate the cables that

connect the conditioning circuit to the scan and transmission circuit, integrating both

components inside the same physical packaging.

We also intend to raise the channels number in the same area of the sensor.

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