A LOW-COST EEG STAND-ALONE DEVICE FOR BRAIN

COMPUTER INTERFACE

Alexandre Ribeiro†, Ant

onio Sirgado†, Jo

ao Aperta†, Ana Lopes†‡

Jorge Guilherme†, Pedro Correia†, Gabriel Pires†‡ and Urbano Nunes‡

†Department of Electrical Engineering, Polytechnic Institute of Tomar, Tomar, Portugal

‡Institute for Systems and Robotics, University of Coimbra, Coimbra, Portugal

Keywords:

EEG, Stand-alone device, BCI, SSVEP.

Abstract:

This paper describes the ongoing development and design of a portable and stand-alone EEG device to be

used as a Brain Computer Interface (BCI) for wheelchair steering. The overall system comprises the EEG

(electroencephalogram) ampliﬁer, data acquisition, communications and stimuli generator. The main attrac-

tive feature of this system is its stand-alone operation which makes it independent of a computer for signal

processing and visual stimuli presentation. Preliminary results obtained from experiments exploring alpha

rhythms and steady state visual evoked potentials attest the overall functionality and robustness of the system.

1 INTRODUCTION

EEG portable devices are mainly used for long term

signal recording or for remote real-time monitoring

(Jiang and Wang, 2005). They are usually small sized

and low-cost and therefore suitable for a daily using.

EEG-based BCI systems are however almost always

dependent of a personal computer (PC) and its screen.

This is mostly because they rely on real-time heavy

processing algorithms and because a computer screen

is needed to provide visual feedback information or

visual stimuli presentation.

Current main non-invasive BCI systems based on

EEG data are divided in three main classes according

to the type of neuromechanisms: event related syn-

chronization and desynchronization (ERD/ERS) of µ

and β rhythms usually associated with motor imagery

(Pfurtscheller et al., 1998), P300 evoked potentials

(Donchin et al., 2000), and ﬁnally, steady-state visual

evoked potentials (SSVEP) (Gao et al., 2003). The

ﬁrst approach is usually used for 1D or 2D cursor con-

trol on a computer screen to select targets or letters (as

a spelling device), or for playing simple games. The

large number of required EEG channels and the heavy

processing needed for EEG pre-processing, feature

extraction and classiﬁcation make this BCI approach

highly dependent on a computer system. The visual

P300-based BCI approach depends on the user at-

tentional focus to an infrequent speciﬁc visual stim-

ulus that occurs among other random stimuli (Pires

et al., 2008). The stimuli can be generated with

LEDs or with a low cost display controlled by a mi-

crocontroller, without resorting to a computer and

screen. Assuming that many EEG epochs are aver-

aged, then low computational demanding feature ex-

traction techniques can be implemented on a micro-

controller or DSP. However, the use of a large number

of epochs slows down the BCI transfer rate and it re-

quires a buffer of several seconds. In the last BCI ap-

proach, the SSVEP results from a stimulus ﬂickering

at a constant frequency. The ﬂicker stimuli are eas-

ily implemented with some LEDs, and signal feature

extraction and classiﬁcation can rely essentially on a

FFT (fast fourier transform) that can be computed on

a simple DSP.

Therefore, the SSVEP BCI approach is the only

one that can be easily implemented on a stand-alone

device. Some works have already been developed fol-

lowing a similar approach. The work presented in

(Gao et al., 2003) describes a stand-alone device DSP-

based. A 48 LED stimuli generator is used to control

external devices (TV, videos, etc.) through the clas-

siﬁcation of SSVEP signals recorded at the occipital

brain region. The LEDs are ﬂickering at frequencies

between 6 and 15 Hz with a 0.2 Hz resolution.

The use of a stand-alone device is of particular

importance for us since it is intended to be applied

as a BCI to steer a wheelchair (Pires and Nunes,

430

Ribeiro A., Sirgado A., Aperta J., Lopes A., Guilherme J., Correia P., Pires G. and Nunes U. (2009).

A LOW-COST EEG STAND-ALONE DEVICE FOR BRAIN COMPUTER INTERFACE.

In Proceedings of the International Conference on Biomedical Electronics and Devices, pages 430-433

DOI: 10.5220/0001780304300433

 SciTePress

2002). Therefore, computer independence, portabil-

ity and low power consumption are required criteria.

BCIs based on SSVEP are not considered true BCIs

because the users have to gaze the speciﬁc stimulus

and therefore have to move the eyes. Notwithstanding

this, the interface can be suitable for people with se-

vere motor disabilities and unable to control standard

interfaces such as joysticks, head switches, voice or

eye trackers, but still able to perform small eye move-

ments.

The remaining sections of the paper describe the

design and implementation of the system still under

development, and also some preliminary results al-

ready reached.

2 MODULE ARCHITECTURE

The diagram on Figure 1 illustrates the proposed ar-

chitecture. The ﬁrst module is a 6 channel EEG

ampliﬁer that receives EEG signal picked from non-

invasive electrodes. The output signal is in the range

of 0 − 5V. The data acquisition module is a PIC

18F4550 (Microchip, 2008), which was conﬁgured to

sample the signal at a 512 Hz rate and was imple-

mented based on a real-time interrupt. The A/D con-

verter has a 10 bit resolution. The digital signal is

then sent via USB to a PC for data recording, mon-

itoring or real-time signal processing. If working on

stand-alone mode, digital data is sent via SPI to the

dsPIC 30F3013. The dsPIC is able to perform some

statistical computation, digital ﬁltering and FFT com-

putation to extract signal features for classiﬁcation.

The output from decoded patterns can be directly sent

to a control unit on the wheelchair. The last module is

a stimuli generator implemented on the PIC 18F458.

It controls a set of LEDs that can be activated sequen-

tially, randomly, or at a given frequency (Figure 5).

The module is therefore suitable to generate stimuli

for SSVEP and for P300. In the case of P300 opera-

tion, each stimulus code is sent to the dsPIC via SPI or

to the PC via USB or RS232. Table 1 presents some

of the main speciﬁcations of the system.

On the present development stage, only one am-

pliﬁer channel was implemented. The signal process-

ing algorithms are not running at the dsPIC, but only

at the PC after signal recording.

2.1 Ampliﬁer

EEG signals are characterized by having very low am-

plitude values, typically in the range of 5-100 µV, re-

quiring a very accurate conditioning in order to am-

plify input signals and reject the existing noise or in-

Figure 1: System architecture.

Table 1: Overall system speciﬁcations.

Number of channels 6

Resolution 10 bit

Min input voltage step detect 195nV

Input voltage full scale 200 µV

Input frequency range 0.25-45 Hz

CMRR 100-120 dB

Noise spectral density 339 nV/

(Hz)

Min and Max Gain 66-91 dB

Current per channel 6 mA

terferences. A high input impedance of the condition-

ing module avoid signal distortion due to loading ef-

fects.

An EEG ampliﬁer module was developed for

signal conditioning and ampliﬁcation. The mod-

ule design follows the OpenEEG project approach

(OpenEEG-Project, 2008). The main difference was

the insertion of a notch ﬁlter to reject the 50 Hz from

power line interference. This improvement made pos-

sible the use of standard electrodes, whilst the Ope-

nEEG project needs non-standard shielded electrodes.

The ampliﬁer module is shown in Figure 2.

The EEG signals acquired from the electrodes pass

through the protection circuit who acts as a voltage

and current limiter. The instrumentation ampliﬁer

with 28dB of gain is implemented with the INA128P

circuit that is characterized by having low offset volt-

age, typically 50 µV, and high Common Mode Re-

jection Ratio (CMRR) (between 100-120 dB). One

second-order high-pass ﬁlter with cut-off frequency

at 0.16 Hz and gain 15 to 40dB follows the INA128P

in order to increase the input signal amplitude and

to reject the DC component imposed by the ampli-

ﬁer offset voltage. Then, the signal passes through

an anti-aliasing ﬁlter that is a third-order Bessel low-

pass ﬁlter with cut-off frequency of 45Hz and gain

22.4dB. In order to reject the 50Hz component, the

A LOW-COST EEG STAND-ALONE DEVICE FOR BRAIN COMPUTER INTERFACE

431

Figure 2: Block diagram of EEG Ampliﬁer.

Figure 3: Notch ﬁlter circuit.

EEG ampliﬁer stage includes one notch ﬁlter imple-

mented using the GIC (Generalized Immittance Con-

verter) topology with adjustable central frequency, f

varying between 44Hz and 58Hz and 50dB of atten-

uation at f

. The notch ﬁlter circuit implementation

and EEG ampliﬁer frequency response simulation are

shown in Figures 3 and 4. The signal conditioning

system has a noise spectral density of 339nV/

(Hz),

which achieves a signal-to-noise ratio higher than the

maximum required by the A/D converter of 61.96 dB

for a N = 10 bit converter (Eq. 1).

SNR = 6.02N + 1.76dB (1)

The circuit dynamic range has to take into account the

signal dynamics of a crest factor of about 6dB plus

the required SNR of the ADC. Therefore the required

circuit minimum dynamic range is 67.96dB. The cir-

cuit SNR, considering a noise bandwidth of 256 Hz is

about 120dB which is more than enough for the appli-

cation. The sampling frequency of the ADC was set

to 512Hz that is a decade above the aliasing ﬁlter cut

off frequency. The ﬁlter attenuation at half the sam-

pling frequency is not critical since the type of signal

detected by the sensor do not fall in this frequency

zone.

Figure 4: Ampliﬁer gain obtained from PSPICE simulation.

3 EXPERIMENTS AND RESULTS

Two experiments were performed with two subjects

to evaluate the performance of the system. The EEG

activity was recorded from Ag/Cl electrodes at posi-

tion Oz according to the internacional 10-20 standard

system (Figure 5). The electrode was referenced to

the right mastoid and the ground was placed at AFz.

The cap and electrodes are g.tec products.

The ﬁrst experiment consisted on the analysis of

the alpha rhythms (typically in the range 8-12 Hz).

It is known that there is an increase of the amplitude

of alpha rhythms when the eyes are closed and a de-

crease when the eyes are open (Feige et al., 2005).

Figure 6 (top) presents the recorded signal, bandpass

ﬁltered between 8 and 12 Hz, during the two situ-

ations. It was asked the user to be relaxed and to

close the eyes and open them (without any kind of vi-

sual stimuli in front) alternately during some seconds.

During the open-eyes period there is a clear decrease

of alpha amplitude, and an increase during the closed-

eyes period. In Figure 6 (bottom), the FFT of the non-

ﬁltered recorded signal shows a peak at the 10 Hz

when the eyes are closed. To compare and evaluate

the results, similar experiments were performed with

a certiﬁed commercial ampliﬁer, reaching the same

results.

The second experiment was performed to validate

simultaneously the stimuli generator and the SSVEP

BCI approach. The user was asked to gaze alternately

two LEDs stimuli blinking at frequencies 15 Hz and

16 Hz. Figure 7 shows the SSVEP signals recorded

at Oz channel. The two frequencies were clearly dis-

criminated. The plot correponds to the application of

the FFT to a time window of 1 s (1 Hz resolution).

These results attest the validity of the overall system

and also of the SSVEP BCI approach.

4 CONCLUSIONS

This paper describes the implementation of a low cost

EEG system for BCI. The ampliﬁer, digital data ac-

BIODEVICES 2009 - International Conference on Biomedical Electronics and Devices

432

Figure 5: Subject with electrodes cap and stimuli presenta-

tion.

Figure 6: Top) Alpha rhythms recorded with open eyes vs.

close eyes; Bottom) FFT of recorded signal with open eyes

vs. close eyes.

quisition and USB communications were already im-

plemented, tested, and validated. A full stand-alone

operation was already achieved, through the connec-

tion of the dsPIC via SPI, even though signal pro-

cessing algorithms are still in the early steps of de-

velopment. Also, the batteries supply system is cur-

rently under development. The proposed SSVEP BCI

was also tested reaching 15 and 16 Hz discrimination

applying a FFT. For comparing purposes the experi-

ments were also performed with a commercial EEG

system. The results showed a similar performance.

ACKNOWLEDGEMENTS

This work has been in part supported by Fundac¸

para a Ci

encia e Tecnologia (FCT), under Grants

POSC/EEASRI/58279/2004 and PTDC/EEA-

ACR/72226/2006.

Figure 7: FFT of the SSVEP signal for a 15 Hz and 16 Hz

stimuli.

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