WIDEBAND WIRELESS LINK FOR BCI CONTROL

100 kHz – 8/16 Channel for High Resolution EEG

C. P. Figueiredo, N. Dias, J. H. Correia and P. M. Mendes

DEI, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal

Keywords: Wireless, Wideband, Biotelemetry, BCI.

Abstract: This work presents a solution to obtain a wireless biopotential acquisition system with high data rate.

Wireless systems are currently emerging with the possibility of being used for monitorization of several

physiological parameters. However, most of the solutions are based on standard wireless systems. Besides

the wireless throughput limitation, those systems are also limited in their software solutions and data

acquisition capabilities. A trade-off solution between commercial of-the-shelf and custom design was

explored by interfacing a MICAz with external instrumentation, while maximizing the rate of

communication. The wireless system is being used for BCI control, operates at 2.4 GHz (Zigbee compliant),

with a data rate of 250 Kbps for wireless link, and up to 1 Mbps for serial communication. Signals down to

about 23 µV can be detected, and 8/16 single-ended channels are provided with 100 kHz sampling rate.

1 INTRODUCTION

The use of wireless sensor networks to assist in

biomedical applications is being pursued by many

researchers and will become available as soon as the

required sensors and network solutions are made

available (Schwiebert et al. 2001). However, before

turning it into reality there are a few challenges to

overcome.

The system must have low power consumption

and the network nodes must operate under limited

computation. Also, since these systems must operate

in the human body, they do have some material

constraints. Moreover, continuous operation is

required, with high robustness and fault tolerance

capability (Schwiebert et al.). Recently, the

widespread availability of low power sensor devices

with physiological monitoring ability is pushing

researchers to include them in smart suits that can be

used to monitor biological signals in different

situations. Their application ranges from

monitorization embedded in space suits (Simons et

al. 2004), to monitorization during jogging activity

(Marculescu et al. 2003).

However, the requirements of sending only

cardiac or respiratory rhythm data are not enough

anymore for modern monitorizing systems. Today,

in many monitorization devices, it is necessary to

route all the acquired data for storage and further

processing. The development trend on physiological

data acquisition is demanding more and more

available bandwidth. As an example, the Brain-

Computer Interface (BCI) operation, demands

several electroencephalogram (EEG) channels with

large bandwidth, which leads to large information

amounts handling for feature and artefact extraction.

BCI is trusted to be a very useful tool for

impaired people, both in invasive and non-invasive

way. Although subjects using invasive approaches

usually show evidence of better device control than

non-invasive method users, it is barely preferred due

to the high risk involved in its research and practical

implementation. BCI has the potential to enable

people to control a device with their brain signals. In

several studies, different BCI approaches have been

tested that enable impaired people to communicate

and control specific devices (Wolpaw et al. 2000).

This paper will start with the presentation of the

hardware platform that was used in this work.

Afterwards, the requirements for physiological data

acquisition for BCI are introduced, as well as the

required hardware modifications. After highlighting

the limitations of the available platform, a solution is

proposed to overcome them and the obtained results

are presented.

202

P. Figueiredo C., Dias N., H. Correia J. and M. Mendes P. (2008).

WIDEBAND WIRELESS LINK FOR BCI CONTROL - 100 kHz – 8/16 Channel for High Resolution EEG.

In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 202-205

DOI: 10.5220/0001054102020205

 SciTePress

2 WIRELESS BCI

The BCI system is mainly made by four

software/hardware modules: (1) EEG signal

acquisition, (2) features extraction, (3) translation

algorithm, and (4) actuator and feedback system.

The system that is being used for BCI records

EEG data using a Labview platform, which receives

data from a BrainProducts® Quickamp through a

socket connection. The Data are digitized at 250 Hz

and passed through a 6th order (48 dB per octave)

band-pass Butterworth filter of 1-50Hz. This

platform extracts the subject specific features,

provides feedback and graphical interface to subject.

There are many challenges to be solved before

BCI systems can show their full potential. A

wideband low-power wireless acquisition platform is

of most relevance for BCI operation. Fig. 1 shows a

possible solution for a wireless BCI system. The

presented solution uses a ZigBee link to transmit the

EEG signals.

Figure 1: Wireless BCI system under development, (red

modules are the target of this work).

2.1 Wideband BCI

A BCI is usually based on the ongoing rhythms of

EEG signals. Those rhythms are the so called delta

(0.5-4 Hz), theta (4-7.5 Hz), alpha (7.5-13 Hz), beta

(15-20 Hz) and gamma waves (20-42 Hz). A

bandwidth of 100 Hz would suffice for the

acquisition of these potentials. However, during BCI

operation, the reactivity of a rhythm to a mental task

is usually identified in power spectra that are

calculated using the FFT algorithm.

A good BCI control, from the user point of view,

is a system with real time feedback. Any action will

happen as soon as the user thinks about it. To obtain

this, the system should collect as much data as

possible in the shortest period of time, limited by the

spectral resolution required. In this way, for a

specific time window, the higher the number of

sampled points, the higher is the spectral content in

the calculated spectra. Once 1000 samples per

second are recorded, the FFT of a 1 s time window

achieves 1 Hz resolution in the frequency domain,

together with a spectral content up to 1000 Hz. This

sampling rate able the acquisition system to track

surface EMG (2-500 Hz bandwidth) signals in order

to detect task related muscle activity (Prutchi and

Norris, 2004), since a BCI system is supposed to

operate in the absence of muscle activity.

BCI systems can be greatly improved if more

complex and faster algorithms can be used but this

would require routing all the available data to a

powerful computing system. The acquisition

systems are, due to power saving requirements, very

limited to perform this task.

2.2 Wireless Platform

One key element required to implement a wireless

BCI system is the wireless platform. There are many

solutions to implement it but the MICAz is a very

popular one. This platform allows easy

implementation of a wireless sensor network formed

by individual wireless nodes. Fig. 2 a) shows the

node core available for system development. This

core includes the microcontroller, the ADC with 10

bits resolution, the ZigBee wireless interface, and

the serial interface.

The microcontroller is the ATMEL Atmega128,

running the TinyOS operating system. The micro

provides access to the ADC, allowing data

acquisition at 76.9K samples/s, with a resolution of

10 bits, from a maximum of 7 differential or

8 single-ended channels. The acquired data can then

be routed trough the wireless ZigBee link, which

allows a throughput of 250 kbps. The other option is

to route the data through the serial interface. It uses a

RS-232 link with the maximum data rate of

115.2 kbps.

2.3 Data Acquisition

The system can use three different node types, as

shown in Fig. 2. The first is the standard wireless

platform (Fig. 2-a)), which has a microprocessor

with a built in analogue-to-digital converter (ADC).

This device allows a maximum data transfer rate of

115.2 kbps. This limitation comes from the serial

port connection (RS232), where the PC USART is

limited to this speed.

When more resolution is required, it is necessary to

use an external ADC. This is required for high

resolution EEG and ECG, e.g., to enable the

WIDEBAND WIRELESS LINK FOR BCI CONTROL - 100 kHz – 8/16 Channel for High Resolution EEG

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Microc on tr oller

ZigBee

10-bit

ADC

Serial Interface

μC

Tri-s tate RS232

Adapter

Microc on tr oller

ZigBee

10-bit

ADC

Serial Interface

μC

Tri-s tate RS232

Adapter

Microc ontr oller

ZigBee

10-bit

ADC

Serial Interface

μC

Tri-s tate RS232

Adapter

16/24-bit

ADC

16/24-bit

ADC

Bus I

C/SPI

Control

Bus I

C/SPI

INT0

b) c)

Microc on tr oller

ZigBee

10-bit

ADC

Serial Interface

μC

Tri-s tate RS232

Adapter

Microc on tr oller

ZigBee

10-bit

ADC

Serial Interface

μC

Tri-s tate RS232

Adapter

Microc ontr oller

ZigBee

10-bit

ADC

Serial Interface

μC

Tri-s tate RS232

Adapter

16/24-bit

ADC

16/24-bit

ADC

Bus I

C/SPI

Control

Bus I

C/SPI

INT0

b) c)

Figure 2: Wireless nodes involved in the physiological data acquisition. a) with internal ADC; b) with external ADC driven

by software interrupt; c) with external ADC drive by hardware interrupt.

recording of EEG signals for BCI. For the second

node type it the AD7714 ADC was selected for

external operation, connected to the Mote I2C bus

using a serial link with the SPI port available in the

ADC. This solution allows 16 or 24 bits of

resolution, with a maximum sampling frequency of

1028 samples/s. However, the data acquisition from

the external ADC requires the use of commands

from the operating system, the TinyOS. Due to

operating system timings, the maximum sampling

speed is 4000 samples/s. In this way, it was

necessary to implement the third solution, which is a

modified version of solution two. Instead of

implementing all the external ADC control by

software, the time critical tasks were implemented

using hardware interruptions. With the third node

type it is possible to sample the analogue channels at

8K samples/s, with 16 or 24 bits of resolution. The

system limitation is on channel switching, made by

software, which takes 3 ADC conversion periods to

change between channels, due to resettling of the

sigma delta modulator and digital filter.

To overcome this switching limitation, the use of the

ADS8345 is proposed. It is an 8-channel, 16-bit,

sampling Analog-to-Digital (A/D) converter with a

synchronous serial interface. This ADC allows data

acquisition from 8 channels at 100 kHz. The channel

switching time is only 500 ns, and typical power

dissipation is 8mW at a 100kHz throughput rate.

2.4 Data Routing

After solving the problem of data acquisition, it is

required to send the EEG data to the host station.

This requires a wireless link and a RS-232 link. The

first hop will be the wireless link, a ZigBee link with

a data rate of 250 kbps. If we consider sampling at

1 kHz, 8 channels, and 16 bits per sample, we have

an overall data rate requirement of 128 kbps. This

means that the wireless link will be enough to

support it.

After receiving the data, the base station needs to

route it to the processing unit, a PC. This is done

using the RS-232 interface. This means we have a

bottleneck in the system since the RS-232 will allow

a maximum data transfer rate of 115.2 kbps.

However, the microcontroller Atmega128 allows the

configuration of his RS-232 port to operate at

921.6 kbps, and it is also possible to use a RS-232 to

USB interface, having the possibility to achieve a

maximum data rate of 1 Mbps. Considering this, the

bottleneck will be on the ZigBee link, but we have

then a bandwidth of 250 kbps.

3 PLATFORM PERFORMANCE

3.1 Data Acquisition

In the second solution (fig. 3 b)), the bottleneck is

the software driven interruption. To test the

maximum sampling frequency, a timer was

implemented and each sample was sent together

with its time stamp. We have found that the smallest

sampling time, Ts, is about 1s (with a 2.5 MHz

crystal). Thus, it is not a timer problem, since when

the Ts was reduced below 1ms, the system started to

fail on the delivering of some data points. In this

way, the system does not allow to obtain all the

signals at maximum sample rate. The third solution

(fig. 2-c)), with the hardware driven interruption,

was able to read 16 bit samples at a maximum data

rate of 1028 samples/s. This makes an overall bit

rate of 131584 bps, which is not a problem for the

ZigBee link. However, the data was not reaching the

host station, despite the serial configuration of

almost 1 Mbps.

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3.2 Serial Link

To test the maximum speed possible with this link, it

was used the solution of Fig. 2-c) and the INT0 line

was used to trigger the data transmission. Instead of

sending data from the ADC, the data was generated

and transmitted through the system. To detect the

wrong samples, each sample was generated from the

previous, by adding a fixed amount. In this way,

each time a sample was missed, the difference

between two consecutive samples allowed the

detection of a missing sample. The results are shown

in Fig. 3 and Fig. 4. It shows the effect of increasing

the sample rate, with the nonzero values

representing instants where data packets (of 10

samples) were lost.

Figure 3: Error plot when data is sampled at 3.95 kHz.

The previous figure shows the received samples

when the interrupt signal was set to 3.95 kHz. This

corresponds to a total bit rate of 113.76 bps. From

the plot, we can see that some data values are

corrupted but the system is able to proceed with the

transmission of correct values.

Figure 4: Error plot when data is sampled at 4.3 kHz.

In the previous plot, the overall data rate is

123.84 kHz and we can see that the system is not

able anymore to recover and transmit correct values.

The microcontroller associated with this block,

which controls the programming steps of the main

microcontroller, also allows 1 Mbps and was not the

problem. The bottleneck resides on the voltage

adapter, responsible for the conversion between PC

voltage levels to the micro voltage levels.

This is the MAX3223, which guarantees only

120 kbps of throughput. Our proposal is to use the

MAX3223E, fully compatible with the available

board, which guarantees a 250 kbps throughput. If

required, another adapter for achieving higher data

rate can be used. However, the ZigBee link will limit

data rate to 250 kbps.

4 CONCLUSIONS

A solution to obtain a high date rate wireless link for

physiological data acquisition was presented,

operating at 2.4 GHz, with a minimum detectable

signal of about 23 µV, and power consumption of

15 mW. The solution is based on a MICAz mote and

is used for external ADC management as well as

transmitting the acquired data via wireless link to

another mote connected to a computer’s serial port.

The written TinyOS components detect the end of

conversion by the ADC via external interrupt,

avoiding sampling jitter, and perform read and write

operations on its registers through the SPI interface.

This solution was tested for BCI control

applications. With the proposed solution it is

possible to acquire data from 8/16 channels at 100

kHz sampling frequency, with a data rate limit of

250 kbps.

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