A Literature Survey on Wireless sensor network for Brain Computer ...

Vaidehi Baporikar et al. / International Journal of Engineering Science and Technology (IJEST)

A Literature Survey on Wireless sensor network for Brain Computer Interface using ATMEGA128RFA1 Vaidehi Baporikar #1, Dr.N.G.Bawane *2 #

3 rd SEM M.E (ESC), G.H.R.C.E, Nagpur, India * Professor, University, G.H.R.C.E., Nagpur, India 1

[email protected]

ABSTRACT Recent advances in computer hardware and signal processing have made possible the use of EEG signals or “brain waves” for communication between humans and Computers. The electrical nature of the human nervous system has been recognized for more than a century. It is well known that the variation of the surface potential distribution on the scalp reflects functional activities emerging from the underlying brain .This surface potential variation can be recorded by affixing an array of electrodes to the scalp, and measuring the voltage between pairs of these electrodes, which are then filtered, amplified, and recorded. The resulting data is called the EEG. Electrodes conduct voltage potentials as microvolt level signals, and carry them into amplifiers that magnify the signals approximately ten thousand times. The use of this technology depends strongly on the electrodes positioning and the electrodes contact. In this paper Wireless sensor network can also be employed for EEG signal Acquisition. This paper aims to develop a wireless sensor node for EEG data acquisition and build a real-time wireless EEG acquisition system. Key Words: - Wireless sensor Network, EEG signal, BCI. I

INTRODUCTION

The initial part of the paper is devoted to EEG data acquisition and its preprocessing using filters to remove its undesirable frequency components. The EEG consists of a set of multi-channel signals. The pattern of changes in signals reflects large-scale brain activities. In addition the EEG also reflects activation of the

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head musculature, eye movements, interference from nearby electric devices, and changing conductivity in the electrodes due to the movements of the subject or physicochemical reactions at the electrode sites. All of these activities that are not directly related to the current cognitive processing of the subject are collectively referred to as background activities below. [2] In this paper Wireless sensor network designed for EEG signal Acquisition. This paper aims to develop a wireless sensor node for EEG data acquisition and build a real-time wireless EEG acquisition system. The common structure of a Brain Computer Interface is the following (Fig 1): 1) Signal Acquisition: the EEG signals are obtained from the brain through invasive or noninvasive methods (for example, electrodes). After, the signal is amplified and sampled. 2) Signal Pre-Processing: once the signals are acquired, it is necessary to clean them. 3) Signal Classification: once the signals are cleaned, they will be processed and classified to find out which kind of mental task the subject is performing. 4) Computer Interaction: once the signals are classified, they will be used by an appropriate algorithm for the development of a certain application.

NCICT Special Issue Feb 2011

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In our paper, we will use the 10-20 System of Electrode Placement, which is based on the relationship between the location of an electrode and the underlying area of cerebral cortex (the "10" and "20" refer to the 10% or 20% inter electrode distance).

Figure 1. BCI common structure.

This paper focuses on two phases of BCI are Signal Acquisition; Signal Pre-Processing and transmitting to remote system. EEG systems currently used in medical institutions are restricted in their application due to several physical limitations. One such limitation involves the signal artifacts created by movement of wires; even small movements of wires within the generated magnetic field causes artifacts of considerable magnitude. Instead of using labor-intensive, on-site EEG data acquisition, low-power EEG Sensor Networks (ESN), consisting of mobile, low-cost EEG sensors that are attached to the patients’ bodies, if deployed in nursing homes or hospitals, will have the potential to significantly improve the EEG portability and timeliness[1] . In an ESN, patient’s EEG signal (analog format) could be automatically collected and processed (such as using Analog-to-Digital conversion) by a small EEG sensor, and then be wirelessly sent to a remote EEG system for analysis purpose (such as using data classification to find out anomaly). In this paper we are trying to design a low-power EEG sensor network, which includes EEG sensor board assembly and wireless communication.

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Figure 2- The 10-20 System of Electrode Placement

Each site has a letter (to identify the lobe) and a number or another letter to identify the hemisphere location. The letters F, T, C, P, and O stand for Frontal, Temporal, Central, Parietal and Occipital. (Note that there is no "central lobe", but this is just used for identification purposes.) Even numbers (2, 4, 6, and 8) refer to the right hemisphere and odd numbers (1, 3, 5, and 7) refer to the left hemisphere. The z refers to an electrode placed on the midline. Nasion: point between the forehead and nose. Inion: Bump at back of skull The silver/silver chloride (Ag/AgCl) electrodes have been the main choice of physicians for a long time to monitor such signals due to their low cost, relatively low skin hypersensitivity, good stability and signal reproducibility. Hence we are using Ag/Agcl electrode in this paper.

SIGNAL ACQUISITIONS

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Figure 3: EEG Sensor Node

EEG SENSOR NODE ARCHITECTURE In order to study normal person’s EEG changes in daily life without the effects on mentality during measuring, a senseless EEG collecting scheme is necessary to acquire pure EEG signals as the traditional one may not be effective. Thus, our aim is to development of low-cost and portable EEG sensor node is necessary for EEG monitoring. This study aims to develop a Wireless sensor node for EEG data acquisition and build a real-time EEG information system. Figure 3 shows Sensor node for EEG data acquisition III

SIGNAL PRE-PROCESSING

Since the scalp EEG signal is very weak, typically with an amplitude in range of 10~100uV, thereby requiring conditioning prior to any signal processing.[4] Furthermore, the

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human skin typically provides source impedance on the order of 1~5Mohm. To acquire the signal effectively, the amplifier must match or have greater input impedance than the source impedance. Differential amplifier Each electrode is connected to one input of a differential amplifier (one amplifier per pair of electrodes); a common system reference electrode is connected to the other input of each differential amplifier. These amplifiers amplify the voltage between the active electrode and the reference (typically 1,000–100,000 times, or 60–100 dB of voltage gain). Filters The digital EEG signal is stored electronically and can be filtered for display. Typical settings for the high-pass filter and a low-pass filter are 0.5-1 Hz and 35–70 Hz, respectively.[8] The high-pass filter typically filters out slow artifact, such as electro galvanic signals and movement artifact, whereas the low-pass filter filters out high-frequency artifacts, such as electromyography signals. An additional notch filter is typically used to remove artifact caused by electrical power lines.


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Figure 4 structures of amplifiers and filters

Micro-controller is suitable for creating “Wireless Sensor Network for Brain Computer Interface”. REFERENCES [1] Haifeng Chen and Jungtae Lee: “The Implementation of a Wireless Electroencephalogram Information System using WLAN” IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.2, February 2008. [2] Syed M. Saddique and Laraib Hassan Siddiqui “EEG Based Brain Computer Interface” journal of software, vol. 4, no. 6, august 2009. [3 ] Fei Hu , Qi Hao , Meik an g Qiu , Yao Wu :” Lo wpower Electroencephalography Sensing Data RF Transmission: Hardware Architecture and Test”

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SIGNAL TRANSMISSION

As mentioned earlier sensor node contains A/D converter, Flash memory, Micro-processor core, and wireless trans-receiver this all requirements are fulfilled by Micro-controller. A the ATmega128RFA1 is a low-power CMOS 8 bit microcontroller based on the AVR enhanced RISC architecture combined with a high data rate transceiver for the 2.4 GHz ISM band. It is derived from the ATmega1281 microcontroller and the AT86RF231 radio transceiver. ATMEGA128RFA1. Figure 5- Sensor Network

[4] “ Sensors & Transducers” Volume 94 Issue 7 July 2008 published by Sensors & Transducers Journal (ISSN 1726-5479) international journal published by International Frequency Sensor Association (IFSA).

[5] P. S. Pandian, K. P. Safeer, Pragati Gupta, D. T. Shakunthala, B. S. Sundersheshu and V. C. Padaki “Wireless Sensor Network for Wearable Physiological Monitoring” JOURNAL OF NETWORKS, VOL. 3, NO. 5, MAY 2008

[6] Vladimir Hinic, Emil M. Petriu, Thomas E. Whalen “Human-Computer Symbiotic Cooperation Robot-Sensor Networks” Instrumentation and Measurement Technology Conference – IMTC 2007 Warsaw, Poland, May 1-3, 2007 [7] Datasheet of ATmega128RFA1. [8] A.Avila, R.Santoyo “Hardware/software implementation of the EEG signal”6th International Caribbean Conference on Devices, Circuits and Systems, Mexico, Apr. 26-28, 2006

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CONCLUSION This papers concludes that ATMEGA128RFA1

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