Brain Actuated Interaction: an application of BCI

Rajesh Singla* Bhupender Singh* *Department of Instrumentation and Control Engineering Dr BR Ambedkar National Institute of Technology, Jalandhar-144011 rksingla1975@gmail.com, bhupendersinghbadhran@gmail.com

Abstract Brain-Computer Interfaces (BCI) is a system that allows

individual with severe neuromuscular disabilities to communicate or perform ordinary tasks exclusively via brain waves.BCI shows promise in allowing these individuals to interact with a computer using EEG. Brain Computer Interface mainly facilitates or formed a communication channel between human brain and machine or device application. A simple model of bed movement i.e. bed upward and downward movement through eye blinks signal of the patients is proposed and its effectiveness is examined online and offline

Keywords: BCI, eye blinks, EEG, Mat lab, C language.

I. INTRODUCTION

BCI (Brain Computer Interface) are defined as the science and technology of devices and systems responding to neural processes in the brain that generate motor movements and to cognitive processes (e.g., memory) that modify the motor movements. For the development of “non-invasive” (multi electrode arrays emplaced on the surface of the skull to record changes in EEG state) BCI we generally use the EEG (electroencephalogram). EEG signal is composed of electrical rhythms and transient discharges. Every person has a different wave shape, amplitude, frequency pattern of the EEG signal. Certain features of the α rhythm and β rhythm can be detected and used to generate a control signal to operate some device application. BCI is capable of assisting in communicating and control needs (such as environmental control and Assistive Technology) has opened new avenues for patients affected by severe movement disorders. In this paper, we have designed a BCI model that is based on the EEG based α rhythm, β rhythm amplitude basis like eye blink signals used to control the motion of the bed of severely paralyzed patients for medicinal and normal activity like sitting on bed while taking food etc. with the help of a stepper motor installed for controlling the motion of the bed in both directions i.e. Upward and downward.

II. SYSTEM ARCHITECTURE

Fig.1. Complete Experimental setup of a BCI system

Fig.1. shows the complete experimental setup of a BCI system, designed to control environment (in this case motion of the bed).

The system is consists of three modules: Data acquisition module, Data processing module and kurtosis coefficient and third is hardware module. The hardware set up module of the system consists of

• Desktop PC interfaced to EEG Hardware via USB port.

• Microcontroller 8051 based circuit to drive stepper motor using stepper motor module for driving the bed up and down movement.

The data acquisition consists of Head box and adaptor box and data processing module consists of analysis and feature extraction of eye blink signals.

A. Data Acquisition module The device used for study is RMS Super Spec 32. It can record 32 channels of EEG data through electrodes placed according to the international 10-20 system of measurement. Each electrode site gas a letter to identify the lobe i.e. F, T, C, P, and O stands for frontal, temporal, central, parietal and

2009 International Conference on Artificial Intelligence and Computational Intelligence

978-0-7695-3816-7/09 $26.00 © 2009 IEEE

DOI 10.1109/AICI.2009.334

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occipital. The even numbers refer to right hemisphere and odd number refers to left hemisphere. The Head box is used for connecting electrode from human scalp to the hardware part. The voltage generated by the brain cells and kind of picked up by EEG is extremely small (between 1-100 microvolt) peak to peak at low frequencies (0.5-100 Hz) at the cranial surface which is amplified and sent to the adaptor box for signal conditioning is then interfaced with PC through connecting cables for analysis. The view of the electrode positions as seen from the side and top is as shown in the following figure.

Fig.2. International system of electrode placement The EEG data acquired through EEG machine channel (FP1-F3), (FP2-F4) the channel being more sensitive to eye events. While acquiring the data it is fed to a band pass filter implemented using low pass filter (4th order FIR filter with cut off 70Hz) and high pass filter (4th order FIR filter with cut off 1Hz) to limit the EEG signal bandwidth (1 to 70 Hz). A 50 Hz notch filter is used to remove power line interference

B. Data processing module and kurtosis coefficient The acquired signal processed in this module using three-filter method i.e. using hanning window, square root polynomial smoothing filter and moving average filter. Using the hanning window filtered data in Mat lab 7.0 software we find that the amplitude of the eye blink signal is decreasing and the FFT(fast fourier transform) pattern of the 2500 samples also shows decreasing amplitude. In moving average filter, the amplitude of the eye blink signal is amplified and the FFT pattern of the 2500 samples shows different pattern as compared with the FFT pattern of sample data. Using the square root polynomial smoothing filter the eye blink signal is of same amplitude and the FFT pattern is almost similar with FFT pattern of sample data. The feature of the eye blink is then processed with finding the kurtosis coefficient of the filtered samples and we found that the value of kurtosis coefficient is high when here is eye blink signal otherwise not. It removes the spurious spikes that could come through switching on/off lights etc. These

spikes will not be detected by the hardware designed for our BCI, only kurtosis value of 3.0 and greater than that in either of the channel, FP1-F3 and FP2-F4 will be considered only as an eye blink signals that will operate our hardware.

A. C. Hardware module The hardware module consists of microcontroller 8051 circuit board based stepper motor control circuit for the bed upward and downward movement. This hardware is interfaced with PC via parallel port is simulated using C programming in windows 98 using the zone value. For generalizing the hardware for different persons, we have formed an equation: Eye blink=50+ zone value and kurtosis coefficient>3.0 We have analyzed that out of 25 patients, 15-17 patients eye blink signals lie in the minimum amplitude equals to or greater than 50 micro volt and adding the zone value which could be different for different persons will be enter with kurtosis coefficient value >3.0 for the device application of bed movement i.e. upward ,downward.

III. RESULTS We have studied 25 patients’ data, age ranging between 17 yrs-25 yrs and analyzed that the BCI system model developed could be useful and offer aid or assist patients who could not sit on bed while taking medicine and food. With just eye blinking signal, we could lift the bed in upward direction as and when desired .

IV. CONCLUSIONS AND FUTURE SCOPE The quality of life could be improved using the BCI

technology especially those are suffering from locked in syndrome, paralysis. We detected the changes in EEG patterns due to eye blinks. We have used eye blink signals for the control of bed motion to facilitate the medicinal and food delivery to the patients. It can be further used to design a wheel chair movement using EEG signals that can enable the movement of patients who cannot walk to interact with environment.

V. REFERENCES [1] Gerwin Schalk, Dennis j. Mcfarland, Thilo Hinterberger, Niels Birbaumer and Jonathan R. Wolpaw “BCI2000- A General purpose Brain Computer Interface(BCI) System” IEEE transactions on Biomedical Engineering, Vol.51,No.6 , pp. 1034-1043 [2] Lucas c parra, Clay D. Spence, Adam D. Gerson and Paul Sadja- “Response Error correction-a Demonstration of improved Human Machine performance using Real time EEG monitoring”, IEEE transactions on Neural systems and Rehabilitation Engineering, Vol11, No.2, June 2003

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[3] J.R. Wolpaw, N. birbauner, D. J. McFarland, G. Pfurtscheller, T.M. Vaughan, “Brain-computer interfaces for Communication and Control” Clinical Neurophysiology 113 pp. 767-791, 2002

[4] B. H. Jansen, A. Allam, P. Kota, K. Lachance, A. Osho, K. Sun” An Exploratory study of Factors Affectin single Trial P300 detection”, IEEE transactions on Biomedical Engineering, Vol. 51, pp. 975-978, 2004 .

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