SSRG International Journal of Electronics and Communication Engineering - (2'ICEIS 2017) - Special Issue April 2017

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1 Eeg Based Brain Computer Interface For Communications And Control J.Abinaya,#1 R.JerlinEmiliya #2, #1,PG students [Communication system], Dept.of ECE, As-salam engineering and technology, Aduthurai, Tamilnadu, India. #2 Head Of The Department [O.G], Dept.of ECE, As-salam engineering and technology, Aduthurai,Tamilnadu India Abstract To increase the performance of a brain computer interface and brain machine interface system, we propose some methods and algorithms for electroencephalograph (EEG) signal analysis. The recorded EEG signal is transmitted to the computer and the brain wave sensor via a bluetooth.to obtain effective commands from brain, the recorded EEG signal is processed by a front filter, denoise filter, feature extraction, and classification, while the personal computer software are driven by EEGbased commands. BCIs are systems that can bypass conventional channels to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a wheelchair can be controlled. Index Terms Brain computer interface (BCI), brain machine interface (BMI), electroencephalograph (EEG) signal. I. INTRODUCTION ELECTROENCEPHALOGRAPHS (EEGs) play a crucial role in rehabilitation treatment due to their noninvasiveness, real-time performance, good data acquisition, and low cost. According to the diverse signal forms, the brain computer interface (BCI) can be divided into spontaneous potentials BCI and evoked potentials BCI. In regard to BCI systems based on evoked potentials, it has been widely used in various areas since the rise of BCIs.These features are then translated into commands to operate a device. The Brain Controlled Wheelchair was designed to provide some motion capability to locked-in people. Motion control commands (Forward, Left, Right, Forward to the Right, Forward to the left and Stop) are classified by simple rule. II.EXISTING SYSTEM A reliable system that uses hand gestures to control a wheelchair is presented. The control actions are generated by gestures of a bare or gloved hand. The gestures are recognized and identified in a scale, position, orientation and skin color independent manner. Yet the position and orientation of the hand gesture for motion control are used for speed and steering angle control. The interface works equally well with either of the hands and can be interchanged anytime without any changes to the system. The hand gestures are organized in a hierarchy taking into account the ergonomics, reliability of identification and the mode of operation. The modes of operation are; the manual mode and the map mode. The results presented show the behavior of the wheelchair in response to manual and map mode hand gesture commands. The system has been put to test by persons of different hand shapes and proved to be extremely reliable. ISSN : Page 41

2 III.PROPOSED SYSTEM Any biometric authentication system consists of four primary modules; data acquisition, pre-processing, feature extraction and classifier module. A. Data Acquisition All EOG signals used in this letter were recorded using Neurosky Mindwave headset. The headset consists of an ear-clip and a sensor arm. This headset is actually used for recording EEG signals; however, it can be used to measure EOG signals as the arm sensor is resting on the forehead above the left eye. The reference electrode is on the ear clip. The sensor of Neurosky headset is made of dry electrode which does not require any skin preparation or conductive pastes. So, it takes less than 10 seconds to wear the headset and start recording signals. Also, the headset is wireless which makes it suitable for practical implementation of biometric authentication systems. B. Pre-Processing Stage The pre-processing stage involved EOG isolation from EEG signals and eye blinks extraction from isolated EOG signal. The most important techniques for EOG isolation from EEG are Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD). ICA works best when it is required to separate mixed signals (EEGEOG) recorded from many sources (EEG channels) G4) were extracted based on time delineation of the eye blinking waveform D. Classification Stage For a proper solution of the classification problem, it is important to use a suitable classifier fitting the scattering distributions generated by the different classes to distinguish among. Different classifiers like Vector Quantization (VQ), Gaussian Mixture Modeling (GMM), Discriminant Analysis (DA) based on linear or quadratic boundaries, and Support Vector Machine (SVM) were tested for the proposed system. DA based on linear. IV. BRAIN COMPUTER INTERFACE BrainComputer Interface (BCI) [1], technology is a new and fast evolving field that measures the specific features of brain activity and translates them into device control signals. Signal is acquired using the electrodes on the scalp. These signals are weak hence amplified and are converted into digital form. Then features are extracted from amplified and digitized version of EEG signals in the signal processing stage.in this stage useful EEG data is separated from noise. C. Feature Extraction Stage The goal of feature extraction is to find a transformation that converts the original signal into a relatively low dimensional feature space that is able to preserve the discriminative information of each subject. Four groups of features (G1, G2, G3, and ISSN : Page 42

3 Fig: Brain wave sensor-transmitter Fig: Brain wave sensor-receiver Fig. Brain Computer Interface Fig: MindWave Headsets The family of NeuroSky MindWave headsets is designed to be used by developers to get to market quickly with complete EEG-monitoring products. The MindWave Mobile headset turns your computer into a brain activity monitor. The headset safely measures brainwave signals and monitors the attention levels of individuals as they interact with a variety of different apps. This headset is useful for OEMs and developers building apps for health and wellness, education and entertainment. V.BRAINWAVE SENSOR Brainwave sensor act as an EEG Headset ISSN : Page 43

4 VI.RESULTS Fig: Right operation Fig:Reverse operation Fig: Forward operation Fig: Left operation Fig: Device control VI CONCLUSION The brain-controlled wheelchair have a great deal of attention because they can help bring mobility back to people with devastating neuromuscular disorders and thus improve their quality of life. A comprehensive of the brain controlled wheelchair, uses a brain wave sensor which can collect EEG based brain signals of different frequency and amplitude and it will convert these signals into packets and ISSN : Page 44

5 transmit through Wireless medium in to the level splitter section to check the attention level. Level splitter section (LSS) analyses the level and gives the brain movement for the person who is sitting in the wheel chair. The brain-controlled wheeelchair can be applied in practice, including finding ways to improve the performance of BCI systems, to improve the overall driving performance given the constraints of the BCI system. 4. P. Campisi and D. La Rocca, Brain waves for automatic biometricbaseduser recognition, IEEE Trans. Inf. Forensics Secur., vol. 9, no.5, pp , May Sarah N. Abdulkader, Ayman Atia, Mostafa-Sami M.Mostafa, Brain computer interfacing: Applications and challenges, in Egyptian Informatics Journal (2015) 16, ACKNOWLEDGMENT I would like to thank our chairman Mr.M.J.A.Jamal, Managing Director Mr.K.Karl Mark principal Dr.M.Ravichandran, Ph.D for encouraging and providingnecessary facilities towards the growth carrying this work.the authors knowledge with the help of Ms. R.JerlinEmiliya,M.E., As-salam college of engineering and technology,aduthurai, Tamilnadu in assisting me towards implemented the project work. REFERENCES 1. Abdel Ilah N. Alshbatat, Peter J. Vial, Prashan Premaratne, Le C. Tran, EEG-based Brain-computer Interface for Automating Home Appliances, JOURNAL OF COMPUTERS, VOL. 9, NO. 9, SEPTEMBER Brent J. Lance,, Scott E. Kerick, Anthony J. Ries, Kelvin S. Oie, and Kaleb McDowell, Brain Computer InterfaceTechnologies in the Coming Decades, in Proceedings of the IEEE, Vol. 100, May 13th, Ms Priyanka D. Girase1, Prof. M. P. Deshmukh, A Review of Brain Computer Interface, International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016) ISSN : Page 45