Brain Computer Interface for User Recognition And Smart Home Control

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 3 ISSUE 2 – FEBRUARY 2015 – ISSN: 2349 – 9303

Brain Computer Interface for User Recognition And Smart Home Control VIDYA.A Dept of Electronics & Communication Engineering Karpagam University, Coimbatore, India. vidyaannadurai@gmail.com

NANDHA KUMAR.R Dept of Electronics & Communication Engineering Karpagam University, Coimbatore, India. nandhanila69@gmail

Abstract This project discussed about a brain controlled biometric based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) 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 biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others.

Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states.

According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.

Index Terms— EEG, biometrics, brain rhythms, elicitation protocols.

1 .INTRODUCTION Electroencephalography (EEG) is a method used in measuring the electrical activity of the brain. This activity is generated by billions of nerve cells, called neurons. Each neuron is connected to thousands of other neurons. Some of the connections are excitatory while others are inhibitory. The signals from other neurons sum up in the receiving neuron. When this sum exceeds a certain potential level called a threshold, the neuron fires nerve impulse. The electrical activity of a single neuron cannot be measured with scalp EEG. However, EEG can measure the combined electrical activity of millions of neurons. The temporal resolution of EEG is very good: millisecond or even better.

In the last few years, the notion that the brain has a default or intrinsic mode of

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functioning has received increasing attention. The idea derives from observations that a consistent network of brain regions shows high levels of activity when no explicit task is performed and participants are asked simply to rest. The importance of this putative “default mode” is asserted on the basis of the substantial energy demand associated with such a resting state and of the suggestion that rest entails a finely tuned balance between metabolic demand and regionally regulated blood supply. These observations, together with the fact that the default network is more active at rest than it is in a range of explicit tasks, have led some to suggest that it reflects an absolute baseline, one that must be understood and used if we are to develop a comprehensive picture of brain functioning. Here, we examine the assumptions that are generally made in accepting the importance of the “default mode”.

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 3 ISSUE 2 – FEBRUARY 2015 – ISSN: 2349 – 9303 2.OVERALLPROJECTOUTLINE Brain signals have been investigated within field for more than a century to study brain diseases like epilepsy, spinal cord injuries. They are also used in both brain computer and brain machine interface systems with assistance, rehabilitative, and entertainment applications.

Despite the broad interest in clinical applications, the use of brain signals has been only recently investigated by the scientific community as a biometric characteristic to be used in automatic people recognition systems. However, most commonly used biometrics, such as brain signals present some peculiarities, not shared by the face, iris, and fingerprints, with reference, robustness against spoofing attacks, possibility to perform continuous identification, intrinsic liveness detection, and universality.

In this project with a minicomputer RF Transmitter and Receiver with different input and output is interfaced. In input section there is brain wave sensor, transmitter, Level analyzer unit and the data processing unit.

Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves.

A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.

When there is a mismatch of the brain wave then a buzzer sound will be indicated.

Figure 1.Block diagram 3. HARDWARE PROTOTYPE 3.1. Elicitation of Brain Responses Since the earliest applications of EEG signals, particular interest has been shown in the study of cerebral activity during a state of rest, due mainly to the simplicity of the acquisition process.

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 3 ISSUE 2 – FEBRUARY 2015 – ISSN: 2349 – 9303 Therefore, the resting state protocol, with eyes closed or open, has been widely studied for different purposes. Within this paradigm the enrolled subjects are typically seated in a comfortable chair with both arms resting, in a dimly lit or completely dark room. Generally, external sounds and noise are minimized to favor the relaxed of the subjects. Participants are asked to perform few minutes of resting state with eyes closed or eyes open, avoiding any focusing or concentration, but staying awake and alert. Brain activity during resting state without performing any task carries interesting information as 3.2. EEG SIGNAL BASED RECOGNITION contained in EEG specific patterns .Eyes closed SYSTEMS and eyes open resting conditions are usually employed in EEG research processes related to The use of brain activity as user identifier is global arousal and focal activation .Moreover, suggested by its general role in controlling the EEG patterns have shown significant differences, functioning of the whole body, the cognitive specially related to the spectral analysis, between processing, and the response to external stimuli. In rest and several cognitive tasks, and even this regard, memory mechanisms (experience), between different cognitive tasks themselves, personality correlates, and anatomo -physiological involving distinct neural systems. In order tinfer factors contribute generating individual specific about the properties of neural activation in the traits. Some promising results have been obtained involved brain regions, match logical and spatial employing different EEG acquisition protocols, cognitive operation have been considered in the involving both resting conditions with closed or development of suitable acquisition protocols. open eyes, response to specific stimuli, like visual Changes in neuronal activation patterns due to stimuli, and execution of real or imagined body specific components of mental calculation tasks movements. Since the recognition performance of can be observed from the analysis of each a biometric system in general, and of an EEG frequency band, as they seem to be related to based system in particular, depends on the proper oscillatory activity of different neural networks. In design of the acquisition protocol, on the feature this regard, different studies for baseline estimates, selection approach, and on the classification although they represent different processes related algorithm, in this Section the afore mentioned to global arousal and focal activation .Moreover, issues will be considered to compare the state of EEG patterns have shown significant differences, the art EEG based biometric systems. Databases specially related to the spectral analysis, between structures will also be taken into account. rest and several cognitive tasks, and even between different cognitive tasks themselves, involving distinct neural systems. In order to infer about the properties of neural activation in the involved brain • Delta 0.5−4Hz: Delta rhythm is a predominant oscillatory activity in EEGs recorded during the so called deep or slow wave sleep (SWS). In this regions, math, logical, and spatial cognitive stage, Delta waves usually have relatively large operations have been considered in the amplitudes (75 − 200μV) and show strong development of suitable acquisition protocols. coherence all over the scalp. In newborns, slow Changes in neuronal activation patterns due to Delta rhythms predominate. An increase in Delta specific components of mental calculation tasks EEG activity during the performance of a mental can be observed from the analysis of each task has shown to be related to an increase in frequency band, as they seem to be related to subjects’ attention to internal processing. oscillatory activity of different neural networks.

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 3 ISSUE 2 – FEBRUARY 2015 – ISSN: 2349 – 9303 • Theta 4 − 8Hz: In human scalp EEG, changes in  Motor Theta rhythm are very difficult to detect without the help of computational methods from raw EEG Software requirements: traces. If EEG power in a resting condition is  Compiler(KEIL IDE) compared with a test condition, an increased  Orcad design activity in the Theta sub band is observed, which is  Programmers(Flash Magic) known as Theta-band power synchronization. In  Languages: Embedded c particular, Theta-band power increases in response  MATLAB to memory demands, electively reflecting the successful encoding of new information.

Applications:  Bio metric applications • Alpha 8 − 14Hz: The oscillatory Alpha band  Online verified applications activity is the most dominant rhythm which  Home applications emerges in normal subjects, most pronounced in  Mobile applications the parieto-occipital region. It is manifested by a  Voting applications peak in frequency spectrum. The Alpha brain oscillations may present amplitudes large enough CONCLUSION to be clearly seen in raw EEG traces acquired in The system can be used in specific mental states several places like banks, hospitals, labs and other sophisticated automated systems, which Beta 14 − 30Hz: Phase synchrony in Beta dramatically reduce the hazard of unauthorized frequency band is enhanced for consciously entry. Evidence can be given to the security perceived stimuli [17], and detectable mainly from department if any robbery issue occurs. The the involved cortical areas, including somato system can However, most commonly used sensory, frontal, parietal and motor regions, biometrics, such as brain signals present some depending on the performed task. Specifically, peculiarities, not shared by the face, iris, and Beta activity is characteristic for the states of fingerprints, with reference, robustness against increased alertness and focused attention. spoofing attacks, possibility to perform Continuous identification.

• Gamma over 30Hz: Neuronal synchronization in In future, EEG measurement using brain the Gamma band is considered important for the computer interface is Possible to measure the transient functional integration of neural activity electrical signals of the brain for authentication across brain areas, which represent various and smart home control with high accuracy. functions involving active information processing, e.g., recognition of sensory stimuli, and the onset REFERENCES of voluntary movements. Gamma components are difficult to record by scalp electrodes and their [1] E. Basar, Brain Function and Oscillations: Integrative Brain Function.Neurophysiology and Cognitive Processes (Springer series in frequency usually does not exceed 45Hz . synergetics).Berlin, Germany: Springer-Verlag, Components up to 100Hz, or even higher, may be 1999. registered in electrocorticogram (ECoG).

Hardware requirements:  ARM lpc2148  Brain wave sensor  Alaram unit  LCD display  Switches  Gsm modem  Light

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[2] G. Dornhege, J. d. R. Mill´an, T. Hinterberger, D. McFarland, andK.-R. M´ oller, Towards Brain-Computing Interfacing.

Cambridge, MA,USA: MIT Press, 2007.

[3] J. R. Wolpaw and E. W. Wolpaw, Event-Related Brain Potentials: Methods, Theory, and Applications. Hoboken, NJ, USA: Wiley,

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[4] J. M. Carmena, “Becoming bionic,” IEEE Spectr., vol. 49, no. 3,pp. 24–29, Mar. 2012., which [5] H. H. Stassen, “Computerized recognition of persons by EEG spectral patterns,” Electroencephalogr. Clin.Neurophysiol., vol. 49, nos. 1–2,pp. 190–194, 1980.

[6] P. Campisi, D. La Rocca, and G. Scarano, “EEG for automatic personrecognition,” IEEE Comput., vol. 45, no. 7, pp. 87–89, Jul. 2012.

[7] J. Berkhout and D. O. Walter, “Temporal stability and individual differences in the human EEG: An analysis of variance of spectral values, ”IEEE Trans. Biomed. Eng., vol. 15, no. 3, pp. 165–168, Jul. 1968.

[8] H. Van Dis, M. Corner, R. Dapper, G. Hanewald, and H. Kok, “Individualdifferences in the human electroencephalogram during quietwakefulness,” Electroencephalogr.

Clin.Neurophysiol., vol. 47, no. 1,pp. 87–94, 1979.

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