fbpx

Robot Interaction Using Brain Waves

Download Full-Text PDF Cite this Publication

Text Only Version

Robot Interaction Using Brain Waves

Robot Interaction Using Brain Waves

Anchit Srivastava, Jaspreet Singh, Gurpreet Singh, Sachin Thakur

Department of Electronics Engineering KCCEMSR, Thane,India

anchit.srivastava@ymail.com

Abstract-Electroencephalography (EEG) is the record- ing of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain.Neural signals are eve- rywhere just like mobile phones. Wepropose to use the neural signals to control a machine using for hands free, silent and effortless human-machine interaction. Until recently, devices for detecting neural signals have been costly, bulky and fragile. By the enhancement in the technology with the cheaper EEG sensors like Neurosky headset implementing low cost brain machine control is possible. The Neurosky headset safely measures brain- wave signals and monitors the attention and relaxation level of students as they interact with math, memory, and pattern recognition applications.

Fig 1 Basic mechanism of Brain-Machine interface

II. BRAIN MACHINE INTERFACE

I. INTRODUCTION

Traditionally robots have been controlled by

Like mobile phones, neural signals are ever present in our everyday lives. Given the recent avail- ability of low-cost wireless electroencephalography (EEG) headsets programmable mobile phones ca- pable of running sophisticated machine learning algorithms, we can now interface neural signals to phones to deliver new mobile computing pa-

hand held remote controls. These remotes have limi- tations such as less battery backup, are bulky and cannot be used by the physically handicapped. All these demand for an alternate form of control system. Hence we put forward the concept of brain machine interface in providing hassle free environment for controlling robots.

radigmsusers on-the-go can simply way through all of their mobile

think their applications.

Research grade EEG headsets are expensive

(e.g. tens of thousands of dollars ) but offer a much

We propose the use of EEG device interfaced with an arduino board to wirelessly control a robot. The EEG device maps the electrical activity in brain and sends

more robust signal then the cheaper variants a result

there is a significant amount of noise in the data of cheaper headsets, requiring more sophisticated signal

these signals to the microcontroller

which in turn

processing and machine learning techniques to classi-

usesthese signals to control the robotsmovement.

fy neural events. Here we use a low cost EEG device

from NeuroSky. However the cheaper headsets pro- vide an encrypted wireless interface between the headset and the computer allowing for mobility but complicating the design of a clean brain machine interface. For robot control we will use eye blink, concentration and meditation levels of the person. These values are acquired using the EEG headset and are given to the RF Dongle. This dongle is connected to the arduino board and RF communication is estab- lished via USART. The parameters send by EEG headset and are processed accordingly.

Fig.2 Flowchart

  1. EEG HEADSET

    Different brain states are the result of different patterns of neural interaction. These patterns lead to waves characterized by different amplitudes and fre- quencies. As examples, brainwaves between 12 and

    Fig 3. Electro Encephalographic Headset (EEG)

    w

    30 hertz, Beta Waves, are associated tion, while waves between 8 and 1

    ith concentra- hertz, Alpha

    2

    Waves, and are associated with calm relaxation. Of-

    ten overshadowing brainwaves, the contraction of

    muscles is also associated with unique wave patterns, called EMG. Isolating these EMG patterns is how NeuroSky devices detect eye blinks.The single sensor on FP1 provides a high degree of freedom; NeuroSky devices can measure multiple mental states simulta- neously. The physics of brainwaves is virtually iden- tical to the physics of sound waves where a single microphone can pick up the complexity of a concert. EEG is the recording of electrical activity of the brain

    Fig 4. Data displaying attention & meditation level of the neural activity of brain

    In normal buildings, the electrical mains current radiates a 50Hz or60Hz electromagnetic field. In a laboratory setting, EEG is usually measured in a room that has less interference. At home, the EEG unit must filter out the troublesome signals. The

    headset has a single electrode with a ground and ref- erence. This means that there are two metallic things

    from the scalp, producedby neurons firing in the

    brain voltages (1100 µV on the scalp). EEG doesnt read your thoughts, but it cantell your general state. For example, EEG can show if you are paying atten- tion ormeditating. The tiny voltages are easily

    masked by electrical noise from muscles and ambient

    touching your head. The measuring electrode goes on the left side of your forehead. In the EEG lingo, this point is called Fp1. Its F for frontal, followed by p1 (1, the first odd number, indicates10% to the left of your nose; 2, the first even number, indicates 10% to

    sources. EEG currents are measured in microvolts (µV), which are millionths of a volt:1µV = 0.001 mV

    = 10-6 VNoise from muscle and eye movement can be quite powerful compared tothis.

    the right of your nose). The other electrode, reference

    point, goes to your leftear (A1). The headset meas- ures the voltage between these two electrodes.

  2. MICROCON ROLLER

    o

    Arduino is an open-source electr ing platform based on flexible, easy-t

    o

    nics prototyp-

    -use hardware

    capped people, and we can also use this technology for making mind controlled-wheelchair. The ad- vancements in the field of brain mapping opens door

    d

    and software. It's intended for artists, designers, hob-

    to numerous applications. Our

    motive is to create

    byists and anyone interested in creating interactive objects or environments.

    n

    Arduino can sense the environment by receiving input from a variety of sensors and can affect its sur- roundings by controlling lights, motors, and other actuators. The microcontroller on the board is pro- grammed using the Arduino programming lan-

    awareness in this field which will lead to more devel- opments in this area. The available technology allows us to develop more devices working solely on the basis of mind controlling. This technology can be used to control functions in a mobile phone like dial- ing a call, opening different apps etc.We can use this for implementing home automation like switching on

    s

    guage (based on Wiring) and the Arduino develop- ment environment (based on Proces ing). Arduino

    and off electronic appliances music players, lights,etc.

    like television sets,

    n

    projects can be stand-alone or they ca communicate

    e

    with software running on a computer (e.g. Flash, Processing, MaxMSP).

  3. DESIGN CONSIDERATION

    e

    The basic circuit for interfacing EEG band to ar- duino board contains a RF dongle and an arduino board. The neural signals are analyzed by EEG head- set and sent to the arduino board via RF dongle. These Signals are then processed by arduino board for controlling the motors. On concentrating the mo- tors start to rotate, focusing more incr ases the speed of the motor. Decrease in the concentration level leads toDecrease in the speed of the motor and even- tually it will stop.

    REFERENCES

    1. C. Lin, L. Ko, C. Chang, Y. Wang, C. Chung,F. Yang, J. Duann, T. Jung, and J. Chiou. Wearable and Wireless Brain-Computer Interface and Its Applications. Founda- tions of Augmented Cognition.Neuro ergonomics and Operational Neuroscience, pages741748, 2009.

    2. L. Farwell and E. Donchin. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and clinical Neurophysiology, 70(6):510523, 1988.

    3. F. Lotte, M. Congedo, A. L´ecuyer, F. Lamarche, and B. Arnaldi. A review of classification algorithms for EEG- based brain-computer interfaces. J Neural Eng, 4(2):R1 R13, Jun 2007.

    4. Massimo BanziGetting started with ArduinoMakezine.

    5. J.D. Bayliss, D.H. Ballard (2000), A virtual reality testbed for brain-computer interf ce

      Research. IEEE Trans Rehabil Eng. 8(2):188-90.

    6. A. J. Casson, D. C. Yates, S. J. Smith, J. S. Duncan, and E.

      RodriguezVillegas, Wearable electroencephalography,

      IEEE Eng. Med. Biol. Mag., vol. 29, no. 3, pp. 4456, 2010.

    7. ] Y. Wongsawat, S. Oraintara, T. Tanaka, and K. R. Rao, Lossless multichannel EEG compression, in IEEE IS- CAS, Kos, May 2006.

    8. Arduino http://www.arduino.cc/

    9. EEG Headset http://www.neurosky.com/Products/MindWave.aspx http://www.emotiv.com/

    10. Youtube video Mindwave http://www.youtube.com/watch?v=2Qp1Z_cTVtE

    Fig.5 complete circuit diagram for Arduino based bot

  4. CONCLUSION

We have presented the evaluation of an initial prototype that brings together neural signals and a machine interface. One could argue that connecting the wireless EEG headset and Arduino is a simple engineering exercise.We believe that the use of the brain machine interfacecan help physically handi-

Leave a Reply

Your email address will not be published. Required fields are marked *