Brain Controlled Artificial Legs

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  • Topic: Electroencephalography, Brain, Digital signal processing
  • Pages : 7 (2209 words )
  • Download(s) : 1758
  • Published : September 10, 2010
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BRAIN CONTROLLED ARTIFICIAL LEGS

ABSTRACT:
This paper describes a brain controlled robotic leg which is designed to perform the normal operations of a human leg. After implanting this leg in a human, the leg can be controlled with the help of user’s brain signals alone. This leg behaves similar to a normal human leg and it can perform operation like walking, running, climbing stairs etc. The entire system is controlled with the help of advanced microcontrollers and digital signal processors. The signals are taken out from the human brain with the help of electroencephalography technique. The person can perform operations like walking, running etc just by their thought. This system will be very much suitable for those who lost their legs in accidents and the proposed system is hundred percent feasible in the real time environment with the currently available technology. The Brain Controlled Artificial Legs are very much cost effective when compared to the normal Artificial legs which is available in the market. The reduction in cost of the proposed system is found to be above 80% when compared to the existing system. Moreover, the user can have full control over the artificial legs which is not possible in the existing system.

Introduction:
A brain-computer interface (BCI), sometimes called a direct neural interface or a brain-machine interface, is a direct communication pathway between a human or animal brain and an external device. In this definition, the word brain means the brain or nervous system of an organic life form rather than the mind. Computer means any processing or computational device, from simple circuits to the complex microprocessors and microcontrollers. An interesting question for the development of a BCI is how to handle two learning sys-tems: The machine should learn to discriminate between different patterns of brain activity as ac-curate as possible and the user of the BCI should learn to perform different mental tasks in order to produce distinct brain signals. BCI research makes high demands on the system and software used. Parameter extraction, pattern recognition and classification are the main tasks to be per-formed in a brain signals. In this paper it is assumed that the user of this system has one leg which is functioning fully and the system is designed accordingly. This system can be extended for both the legs and it is not limited to the basic operation of human legs such as walking, running, climb-ing stairs etc. It can also perform operations like cycling, hopping etc Brain Waves

Electrical activity emanating from the brain is displayed in the form of brainwaves. There are four categories of these brainwaves ranging from the most activity to the least activity. When the brain is aroused and actively engaged in mental activities, it generates beta waves. These beta waves are of relatively low amplitude, and are the fastest of the four different brainwaves. The frequency of beta waves ranges from 15 to 40 cycles a second.

The next brainwave category in order of frequency is Alpha. Where beta represented arousal, alpha represents non-arousal. Alpha brainwaves are slower and higher in amplitude. Their frequency ranges from 9 to 14 cycles per second. The next state, theta brainwaves, is typically of even greater amplitude and slower frequency. This frequency range is normally between 5 and 8 cycles a second. A person who has taken time off from a task and begins to daydream is often in a theta brainwave state. The final brainwave state is delta. Here the brainwaves are of the greatest amplitude and slowest frequency. They typically center around a range of 1.5 to 4 cycles per second. They never go down to zero because that would mean that you were brain dead. But, deep dreamless sleep would take you down to the lowest frequency. Typically, 2 to 3 cycles a second.

In the proposed system alpha waves and beta waves are used from the brain for signal processing. It is...
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