The project is to develop a cost effective brain-computer interface for controlling the movement of a wheelchair. this project is primarily focused as an aid to completely paralyzed patients, as it can enable them to move about on their own, without any physical strain. the basic concept behind this project is to capture brainwaves using an eeg (electroencephalogram) sensor. brainwave is the electrical signals emitted by the brain as a result of neural interaction amongst billions of neurons in our brain. many scientists from different parts of the world have developed such wheelchairs that work on pure eeg signals; this is more complicated and is inconvenient as it covers the whole of the head and comes at an exorbitant prize which is away from the common mans' reach. the wheelchairs that are available currently rely on pure eeg signals for its control but its price makes it an impossible dream for most patients. also such wheelchairs use complex multi electrode eeg sensors that cover the whole head and will require the user to shave his head for better electrical conduction. such eeg sensors might prove difficult and tiring for an everyday use. the presently designed wheelchair uses a commercial eeg sensor (neurosky mindwave) which is a single electrode sensor. it thus makes it an easily accessible device. the electrode (eeg sensor) is placed on the forehead just above the left eye. being close to the eye, when the user blinks the eog (electroocculogram) signals interfere with the eeg signals. when an eye blink occurs electrical signals of amplitude of about 400µv are produced. eeg signals rarely exceed a 100µv. thus when a user blinks a sudden peak is produced in the eeg data. we can thus detect eye blinks by detecting the peaks in eeg data. the eeg sensor being a single electrode sensor cannot detect complex thoughts and associated brainwaves. it can only detect average brainwave amplitudes. also beta wave among all brainwaves is related to the alertness of a person; therefore measuring the amplitude of beta wave would indirectly mean measuring the attention level of the user. the beta wave has a frequency between 12.5 to 30hz, thus by using suitable filters, the beta waves can be separated from the rest of the brainwaves. a program using labview software is developed for receiving the raw signals from eeg headset. the program would then filter out beta waves and process the attention level of the user. the program will also detect peaks in raw brainwave signal and consider it as a blink event. an algorithm is also developed in the program that would trigger a series of loops as blink event occur. thus by finding the time interval between eye blinks and by finding the number of successive eye blinks, the program would switch between directions of wheelchair motion. the final data is then sent to the receiver on the wheelchair which is connected to a microcontroller. it controls the motion of the wheelchair according to the commands received from the labview program. however, the presently designed wheelchair designed is more convenient to use as its sensor rests on the forehead. also the cost of this wheelchair is 10 times less than that presently available in the market.