The common structure of a Brain Computer Interface can be simplified in to: 1) Signal Acquisition andPre-Processing: the EEG signals are obtained from the brain through invasive or non-invasive methods (for example, electrodes). After that , the signal is amplified and sampled. Once the signals are acquired, it is necessary to clean them. 2) Signal Classification: once the signals are cleaned, they will be processed and classified to find out which kind of mental task the subject is performing. 3) Computer Interaction: once the signals are classified, they will be used by an appropriate algorithm for the development of a certain application.
Block diagram of Mind controlled wheelchair
For simplicity the mind controlled wheelchair can be viewed as a combination of three functional blocks. These are thought acquisition and signal preprocessing block, thought processing block and motor control block each of which aims at acquisition of the EEG signal from user scalp and processing it for controlling a wheelchair. EEG scalp potentials obtained are amplified, digitized and transmitted to a processor and after processing the output of the processed signals are used to control the wheelchair. The three main blocks involved in the wheelchair system are discussed below. A. Thought Acquisition and Signal Pre-processing Block The block acquires signal from the scalp by the electrodes. This section may include instrumentation amplifier, operational amplifier, high pass, low pass and notch filters. The purpose of the instrumentation amplifier is to extract the EEG signal by introducing as low noise as possible. This increases the signal to noise ratio of the acquired signal. The extracted EEG signal is passed through the operational amplifier for proper amplification, It is then, passed through the high pass, low pass and notch filters. These filters enable the excluding of the frequency ranges that are not point of interest. One of the disadvantages of these filters is the introduction of delay.
Block diagram of Thought Acquisition and Pre-processing block
B. Thought Processing Block The acquired EEG signal (thought) is feed to a processor. It consists of ADC converter for digitizing the EEG signal. This block consists of processor which processes the signal that is transmitted from the thought acquisition block to processor block. This section does all the functions for decision making (signal classification). It processes functions such as direction, speed , stop etc [5]. C. Wheelchair Control System The wheelchair controller has the functionality of controlling the direction and speed of the wheelchair based on the output bits obtained from the signal processing block. Motor driver is used in between the processor and the Permanent Magnet Direct Current motors that drives the wheel. This module is used to control the motors in real time wheelchair. Two motors connected to the rear wheels of the wheelchair have to be controlled to define its motion. Pulse Width Modulation (PWM) technique can be used to control the speed. The power wheelchair can be directionally controlled using suitable motor driver. Emergency Stop can be matched with other activities such as deep breath.
Direction of the wheel chair
Shared control: Driving a real wheelchair safely in more complex environments would be a highly demanding task for a BCI subject, for example, due to the necessity of delivering commands with precise timing. However, shared control techniques where the intelligent controller relieves the human from low level tasks, without sacrificing the cognitive superiority and adaptability of human beings have been shown to significantly reduce workload, whilst simultaneously improving task performance. Therefore, shared control principles were incorporated into the motor–imagery brain controlled wheelchair. In other words, the shared control paradigm includes two intelligent agents: the human user and the robot, such that the user need only convey intentions, which the robot interprets in the current context.
To tackle navigation in the real–world, which is full of obstacles, development of sophisticated computer vision algorithms that worked with off the shelf webcams and combined this sensory information with an array of sonars that fed to the shared control algorithm is necessary for obstacle detection. These two low cost sensing devices compensate for each other’s drawbacks and complement each other’s strengths.