Development of electrical control system through human brain signal analysis

Abstract

EEG based Brain-Computer Interface (BCI) systems can establish a direct communication channel from the brain to an electronic device. It bypasses conventional motor output pathways of nerves and muscles and can provide effective control over electromechanical devices such as neuro- prosthesis, wheelchair or other communication devices, making it suitable for physically challenged people. Needless to say, the technology is very sophisticated and it is not mature yet in the global scenario. Besides, most such work is done in the economically advanced countries where technological simplicity, low cost, etc., are not of primary concern, while these are important for a low resource country like Bangladesh. The present work was taken up with such a scenario in mind, development of a BCI system, including hardware and software, with a high quality of performance but keeping cost and technological sophistication low. In the present work a complete BCI system has been developed from scratch, comprising of (i) hardware and software for EEG recording and interfacing to a computer, (ii) software for EEG trace visualization, (iii) signal analysis and processing, (iv) feature extraction algorithm and (v) classification method. The EEG device used is of only two channels which records from the sensorimotor cortex area (C3 and C4) of the human scalp that are responsible for limb movement. The bioelectric amplifier of the device consists of two channels and was designed to meet the requirement of low component count, high power efficiency, low noise and small physical size, suitable for a wearable system. To overcome some challenges of reliable EEG signal recording, several novel designs of hardware modules like AC coupled input stage, Driven Right Leg (DRL) circuit with improved performance, two-wired active electrode were proposed and implemented. These modules resulted in more stable EEG recordings with substantial amount of interference reduction as well as providing reinforced user safety. A skin-electrode contact impedance measurement system was developed which is able to measure the impedances of the ground and reference electrodes as well. A USB based data acquisition system was also developed for the BCI system using a microcontroller. The data acquisition system is useful during training phase and can also be used for standalone signal processing, data classification and control purpose.