ISSN (Online) : 2278-1021 ISSN (Print) : 2319-5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 1, January 2015 Copyright to IJARCCE DOI 10.17148/IJARCCE.2015.4161 271 A Survey of Brain-Computer-Interaction Methods and Algorithms Preeti Ghasad 1 , V.N. Sahare 2 Dept., of Computer Science and Engg., G.H Raisoni Institute of Engg., and Technology for Women, Nagpur, Maharashtra, India 1 Assistant Professor, Dept., of Computer Science and Engg., G.H Raisoni Institute of Engg., and Technology for Women Nagpur, Maharashtra, India 2 Abstract: In recent years several researchers are practicing on multiple body-machine interfacing techniques. Many techniques were successfully implemented and launched as a commercial products for medical and bioinformatics communications. Still concerning electro-oculography interfacing for Human Computer Interface (HCI) has wide scope of development and real life implementations. Like EEG, EMG and ECG, EOG doesn't provide important body parameters which could be used for disease diagnosis but it has very wide applications of machine interactions. The eye movement is known to be a essential communication tool for any person hence EOG could be used by Paralyzed stroke patients are unable to normally communicate with their environment. These patients can control the only part of their body is their eyeballs. Proposed system will identify the variations in electric signal strength through voltage level near the eye area and generates a signal in order to control the multimode interactive device. Different type of instrumental amplifiers could be used for better results and interfaced with communication devices. Keywords: Human Computer Interface (HCI), Electrooculogram (EOG), Electroencephalograph (EEG), Electrocardiogram (ECG), Electromyogram (EMG). I. INTRODUCTION BRAIN computer interface (BCI), often called as a mind- machine interface (MMI), or sometimes also known as straight neural interface or a brain–machine interface (BMI), it is a direct communication pathway between the brain and an external device. BCIs are frequently anticipated for support, augmenting, or repairing human cognitive or sensory functions. A brain– computer interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Mostly researchers have investigated BCI in humans using scalp-recorded electroencephalography or intracranial electrocorticography. The use of brain signals which obtained directly from stereotactic depth electrodes to control a BCI has not previously been explored. Most researchers are investigating BCI in humans has used scalp-recorded electroencephalography or intracranial electroorticography.In other methodology; most famous approaches involve the use of a camera to visually track the eye. However, this method has problems that the eyes of user must always be open. Proposed systems will detect the variations in electric signal strength through the voltage level near the eye area and generates a wireless radio frequency signals in order to control the robotic model. By implementing this system we can further extend it to bio enabled human body parts to control through brain waves. Electroencephalography (EEG) is the most studied potential non-invasive interface occurs mainly due to its fine temporal declaration, easy to use, high portability and low cost set-up. But as well as the technology's susceptibility to noise, another considerable difficulty to using EEG as a brain– computer interface is that the Fig1. EEG, EOG, EMG extensive training is required before users can work on this technology. For example, in these experiments several paralyzed people are trained to self-regulate the slow cortical potentials in their EEG to such an extent that these signals could be used only as a binary signal to control a computer cursor. By controlling their brain waves, the experiment saw the ten patients trained to move a computer cursor. The process was slow, it took more than an hour for patients to write 100 characters with the cursor, while training often required many months. II. RELATED WORK MINGMIN YAN SOU GO, HIROKI TAMURA,KOICHI TANNO [1], proposed the mouse cursor control system for ALS patients using EOG and EEG signals. They introduced the algorithm using alternating current and direct current of EOG corresponding to the drift. They also took measurement to examine whether the subject could control their eye movement consciously. The EEG signals were not used to control the mouse movement, but to determine the subject’s control state.