It has been known for a long time that as neurons within the brain fire, some measurable electrical activities is produced. Electroencephalography (EEG) is one way of measuring and recording of the electrical signals produced by these electrical activities via some sensor arrays across the scalp. Although there is plentiful research in using EEG technology in the fields of neuroscience and cognitive science and the possibility of controlling computer-based devices by thought signals, utilizing of EEG measurements as inputs to the control devices has still been an emerging technology over the past 20 years, current BCIs suffer from many problems including inaccuracies, delays between thought, false positive detections, inter people variances, high costs, and constraints on invasive technologies, that needs further research in this area.The purpose of this research is to examine this area from Machine Learning and Signal Processing perspective and exploit the Emotiv System as a cost-effective, noninvasive and also a portable EEG measurement device, and utilize it to build a thought-based BCI to control the cursor movement. This research also assists the analysis of EEG data by introducing the algorithms and techniques useful in processing of EEG signals and inferring the desired actions from the thoughts. It also offers a brief future potential capabilities of research based on the platform provided.
Read
