Intelligent Analysis of EEG Signals

ADI has developed a system that monitors different biological signals from the human body. We can see the heart electrical activity that is generated by action potential in heart muscle walls (SA node, bundle branches, peace maker and etc.) This is called ECG(electro cardiogram). There are also another signal called EMG (Electro-Myogram) which monitoring the activity of the muscles. The brain neuron electro-chemical activities can be monitored by the EEG which stands for electro-encifologram. Finally the system also detects the GSR which stands for galvanic skin response. Our main goal is to understand and extract patterns of the EEG and understand actions that affects this signal, therefore we are aiming to develop an accurate brain computer interference to process these signals. Every movement of the body is controlled by thoughts that turns into an actions. When we try to learn something new, we feel sometimes are excited and sometimes not, however the question here is how can we quantify our awareness?. This is the area of cognitive psychology where the EEG analysis can be very useful tool. This information is being generated in our brain and is related with a electrical activity that can be obtained using electrodes placed on scalp. In the same time we have other bio-generated electrical activities in our body such as ECG, EMG and GSR. Mostly researchers concentrate on working on EEG analysis, but there has been studies conducted proving that these signals are related in some way. Our objective is to control things using just our mind and monitor brain activities. This would give another way instead of muscle movement for severely paralysed people. The applications are possible in many areas. On the other hand this gives us an opportunity to monitor person’s cognitive state. There are many car crashes that are related wit to create a software recognising EEG signal patterns related to particular tasks. At the moment we have sufficiently extracted specific frequency bands of EEG signal that’s been extracted during e-learning lesson. We have different ways to remove a noise and filter the signal, remove muscle artefacts and monitor where in certain periods of time the frequency is higher and where is lower. Eventually our goal is to see what patterns are presented during different activities such as opening and closing the palm, watching exciting video, a very boring and tedious one, and learning something that is very interesting or sometimes is not. If we manage to understand deeply how everything is related to each other, we can design a system that in real time inputs signal, pre-processes it, extracts significant information and outputs an action signal or specific information we desire.