Abstract
Electroencephalography (EEG) will play an intelligent role our life: especially EEG based health diagnosis of brain disorder and Brain-Computer Interface (BCI) are growing areas of research. However, these approaches fall extremely short when attempting to design an automatic detection system and to use the same in BCI framework. The situation becomes even more difficult when the measurement system is being designed for a ubiquitous application for supporting people with disabilities, in which the patient is not confined to the hospital and the device is attached to him/her externally while the person is involved in daily chores. This paper presents a classification technique for one such system which is being built by the same team. Hence the presented work covers the initial findings related to some of the brain conditions in different scenarios that can be monitored in this setting and the detection system can produce control signals for the wheel chair movements that can be conveyed. Due to the compact nature of such systems, the detection and classification techniques have to be extremely simple in order to be stored in the small memory of the microcontroller of the ubiquitous system. The paper presents one such technique which is based on Fuzzy classifications of the EEG data using certain statistical features from the signal.