ABSTRACT

Electroencephalogram (EEG) signal plays an important role in E-healthcare system, especially the mental healthcare field. In order to improve access and quality of EEG data delivery, detect the mental depression and reduce the hardware cost, we present the design and application of a novel wearable EEG system. After the introduction of hardware, a novel algorithm to calculate EEG signal quality is given so as to control the communication, reduce complexity and the power consumption. Then, the main noises in EEG, such as Ocular Artifacts (OA) and DC adrift are removed by an improved de-noising approach. Finally, Alpha asymmetry and C0 complexity are used as main features to identify mental depression and sent to server by internet for further research. The results show that this EEG system can both work correctly and has low hardware cost. Furthermore, it has been used in the OPTIMI project of the EU's Seventh Framework Programme (FP7) and works well.