An improved EEG de-noising approach in electroencephalogram (EEG) for home care

Mental health care is becoming an increasing concern in home care projects. As an integral part of Telecare and Telehealth systems, portable EEG recording and real-time analysis are increasingly being used as non-intrusive monitoring techniques. In home environments without the supervision of a physician and absence of electromagnetic shielding, the raw EEG data, especially the most important alpha rhythm, which can be used to detect the mental illness and depression, is polluted by background noise such as Ocular Artifacts (OA), DC adrift and so on. In this paper, the raw data is processed in two steps: step one is a pre-process to remove DC adrift and 50/60Hz AC. In the second step, we demonstrate an improved real-time approach for removing OA online from alpha band of EEG. Furthermore, the application of this approach in the OPTEVII project of the EU's Seventh Framework Programme (FP7) demonstrates the applicability and reliability of our approach.