Computer Aided Detection of Dominant Artifacts in Ear-EEG Signal

Analysis of Electroencephalography (EEG) signals for everyday in-situ applications is hindered by many challenges including artifacts induced from physiological and environmental sources as well as anatomical factors. Recent studies have proposed analytical frameworks for the assessment of scalp EEG quality and identification of artifacts using rule based methods. These methods are typically used in an offline processing manner, employed before signal analysis when abundant data is available. With the advent of wearable devices, it is important to build techniques that work on short term signals giving us the ability of intervention based on the signal quality. Further, ear-EEG is a new modality that requires assessment and calibration with short term signals. To support this new modality we conducted a detailed study of scalp and ear-EEG data from the perspective of different measures as well as time duration. An algorithm is developed to identify the epochs with EOG and EMG artifacts in the ear-EEG using a set of metrics based on the characteristics of EEG. Thresholds of these metrics are determined by training on a dataset containing synchronous ear and scalp EEG with good results. The algorithm obtained an accuracy of 76.7% and 76.84% in classifying artifact EEG for scalp referenced ear-EEG and re-referenced ear-EEG, respectively.