I Know it's EEG: An In-situ Selection of Ear-EEG channels

In order for an earable to function, it is necessary for the sensor electrodes within the ear to remain in constant contact with the skin. However, body movements tend to disrupt the extent of contact, leading to noisy signals being captured, which are often difficult to distinguish from a valid EEG signal. It is, therefore, important to identify which channels are capturing EEG when the data is being recorded. In this work, we present an innovative method for channel identification using the manifolds of an EEG signal. Furthermore, we test the ability of these manifolds using a clustering algorithm to classify EEG and non-EEG channels and achieve an accuracy of 87.09% for the classification. The proposed method will help enhance the performance of various applications pertaining to EEG monitoring and processing.