Classifying mental gestures with in-ear EEG

While brain-computer interfaces (BCI) based on electroencephalography (EEG) have improved dramatically over the past five years, their inconvenient, head-worn form factor has challenged their wider adoption. In this paper, we investigate how EEG signals collected from the ear could be used for “gestural” control of a brain-computer interface (BCI). Specifically, we investigate the efficacy of a support vector classifier (SVC) in distinguishing between mental tasks, or gestures, recorded by a modified, consumer headset. We find that an SVC reaches acceptable BCI accuracy for nine of the subjects in our pool (n=12), and distinguishes at least one pair of gestures better than chance for all subjects. User surveys highlight the need for longer-term research on user attitudes toward in-ear EEG devices, for discreet, non-invasive BCIs.