A Sensor-Fusion Method for Motion Artifacts Reduction in Intraoral EEG Signals

In recent studies, electroencephalogram (EEG) signals are acquired intraorally from the palate region. However, intraoral EEG study is a less explored research area and its challenges are yet to be investigated. In this study, we look into the possibility of studying EEG signals from various intraoral locations and investigate the sources of motion artifacts during intraoral EEG measurements. Later, we propose a sensor fusion of EEG electrodes and accelerometer module to monitor intraoral EEG signal and intraoral motions simultaneously. The EEG electrodes, accelerometer, and sensor read-out circuitry are integrated with a mandibular advancement device (MAD). The system is battery-operated and uses a Bluetooth 5.0 transceiver to send data wirelessly. The smart MAD is used to acquire intraoral EEG and accelerometer data and a MATLAB-based algorithm is implemented using empirical mode decomposition (EMD) and independent component analysis (ICA) to decompose the EEG signal components. The decomposed ICA components containing intraoral motion artifacts are then mapped with the motion events extracted from the accelerometer data to identify the motion-corrupted data segments. The ICA components containing intraoral motions are then denoised by nullifying the motion-corrupted data segments. A motion artifact reduced intraoral EEG is reconstructed from the denoised ICA components. The efficacy of the sensor fusion and the proposed algorithm are demonstrated by quantifying the signal-to-noise ratio (SNR) difference and percentage artifacts reduction based on correlation analysis from the EEG signals before and after motion artifacts reduction. Later, the processed intraoral EEG signals are also analyzed for the detection of ‘eye open’ and ‘eye close’ activities in the presence of intraoral motions. The device along with the algorithm will have potential applications for motion artifact-free intraoral EEG monitoring.