Dynamical Embedding of Single Channel Electroencephalogram for Artifact Subspace Reconstruction

TL;DR

This paper presents Embedded-ASR, a novel method that applies artifact subspace reconstruction to single-channel EEG data using dynamical embedding, enabling artifact removal on smartphones with minimal electrodes.

Contribution

The study introduces Embedded-ASR, a new approach that extends ASR to single-channel EEG by incorporating dynamical embedding, facilitating artifact removal without multi-channel data.

Findings

E-ASR reduced artifacts with RRMSE of 45.45%

Correlation coefficient of 0.91 indicates high signal fidelity

Successfully detected eye-blinks aligning with ground truth

Abstract

This study introduces a novel framework to apply Artifact Subspace Reconstruction (ASR) algorithm on single-channel Electroencephalogram (EEG) data. ASR, renowned for its automated capability to effectively eliminate various artifacts like eye-blinks and eye movements from EEG signals. Importantly it has been implemented on android smartphones, but relied on multiple channels for principal component subspace calculations. To overcome this limitation, we incorporate the established dynamical embedding approach into the algorithm, naming it Embedded-ASR (E-ASR). In our proposed method, an embedded matrix is first constructed from a single-channel EEG data using series of delay vectors. ASR is then applied to this embedded matrix, and the resulting cleaned single-channel EEG is reconstructed by removing the time lag and concatenating the rows of the embedded matrix. Data was collected from four subjects in resting states with eyes open from pre-frontal (Fp1 and Fp2) electrodes using CameraEEG app. To assess the effectiveness of the E-ASR algorithm on an EEG dataset with artifacts, we employed performance metrics such as relative root mean square error (RRMSE), correlation coefficient (CC), average power ratio as well as estimated the number of eye-blinks with and without the E-ASR approach. E-ASR was able to reduce artifacts from the semi-simulated EEG data, with an RRMSE of 45.45% and a CC of 0.91. For real EEG data, the counted eye-blinks were manually cross-checked with ground truth obtained from CameraEEG video data across all subjects for individual Fp1 and Fp2 electrodes. In conclusion, our study suggests E-ASR framework can remove artifacts from single channel EEG data. This promising algorithm might have potential for smartphone-based natural environment EEG applications, where minimal number of electrodes is a critical factor.