  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.
