Robust single-trial ERP estimation based on spatiotemporal filtering

Extraction of the GVS electrical artifact from EEG recordings of the motor related cortical potential

BACKGROUND

Galvanic vestibular stimulation (GVS) involves the administration of low-amplitude trans-mastoidal current which induces a dense electrical field across the scalp that is difficult to remove from the EEG record. In two proof-of-concept experiments, we designed a paradigm to evaluate functional limb movement, and tested a method of blind source separation to remove the scalp artifact induced by low-amplitude, alternating current GVS to allow measurement of the motor-related cortical response (MRCP) during voluntary movement.

NEW METHOD

Off-line Extended Infomax Independent Component Analysis (ICA) was applied to the concatenated dataset to identify and remove core characteristics of the artifact induced by a trans-mastoidal current (Experiment 1: 0.01Hz, 0.2-3mA; Experiment 2: 0.01Hz, 0.3-0.4mA) during finger (Experiments 1 and 2) and foot tapping (Experiment 2).

RESULTS

In Experiment 1, a GVS-related independent component was identified and successfully removed without compromising measurement of the MRCP. This success was replicated in Experiment 2 which included both finger and foot tapping, and a higher GVS amplitude, which resulted in the identification of additional GVS-related artifacts.

COMPARISON WITH EXISTING METHODS

Existing methods of artifact rejection typically use an offline bandpass filter that overlaps with the frequency range of the MRCP. Even when similar ICA-based approaches have been employed, they have been applied during rest rather than active movement, have not been described in sufficient detail to enable replication, and require significant expertise and bespoke software to implement.

CONCLUSION

The ICA-based approach described here provides a relatively simple and accessible means by which MRCPs can be measured during alternating current GVS. This provides opportunity to identify new biomarkers associated with the therapeutic effects of GVS in people with Parkinson's disease and other disorders of voluntary movement.

A spatiotemporal filtering methodology for single-trial ERP component estimation

A new spatiotemporal filtering method for single-trial event-related potential (ERP) estimation is proposed. Instead of attempting to model the entire ERP waveform, the method relies on modeling ERP component descriptors (amplitude and latency) thru the spatial diversity of multichannel recordings, and thus, it is tailored to extract signals in negative SNR conditions. The model allows for both amplitude and latency variability in the ERP component under investigation. The extracted ERP component is constrained through a spatial filter to have minimal distance (with respect to some metric) in the temporal domain from a user-designed template component. The spatial filter may be interpreted as a noise canceller in the spatial domain. Study with both simulated data and real cognitive ERP data shows the effectiveness of the proposed method.