Abstract
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: 0.01 Hz, 0.2-3 mA; Experiment 2: 0.01 Hz, 0.3-0.4 mA) 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.
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: 0.01 Hz, 0.2-3 mA; Experiment 2: 0.01 Hz, 0.3-0.4 mA) 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.
| Original language | English |
|---|---|
| Article number | 109459 |
| Journal | Journal of Neuroscience Methods |
| Early online date | 23 Dec 2021 |
| DOIs | |
| Publication status | Published - 7 Feb 2022 |