Cochlear implantation and EEG
The paragraph below is written by Dr.-Ing. Kidist Mideksa, Brain Products GmbH, Scientific Support.
The main difficulty in assessing ERPs of CI users is the electrical artifact generated by the implant. Several studies have addressed different options to attenuate this artifact depending on the duration of the auditory stimuli:
- using short auditory stimulation to avoid an overlap of the CI artifact to assess late (longer latency) ERP components.
- for longer stimuli where the artifact persists into the latencies of the ERP components, the possibilities are:
- to attenuate CI artifacts using ICA. Here, the main challenge is how to identify the component that represents such an artifact.
- Analyzer offers the infomax based ICA which is also offered by EEGLab. As opposed to identification of ocular related artifacts, Analyzer's ICA implementation doesn't pre-identify the components related to CI artifacts. However, it provides the temporal evolution and topography of each component thereby facilitating the user to perform subjective decision in identifying and selecting the representative component. Further information on Analyzer's ICA module can be found in the dedicated chapter of the User Manual. Our press article on "Independent Component Analysis – demystified" also provides detailed complementary information.
- On the other hand, EEGLab's plugin (CIAC algorithm) is tailored in identifying CI artifacts by combining the temporal and spatial property of the components along with user-defined thresholds.
- analyzing the ERP on the difference wave of conditions (e.g., target vs non-target conditions). It's usually expected for the CI artifacts to be present in both conditions. Hence, by subtracting the waveform of the conditions, the artifact is eliminated or minimized.
- If you would like to perform such a difference wave, you can use Analyzer's module called Data Comparison.
- to attenuate CI artifacts using ICA. Here, the main challenge is how to identify the component that represents such an artifact.
You might also find the following articles helpful:
- Vavatzanidis et al.: "Establishing a mental lexicon with cochlear implants: an ERP study with young children"
- Friesen et al.: "A method for removing cochlear implant artifact"
The paragraph below is written by Dr. David Schubring, Brain Products GmbH, Scientific Consultant
In general all electrode types will show an artifact, especially around the region of the cochlear implant (CI) coil. This is true for both active and passive electrodes. The nature of this artifact depends also on the auditory stimulation, e.g. electrical vs. free field.
One should generally omit the electrodes directly around the CI, also because of the gel. There are also special EEG caps for this, that could also be adapted for active electrodes (the link is in german, but you can see picture with the cutout in the cap).
Moreover, there are multiple papers on the matter, e.g. here, where you can see the CI artifacts lateralized on the implanted side:
https://www.sciencedirect.com/science/article/pii/S1746809416300933#fig0025
And multiple papers demonstrating the general feasibility of EEG with CI.
https://www.frontiersin.org/articles/10.3389/fnhum.2014.00181/full
or
https://www.frontiersin.org/articles/10.3389/fnhum.2015.00007/full
Moreover, a thorough search of the respective literature should reveal even more.
Some relevant papers
A 2018 paper exploring CI artifacts in EEG experiments:
The Cochlear Implant EEG Artifact Recorded From an Artificial Brain for Complex Acoustic Stimuli
Conclusion: "The CI EEG artifact for speech appears more difficult to detect than for simple stimuli. Since the artifact differs across CI users, due to their individual clinical maps, the method presented enables insight into the individual manifestations of the artifact."
A 2020 EEG study with CI participants, describing artifact removal with MATLAB/EEGLAB:
Neural Mechanisms of Hearing Recovery for Cochlear-Implanted Patients: An Electroencephalogram Follow-Up Study
From methods: "Besides, the onset of CI stimulation evokes an electrical artifact and therefore inevitably corrupts the EEG signal. The CI artifact may largely be due to the radio frequency transmission of the signal to the receiver. Then, CI-artifact-related ICs were identified using the CI Artifact Correction (CIAC) algorithm, a plug-in in EEGLAB (Viola et al., 2009, 2011, 2012).
In our study, the CI-artifact-related ICs were selected both by CIAC and manual selection. The CI-artifact-related ICs selected by CIAC were not complete in some cases. Therefore, if the reconstruction of the individual ERPs was not reasonable after CIAC, the remaining CI-artifact-related ICs were selected manually based on the characteristics of the waveform and brain topographic maps of the ICs. The epochs contaminated by head movement were identified visually and rejected."
A 2013 CI study that utilized the same EEG analysis software as the lab provides (BrainVision Analyzer 2). However, the artifact removal step was done in MATLAB/EEGLAB:
From methods:
"An oddball paradigm with 680 standard (/u/std) and 120
deviant (/i/dev) stimuli (ISI=700—900ms) was implemented.
At least five standards separated two deviants. Nine standards
preceded the first deviant. The EEG was passively recorded
while children were watching a silent movie on a TV screen.
BrainVision Recorder 1.20 and Acticap System (also
BrainAmp and BrainVision Analyzer 2.0, BrainProducts,
Gilching, Germany) with 32 active Ag/AgCl channels (F7, F3,
Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, CP5,
CP1, CP2, CP6, TP9, P7, P3, Pz, P8, TP10, Oz, FP1VEOG,
FP2VEOG, FT9HEOG, FT10HEO2, FCzRef, and AFzGnd) were used
to record the EEG signal. This was amplified with a BrainAmp
Amplifier, using a bandpass filter from 0.1 to 200Hz and a
sampling rate of 500Hz. Impedances were kept below 10kΩ.
To reduce/eliminate the CI artifacts, we performed an
Independent Component Analysis (ICA) [29-31] implemented
on EEGLAB [32] and running in MATLAB. The ICA was
conducted for each CI user, decomposing the EEG data into 16
components. Only components clearly showing CI related
artifacts were removed. All EEG data were imported into
BrainVision Analyzer 2.0. Individually, the initial nine
standards and the 120 standards following the deviants were
excluded from the analysis."
EEG in head and movement:
The paragraph below is written by Dr.-Ing. Kidist Mideksa, Brain Products GmbH, Scientific Support.
Regarding the tolerance level of body motion artifacts while getting meaningful ERPs, I'm afraid it's not that trivial to provide a single value. This needs to be assessed by comparing the data contaminated with motion artifacts to that of a control dataset (for e.g., "resting state" activity). Considering the difficulty of getting movement-free infant EEG data, the following article, by Noreika et al.: "14 challenges and their solutions for conducting social neuroscience and longitudinal EEG research with infants", have addressed common motions produced by infants and their respective artifactual influence in the EEG recording. I would also recommend to check out for literature's in the direction of your research area.
The paragraph below is written by Dr. David Schubring, Brain Products GmbH, Scientific Consultant
Movement artifacts should of course be minimized as good as possible. The advantage of active electrodes is that the cables are not as sensitive to artifacts as passive electrodes, as the impedance conversion already happens in the electrode. However, the contact of the electrode and the cables as well should be fixated as good as possible nevertheless. There is no set threshold for this, as for some signals of interest you can also separate the artifacts from the signal, while for others they might be not as easily separable. Most importantly, the artifacts should not be correlated to your stimulation, so that you can still separate them from your trials. E.g. if your stimulation evokes not only a brain potential but also a movement, you cannot easily separate them. However, if the movement is uncorrelated to your signal, you can average it out with enough trials eventually.