Reference layer adaptive filtering (RLAF) in simultaneous EEG-fMRI David Steyrl 1 and Gernot R. M¨ uller-Putz 1 1 Institute of Neural Engineering, BCI Lab, Graz University of Technology, Austria Introduction Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods: high temporal resolution of EEG and high spatial resolution of fMRI. EEG recordings are, however, afflicted by severe artifacts caused by fMRI scanners. Average artifact subtraction (AAS) is a common method to reduce those artifacts [1]. Recently, we introduced an add-on method that uses a reusable reference layer EEG cap prototype in combination with adaptive filtering, to improve EEG data quality substantially [2, 3]. The methods applies adaptive filtering with reference layer artefact data to optimize artefact subtraction from EEG and is named reference layer adaptive filtering (RLAF). Methods The reference layer cap was developed by GUGER TECHNOLOGIES OG, Austria, see Figure 1 panel A, B and C. It consists of 30 double- layer electrode pairs and 2 additional ECG electrodes. 29 electrode pairs are capturing EEG and one electrode pair serves as common ground/reference electrode. The reference layer itself is a system of saline-water filled tubes and galvanically isolated from the scalp except at the common ground/reference electrode. Figure 1: A - Rendering of the reference layer cap prototype. B - The reference layer cap in use. C - Schematics of the double layer electrode pairs. The experiment was designed to evaluate visual evoked potentials (VEPs) and alpha-rhythm changes between opened and closed eyes. The two participants underwent inverse checker-board stimuli to trig- ger VEPs (1200 inversions). Subsequently, 10min of resting EEG during closed eyes was recorded. The signal processing is depicted in Figure 2 and included a 1 Hz high- pass, AAS of the gradient artefact (GA, 50 epochs for averaging), down- sampling to 250 Hz, AAS of the pulse artefact (PA, 50 epochs), a 50 Hz notch filter and finally the adaptive filtering. HP 1Hz 1 2 N ... reference 1 2 N ... scalp 1 2 N ... reference 1 2 N ... scalp Raw AAS GA 1 2 N ... reference 1 2 N ... scalp AAS PA 1 2 N ... reference 1 2 N ... scalp ECG Notch 50Hz 1 2 N ... reference 1 2 N ... scalp AAS(GA+PA) 1 2 N ... RLAF RLAF adaptive ltering Figure 2: Signal processing scheme. We optimized the step width of the adaptive filtering to guarantee sta- bility and fast convergence of the method (7 · 10 -6 to 1.5 · 10 -4 ). Results We used a similar evaluation procedure as used by Jorge et al. [4]. All results are depicted in Figure 3. −100 0 100 −30 0 30 −30 0 30 Time (s) 1 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 0 0.5 1 1.5 2 2.5 Time (s) Scaling Factors (AU) 0.7 1.4 2.0 B Channel O2 Participant 2 Channel FC1 Participant 2 Channel FC1 Participant 1 1.7 1.2 0.7 Amplitude (µV) EEG at O2 after AAS (GA) EEG at O2 after AAS (GA+PA) Ref at O2 after AAS (GA+PA) PA PA PA PA PA PA PA PA A3 A3 A5 A5 A2 A2 A1 A1 alpha alpha A4 A4 −30 0 30 EEG at O2 after RLAF A5 A3 A2 A1 alpha alpha A4 A 760 761 762 763 764 765 766 768 769 40 35 30 25 20 15 10 5 0 µV² alpha power after AAS (GA+PA) alpha power after RLAF S2 alpha power after AAS (GA+PA) 20 18 16 14 12 10 8 6 4 2 0 µV² alpha power after RLAF S1 C Number VEP O2 Raw 200 400 600 800 1000 1200 O2 AAS(GA+PA) .1 .2 .3 −4 −2 0 2 4 6 Time (s) Amplitude (µV) MSD: 1918.5 O2 O1 .1 .2 .3 Time (s) MSD: 200.2 O2 O1 O2 AAS+RLAF .1 .2 .3 Time (s) MSD: 28.6 O2 O1 Number VEP O1 Raw 200 400 600 800 1000 1200 O1 AAS(GA+PA) .1 .2 .3 −4 −2 0 2 4 6 Time (s) Amplitude (µV) MSD: 1136.5 O1 O2 .1 .2 .3 Time (s) MSD: 152.8 O1 O2 O1 AAS+RLAF .1 .2 .3 Time (s) MSD: 38.4 O1 O2 D Figure 3: A - EEG time course comparison after artefact reduction in partici- pant1. B - Adaptive filter coefficients’ course over time. C - Alpha power spatial distribution. D - Selected single and average VEPs of participants 1 and 2. Discussion The adaptive filter scaling values are changing over time, which indicates the necessity of adaptive filtering. Our results demonstrate that RLAF reduces artefacts while it preserves EEG phenomenons as evoked poten- tials and the occipital alpha rhythm. Hence, RLAF is able to improve the quality of EEG of simultaneous EEG-fMRI experiments. References 1. P.J. Allen, O. Josephs, and R. Turner, A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage, 12(2), pp.230-239, 2000. doi:10.1006/nimg.2000.0599 2. D. Steyrl, F. Patz, G. Krausz, G. Edlinger, and G. R. M¨ uller-Putz, Reduction of EEG Artifacts in Simultaneous EEG-fMRI: Reference Layer Adaptive Filtering (RLAF), in Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC15, Milano, Italy, August 25-29, 2015, pp.3803-3806. doi.org/10.1109/EMBC.2015.7319222 3. D. Steyrl, G. Krausz, K. Koschutnig, G. Edlinger, and G. R. M¨ uller-Putz, Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI, Journal of Neural Engineering, vol.14(2), pp.1-20, 2017. doi.org/10.1088/1741-2552/14/2/026003 4.J. Jorge, F. Grouiller, R. Gruetter, W. van der Zwaag, P. Figueiredo, Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion, Neuroimage, 120, pp.143-153, 2015. doi:10.1016/j.neuroimage.2015.07.020 Acknowledgments This work was partly supported by the ERC Consolidator Grant 681231 ‘Feel Your Reach’. 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