EEG-LLAMAS: an open source, low latency, EEG-fMRI neurofeedback platform

Abstract

Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source BCG removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors

“…We also found that the computational complexity of this algorithm was sufficiently low for use in real-time experiments, as the time required to process a segment of data was lower than the duration of that segment. This result is unsurprising, as an analogous technique was previously used in real-time [ 51 ]. However, it is important to note that this processing time depends upon the specific hardware used to perform the computation, and performance will vary across individual implementations.…”

“…However, in this work, we present a steerable spatial filter, rather than an FIR filter, that adaptively weighs all the motion sensor signals optimally or steers the weighted sum in the direction of minimal BCG noise. This type of spatial Kalman adaptive noise cancellation has been successfully applied to EEG/fMRI [ 51 ].…”

Sleep Neuroimaging: past research, present challenges and future directions

“…Artifact removal software is complex and mostly designed by private companies with closed-source code. Making such algorithms open-source or partially accessible to the public would enable improvements in the field (Levitt et al, 2022), potentially leading to advances such as an adapted individual-based artifact removal algorithm. Such a customized level would positively facilitate data pre-processing without compromising the EEG signal in special cases where artifact removal implies data loss.…”