EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI

Levitt, Joshua; Yang, Zinong; Williams, Stephanie D; Espinosa, Stefan E Lütschg; Garcia-Casal, Allan; Lewis, Laurie

doi:10.1016/j.neuroimage.2023.120092

Cited by 3 publications

(1 citation statement)

References 53 publications

(92 reference statements)

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“…Thus, averaged latency calculated for each lens separately could be used for better accuracy ( Cattan et al, 2021 ). It would be interesting to see the application of CLET in Brain-Computer Interface (BCI) extended to other neuroimaging modalities, such as functional magnetic resonance imaging (fMRI) ( Levitt et al, 2023 ) and magnetoencephalography (MEG) ( Liesefeld, 2018 ), to enable further multimodal investigations of brain activities during Virtual Reality (VR) or Augmented Reality (AR) experiences. This study was limited to only the onset of visual stimuli and offline analysis.…”

Section: Conclusion Limitations and Future Scopementioning

confidence: 99%

CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses

Swami,

Gramann,

Vonstad

et al. 2023

Front. Hum. Neurosci.

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To investigate event-related activity in human brain dynamics as measured with EEG, triggers must be incorporated to indicate the onset of events in the experimental protocol. Such triggers allow for the extraction of ERP, i.e., systematic electrophysiological responses to internal or external stimuli that must be extracted from the ongoing oscillatory activity by averaging several trials containing similar events. Due to the technical setup with separate hardware sending and recording triggers, the recorded data commonly involves latency differences between the transmitted and received triggers. The computation of these latencies is critical for shifting the epochs with respect to the triggers sent. Otherwise, timing differences can lead to a misinterpretation of the resulting ERPs. This study presents a methodical approach for the CLET using a photodiode on a non-immersive VR (i.e., LED screen) and an immersive VR (i.e., HMD). Two sets of algorithms are proposed to analyze the photodiode data. The experiment designed for this study involved the synchronization of EEG, EMG, PPG, photodiode sensors, and ten 3D MoCap cameras with a VR presentation platform (Unity). The average latency computed for LED screen data for a set of white and black stimuli was 121.98 ± 8.71 ms and 121.66 ± 8.80 ms, respectively. In contrast, the average latency computed for HMD data for the white and black stimuli sets was 82.80 ± 7.63 ms and 69.82 ± 5.52 ms. The codes for CLET and analysis, along with datasets, tables, and a tutorial video for using the codes, have been made publicly available.

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Section: Conclusion Limitations and Future Scopementioning

confidence: 99%

CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses

Swami,

Gramann,

Vonstad

et al. 2023

Front. Hum. Neurosci.

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NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI

Caetano,

Esteves,

Vourvopoulos

et al. 2023

NeuroImage

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

Levitt

Kouwe

Jeong

et al. 2023

Sensors

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We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat. Methods: The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. Results: MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. Conclusions: The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.

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EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI

Cited by 3 publications

References 53 publications

CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses

CLET: Computation of Latencies in Event-related potential Triggers using photodiode on virtual reality apparatuses

NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI

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

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