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1. SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction

   https://doi.org/10.3389/fncom.2022.803384  

   Liu, Yimeng; Höllerer, Tobias; Sra, Misha (May 2022, Frontiers in Computational Neuroscience)

   Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. In this work, we address the EEG noise/artifact correction problem. Our goal is to detect physiological artifacts in EEG signal and automatically replace the detected artifacts with imputed values to enable robust EEG sensing overall requiring significantly reduced manual effort than is usual. We present a novel EEG state-based imputation model built upon a recurrent neural network, which we call SRI-EEG, and evaluate the proposed method on three publicly available EEG datasets. From quantitative and qualitative comparisons with six conventional and neural network based approaches, we demonstrate that our method achieves comparable performance to the state-of-the-art methods on the EEG artifact correction task. 

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2. Quantifying VR cybersickness using EEG

   https://doi.org/10.1007/s10055-021-00517-2  

   Krokos, Eric; Varshney, Amitabh (May 2021, Virtual Reality)

   null (Ed.)

   Current techniques for characterizing cybersickness (visually induced motion sickness) in virtual environments rely on qualitative questionnaires. For interactive graphics to create visual experiences that enhance the illusion of presence while mitigating cybersickness, interactive measures are needed to characterize cybersickness. In this paper, we acquire EEG signals from participants as they experience vection-induced cybersickness and compare those signals to a baseline. Our study shows that there is a correlation between the participant-reported cybersickness (as measured by movements of a joystick) and brain EEG signals. Through independent component analysis, we separate those signals which are a result of cybersickness from other sources (such as eye blinks). Our user study finds that there is a highly correlative and statistically significant Delta- (1.0–4.0 Hz), Theta- (4.0–7.0 Hz), and Alpha-wave (7.0–13.0 Hz) increase associated with cybersickness in immersive virtual environments across participants. Establishing a strong correlation between cybersickness and EEG-measured brain activity provides us with the first step toward interactively characterizing and mitigating cybersickness in virtual environments. 

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3. Speech Activity Detection from Stereotactic EEG

   https://doi.org/10.1109/SMC52423.2021.9659058  

   Soroush, P. Z.; Angrick, M.; Shih, J.; Schultz, T.; Krusienski, D. J. (January 2021, IEEE SMC)
4. ASRE: Adaptive Spatial Resolution Wearable EEG

   https://doi.org/10.1109/SENSORS47087.2021.9639244  

   Yang, Yongchao; Sun, Ye (December 2021, IEEE Sensors Conference)
5. ASRE: Adaptive Spatial Resolution Wearable EEG

   Yongchao Yang and Ye Sun (January 2021, In 2021 IEEE Sensors (pp. 1-4). IEEE.)