Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.

Psilocybin is a classical serotoninergic psychedelic that induces cognitive disruptions similar to psychosis. Gamma activity is affected in psychosis and is tightly related to cognitive processing. The 40 Hz auditory steady-state responses (ASSR) are frequently used as indicators to test the ability to generate gamma activity. Based on previous literature, we studied the impact of psilocybin on 40 Hz ASSR in healthy volunteers. The study was double blind and placebo controlled with a crossover design. A sample of 20 healthy subjects (10M/10F) received psilocybin orally 0.26 mg/kg or placebo. Participants were measured four times in total, one time before ingestion of psilocybin/placebo and one time after ingestion, during the peak of intoxication. A series of 500 ms click trains were used for stimulation. Psilocybin induced a psychedelic effect and decreased 40 Hz ASSR phase-locking index compared to placebo. The extent of the attenuation was related to Cognition and Affect on the Hallucinogen Rating Scale. The current study shows that psilocybin lowers the synchronization level and the amplitude of 40 Hz auditory steady-state responses, which yields further support for the role of gamma oscillations in cognitive processing and its disturbance.

Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO${}_2$ channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively.

Slow-wave synchronous acoustic stimulation is a promising research and therapeutic tool. It is essential to clearly understand the principles of the synchronization methods, to know their performances and limitations, and, most importantly, to have a clear picture of the effect of stimulation on slow-wave activity (SWA). This paper covers the mentioned and currently missing parts of knowledge that are essential for the appropriate development of the method itself and future applications. Artificially streamed real sleep EEG data were used to quantitatively compare the two currently used real-time methods: the phase-locking loop (PLL) and the fixed-step stimulus in our own implementation. The fixed-step stimulation method was concluded to be more reliable and practically applicable compared to the PLL method. The sleep experiment with chronic insomnia patients in our sleep laboratory was analyzed in order to precisely characterize the effect of sound stimulation during deep sleep. We found that there is a significant phase synchronization of delta waves, which were shown to be the most sensitive metric of the effect of acoustic stimulation compared to commonly used averaged signal and power analyses. This finding may change the understanding of the effect and function of the SWA stimulation described in the literature.

In this paper we report a new rat head phantom developed for testing electroencephalogram source localization techniques. The phantom is composed of an agar based mixture mimicking the rat brain, six dipoles and fourteen electrodes for modeling and monitoring of neural activity of the brain, respectively, and a 3D printed skull based on a computed tomography scan of a rat skull. In order to fabricate the phantom with currently available conventional techniques, the phantom is 1.8 times enlarged. To allow scaling, we performed an extensive study of electric properties of the agar based mixture, including electric conductivity, permittivity, and applied voltage, to ensure a linear operating regime. The new phantom facilitates testing of existing and the development of new cortical electrode implants as well as studying the quality of various source localization techniques.