BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

The Lab Streaming Layer for Synchronized Multimodal Recording.

Accurately recording the interactions of humans or other organisms with their environment or other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common LAN. Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features ensure precise, continuous data recording, even in the face of interruptions. The LSL ecosystem has grown to support over 150 data acquisition device classes as of Feb 2024, and establishes interoperability with and among client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording and it is now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis packages, and brain-computer interfaces. Outside of basic science, research, and development, LSL has been used as a resilient and transparent backend in scenarios ranging from art installations to stage performances, interactive experiences, and commercial deployments. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes using multiple data streams at a common timebase while capturing time details for every data frame.

Who's calling? Evaluating the accuracy of guessing who is on the phone.

Some people claim to occasionally know who is calling them without using traditional means. Controlled experiments testing these claims report mixed results. We conducted a cross-sectional study of triads examining the accuracy of knowing who was calling using two randomly selected designs: 1) a web server randomly chose the caller before the callee's guess (telepathic/pre-selected trials), and 2) a web server randomly chose the caller after the callee's guess (precognitive/post-selected trials). We also performed exploratory multilevel mixed-effects logistic regressions on the relationship of genetic relationships, emotional closeness, communication frequency, and physical distance data with accuracy. A total of 177 participants completed at least one trial (105 "completers" completed all 12 trials). Accuracy was significantly above chance for the 210 completers telepathic/pre-selected trials (50.0% where the chance expectation was 33.3%, p<.001) but not the 630 completers precognitive/post-selected trials (31.9% where the chance expectation was 33.3%, p = .61). We discuss how these results favor the psi hypothesis, although conventional explanations cannot be completely excluded. Genetic relatedness significantly predicted accuracy in the regression model (Wald χ2 = 53.0, P < .001) for all trials. Compared to 0% genetic relatedness, the odds of accurately identifying the caller was 2.88 times (188%) higher for 25% genetic relatedness (Grandparent/Grandchild or Aunt/Uncle or Niece/Nephew or Half Sibling; ß = 1.06, z = 2.10, P = .04), but the other genetic relatedness levels were not significant. In addition, communication frequency was significant (ß = 0.006, z = 2.19, P = .03) but physical distance (ß = 0.0002, z = 1.56, P = .12) and emotional closeness (ß = 0.005, z = 1.87, P = .06) were not for all trials. To facilitate study recruitment and completion, unavoidable changes to the protocol were made during the study due to persistent recruitment difficulties, including changing inclusion/exclusion criteria, increasing total call attempts to participants, adjusting trial type randomization schema to ensure trial type balance, and participant compensation. Thus, future research will be needed to continue to improve the methodology and examine the mechanism by which people claim to know who is calling, as well as factors that may moderate the effects.

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS).

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

A comparison of neuroelectrophysiology databases.

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

A Comparison of Neuroelectrophysiology Databases.

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

Evaluating brain spectral and connectivity differences between silent mind-wandering and trance states.

Trance is an altered state of consciousness characterized by alterations in cognition. In general, trance states induce mental silence (i.e., cognitive thought reduction), and mental silence can induce trance states. Conversely, mind-wandering is the mind's propensity to stray its attention away from the task at hand and toward content irrelevant to the current moment, and its main component is inner speech. Building on the previous literature on mental silence and trance states and incorporating inverse source reconstruction advances, the study's objectives were to evaluate differences between trance and mind-wandering states using: (1) electroencephalography (EEG) power spectra at the electrode level, (2) power spectra at the area level (source reconstructed signal), and (3) EEG functional connectivity between these areas (i.e., how they interact). The relationship between subjective trance depths ratings and whole-brain connectivity during trance was also evaluated. Spectral analyses revealed increased delta and theta power in the frontal region and increased gamma in the centro-parietal region during mind-wandering, whereas trance showed increased beta and gamma power in the frontal region. Power spectra at the area level and pairwise comparisons of the connectivity between these areas demonstrated no significant difference between the two states. However, subjective trance depth ratings were inversely correlated with whole-brain connectivity in all frequency bands (i.e., deeper trance is associated with less large-scale connectivity). Trance allows one to enter mentally silent states and explore their neurophenomenological processes. Limitations and future directions are discussed.

Identifying good practices for detecting inter-regional linear functional connectivity from EEG.

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC. Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.

Accounting for endogenous effects in decision-making with a non-linear diffusion decision model.

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

EEG is better left alone.

Automated preprocessing methods are critically needed to process the large publicly-available EEG databases, but the optimal approach remains unknown because we lack data quality metrics to compare them. Here, we designed a simple yet robust EEG data quality metric assessing the percentage of significant channels between two experimental conditions within a 100 ms post-stimulus time range. Because of volume conduction in EEG, given no noise, most brain-evoked related potentials (ERP) should be visible on every single channel. Using three publicly available collections of EEG data, we showed that, with the exceptions of high-pass filtering and bad channel interpolation, automated data corrections had no effect on or significantly decreased the percentage of significant channels. Referencing and advanced baseline removal methods were significantly detrimental to performance. Rejecting bad data segments or trials could not compensate for the loss in statistical power. Automated Independent Component Analysis rejection of eyes and muscles failed to increase performance reliably. We compared optimized pipelines for preprocessing EEG data maximizing ERP significance using the leading open-source EEG software: EEGLAB, FieldTrip, MNE, and Brainstorm. Only one pipeline performed significantly better than high-pass filtering the data.

Brain mechanisms of automated conflict avoidance simulator supervision.

Supervision of automated systems is an ubiquitous aspect of most of our everyday life activities which is even more necessary in high risk industries (aeronautics, power plants, etc.). Performance monitoring related to our own error making has been widely studied. Here we propose to assess the neurofunctional correlates of system error detection. We used an aviation-based conflict avoidance simulator with a 40% error-rate and recorded the electroencephalographic activity of participants while they were supervising it. Neural dynamics related to the supervision of system's correct and erroneous responses were assessed in the time and time-frequency domains to address the dynamics of the error detection process in this environment. Two levels of perceptual difficulty were introduced to assess their effect on system's error detection-related evoked activity. Using a robust cluster-based permutation test, we observed a lower widespread evoked activity in the time domain for errors compared to correct responses detection, as well as a higher theta-band activity in the time-frequency domain dissociating the detection of erroneous from that of correct system responses. We also showed a significant effect of difficulty on time-domain evoked activity, and of the phase of the experiment on spectral activity: a decrease in early theta and alpha at the end of the experiment, as well as interaction effects in theta and alpha frequency bands. These results improve our understanding of the brain dynamics of performance monitoring activity in closer-to-real-life settings and are a promising avenue for the detection of error-related components in ecological and dynamic tasks.

NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data.

To preserve scientific data created by publicly and/or philanthropically funded research projects and to make it ready for exploitation using recent and ongoing advances in advanced and large-scale computational modeling methods, publicly available data must use in common, now-evolving standards for formatting, identifying and annotating should share data. The OpenNeuro.org archive, built first as a repository for magnetic resonance imaging data based on the Brain Imaging Data Structure formatting standards, aims to house and share all types of human neuroimaging data. Here, we present NEMAR.org, a web gateway to OpenNeuro data for human neuroelectromagnetic data. NEMAR allows users to search through, visually explore and assess the quality of shared electroencephalography (EEG), magnetoencephalography and intracranial EEG data and then to directly process selected data using high-performance computing resources of the San Diego Supercomputer Center via the Neuroscience Gateway (nsgportal.org, NSG), a freely available web portal to high-performance computing serving a variety of neuroscientific analysis environments and tools. Combined, OpenNeuro, NEMAR and NSG form an efficient, integrated data, tools and compute resource for human neuroimaging data analysis and meta-analysis. Database URL: https://nemar.org.

A streamable large-scale clinical EEG dataset for Deep Learning.

Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on their data without extensive feature engineering. The availability of large-scale datasets is a crucial aspect of allowing the experimentation of Deep Learning models. We are publishing the first large-scale clinical EEG dataset that simplifies data access and management for Deep Learning. This dataset contains eyes-closed EEG data prepared from a collection of 1,574 juvenile participants from the Healthy Brain Network. We demonstrate a use case integrating this framework, and discuss why providing such neuroinformatics infrastructure to the community is critical for future scientific discoveries.

What if consciousness is not an emergent property of the brain? Observational and empirical challenges to materialistic models.

The nature of consciousness is considered one of science's most perplexing and persistent mysteries. We all know the subjective experience of consciousness, but where does it arise? What is its purpose? What are its full capacities? The assumption within today's neuroscience is that all aspects of consciousness arise solely from interactions among neurons in the brain. However, the origin and mechanisms of qualia (i.e., subjective or phenomenological experience) are not understood. David Chalmers coined the term "the hard problem" to describe the difficulties in elucidating the origins of subjectivity from the point of view of reductive materialism. We propose that the hard problem arises because one or more assumptions within a materialistic worldview are either wrong or incomplete. If consciousness entails more than the activity of neurons, then we can contemplate new ways of thinking about the hard problem. This review examines phenomena that apparently contradict the notion that consciousness is exclusively dependent on brain activity, including phenomena where consciousness appears to extend beyond the physical brain and body in both space and time. The mechanisms underlying these "non-local" properties are vaguely suggestive of quantum entanglement in physics, but how such effects might manifest remains highly speculative. The existence of these non-local effects appears to support the proposal that post-materialistic models of consciousness may be required to break the conceptual impasse presented by the hard problem of consciousness.

Salivary neutrophil sampling feasibility in general population for gene expression analysis.

OBJECTIVE: Human gene expression studies typically rely on peripheral blood samples as a cellular source, however there are numerous situations in which venipuncture is contraindicated. To this end, an oral rinse-based method for collecting salivary neutrophils as a cellular source for gene expression analyses was previously developed and shown in a pilot study with five male participants to yield mRNA expression results comparable to those obtained from peripheral blood samples. The objective of the current study was to characterize the generalizability of the oral rinse-based method by analyzing unpublished RNA quality data obtained through a parent study that collected salivary neutrophil samples using the method from a larger sample size and including both men and women. RESULTS: The 260/280 nm absorbance ratios of the RNA obtained from 48 participants using the oral rinse-based method were within the expected range (average = 1.88 ± 0.16) for the majority of the samples, and no significant differences in RNA quality were found between participants' health, age group, or gender. Together with published data confirming the integrity of RNA obtained using the same method, these results support the feasibility of using this noninvasive method for obtaining samples for human gene expression analyses.

Exploring Personal Development Workshops' Effect on Well-Being and Interconnectedness.

Introduction: Personal development workshops are increasingly popular. This study evaluated the relationships between the measures of well-being, interconnectedness, and extended perception in various workshops and explored which kinds of workshops and individual characteristics predicted changes in these outcomes. Materials and Methods: In a prospective, uncontrolled, within-participant design study, adult participants completed questionnaires and online tasks before and after personal development workshops. Three analyses were conducted: (1) examining the relationships between measures by using only pre-workshop measures using Spearman correlations; (2) exploring change scores pre- to post-workshop and workshop using Wilcoxon signed-rank test; (3) assessing workshop format and content, and individual characteristics as predictors of those change scores multivariate nonparametric regression. The following outcomes were collected: Well-being-Arizona Integrative Outcomes Scale, positive and negative affect, Dispositional Positive Emotions Scale-Compassion subscale, Sleep Quality Scale, Numeric Pain Rating Scale; Interconnectedness-Cloninger Self-Transcendence Scale, Inclusion of Nature in Self and Inclusion of the Other in Self; and Extended perception tasks-Intuition Jar, Quick Remote Viewing, Psychokinesis Bubble, and Time Estimation. The following potential predictor variables were collected: demographic, mental health, psychiatric and meditation history, Single General Self-Rated Health Question, Brief Five-Factor Inventory-10, and the Noetic Experience and Belief Scale. Workshop leaders also selected which format and content characteristics applied to their workshop. Results: Interconnectedness measures were significantly and positively correlated with well-being (ρ: 0.27 to 0.33), positive affect (ρ: 0.20 to 0.27), and compassion (ρ: 0.21 to 0.32), and they were negatively correlated with sleep disturbance (ρ: -0.13 to -0.16) and pain (ρ: -0.11 to -0.16). Extended perception task performance was not correlated with interconnectedness or well-being. General personal development workshops improved subjective interconnectedness, well-being, positive emotion, and compassion, and they reduced sleep disturbances, negative emotion, and pain (all p's < 0.00005). The lecture (p = 0.03), small groups (p = 0.001), pairs (p = 0.01), and discussion (p = 0.03) workshop formats were significant predictors of well-being outcomes. The workshop content categories of meditation (p = 0.0002) and technology tools (p = 0.01) were also predictive of well-being outcomes, with meditation being the most consistent predictor of positive well-being changes. Conscientiousness was the only significant individual characteristic predictor (p = 0.002), although it was associated with increases in some well-being measures and decreases in others. Conclusions: This study provides preliminary evidence for the positive relationship between the subjective sense of interconnectedness and multiple well-being measures and the beneficial effects of some personal development workshops.

Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features.

The success of deep learning in computer vision has inspired the scientific community to explore new analysis methods. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on EEG data. Research remains open on the network architecture and the feature space that is most effective for EEG decoding. This paper compares deep learning using minimally processed EEG raw data versus deep learning using EEG spectral features using two different deep convolutional neural architectures. One of them from Putten et al. (2018) is tailored to process raw data; the other was derived from the VGG16 vision network (Simonyan and Zisserman, 2015) which is designed to process EEG spectral features. We apply them to classify sex on 24-channel EEG from a large corpus of 1,574 participants. Not only do we improve on state-of-the-art classification performance for this type of classification problem, but we also show that in all cases, raw data classification leads to superior performance as compared to spectral EEG features. Interestingly we show that the neural network tailored to process EEG spectral features has increased performance when applied to raw data classification. Our approach suggests that the same convolutional networks used to process EEG spectral features yield superior performance when applied to EEG raw data.