The link between the cortical and cardiovascular systems is garnering increased attention due to its potential to offer valuable insights into brain and heart function coupling. Current joint analysis methodologies largely involve invasive or high-cost neuroimaging methods. EEG and ECG/PPG, however, provide non-invasive, cost-effective, and portable alternatives enabling broader data collection in both laboratory and clinical settings. However, the analysis of these biosignals is challenging for scalable applications due to their complex nature. Existing research and tools often lack consensus in processing and statistical methodologies, easy-to-use user interface, or batch processing capacity of large datasets, impeding reproducibility. A further void exists in standardized methods for EEG and heart-rate variability (HRV) feature extraction, undermining clinical diagnostics or the robustness of machine learning models. We introduce the BrainBeats toolbox in response to these challenges, an open-source EEGLAB plugin providing an suite of signal processing and feature-extraction functions adhering to current guidelines and recommendations. The toolbox integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO); 2) EEG and HRV feature extraction; 3) Automated removal of heart artifacts from EEG signals. Accompanied by sample data and guidance, BrainBeats aims to facilitate brain-heart interplay research and reproducibility. This open-source toolbox offers a valuable resource for clinicians and researchers studying brain-heart interactions and can be tailored to unique research needs.
Machine learning (ML) is revolutionizing the field of biosignals processing and classification. Feature-based and feature-free (deep learning) ML perform equally well on MEEG (electroencephalography and magnetoencephalography) data classification problems. Yet, feature-based ML approaches dominantly rely on traditional, linear MEEG measures such as power spectral density, potentially missing useful information about the neural time series. There is a need for more advanced features that can capture the nonlinearity, complexity, and irregularities of such signals. Entropy-based measures are promising techniques to fill this gap but are difficult to apply to MEEG signals because of the variety of algorithms and parameters available, while user-friendly tools and methodological guidelines are lacking. Hence, we have developed a set of MEEG tools to fill that gap, where we gathered multiple entropy measures, tailored them for MEEG signal, and packaged them in an open-source EEGLAB plugin with a graphical user interface (GUI). We hope this newly accessible framework will help popularize the use of entropy measures with MEEG data, their cross-comparison, and their use as a feature for ML models.
The nature of consciousness is considered one of the most perplexing and persistent mysteries in science. 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 “nonlocal” properties are vaguely suggestive of quantum entanglement in physics, but how such effects might manifest remains highly speculative. The existence of these nonlocal 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.
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