Electroencephalography (EEG) monitors ---by either intrusive or noninvasive electrodes--- time and frequency variations and spectral content of voltage fluctuations or waves, known as brain rhythms, which in some way uncover activity during both rest periods and specific events in which the subject is under stimulus. This is a useful tool to explore brain behavior, as it complements imaging techniques that have a poorer temporal resolution. We here approach the understanding of EEG data from first principles by studying a networked model of excitatory and inhibitory neurons which generates a variety of comparable waves. In fact, we thus reproduce $α$, $β,$ $γ$ and other rhythms as observed by EEG, and identify the details of the respectively involved complex phenomena, including a precise relationship between an input and the collective response to it. It ensues the potentiality of our model to better understand actual mind mechanisms and its possible disorders, and we also describe kind of stochastic resonance phenomena which locate main qualitative changes of mental behavior in (e.g.) humans. We also discuss the plausible use of these findings to design deep learning algorithms to detect the occurence of phase transitions in the brain and to analyse its consequences.
Electroencephalogram (EEG) has been a core tool used in functional neuroimaging in humans for nearly a hundred years. Because it is inexpensive, easy to implement, and noninvasive, it also represents an excellent candidate modality for use in the BCI setting. Nonetheless, a complete understanding of how EEG measurements (voltage fluctuations) relate to information processing in the brain remains somewhat elusive. A deeper understanding of the neuroanatomical underpinnings of the EEG signal may help explain inter-individual variability in evoked and induced potentials, which may improve BCI the…
Neural oscillations are electrical activities of the brain measurable at different frequencies. This paper studies the interaction between the fast and slow processes in the brain. We recorded signals intracranially from the simple Wistar rats, performed the signal processing, and computed the correlation between envelopes of the high-frequency gamma rhythm and a low-frequency ECoG signal. The analysis shows that the low-frequency signal (delta rhythm) modulates the gamma rhythm with a small time delay. Further, we used simple excitable neuron models, namely FitzHugh-Nagumo and Hindmarsh-Rose,…
Mental disorders may exhibit pathological brain rhythms and neurostimulation promises to alleviate of patients' symptoms by modifying these rhythms. Today, most neurostimulation schemes are open-loop, i.e. administer experimental stimulation protocols independent of the patients brain activity which may yield a sub-optimal treatment. We propose a closed-loop feedback control scheme estimating an optimal stimulation based on observed brain activity. The optimal stimulation is chosen according to a user-defined target frequency distribution, which permits frequency tuning of the brain activity i…
While brain rhythms appear fundamental to brain function, why brain rhythms consistently organize into the small set of discrete frequency bands observed remains unknown. Here we propose that rhythms separated by factors of the golden ratio ($ϕ=(1+ \sqrt{5})/2$) optimally support segregation and cross-frequency integration of information transmission in the brain. Organized by the golden ratio, pairs of transient rhythms support multiplexing by reducing interference between separate communication channels, and triplets of transient rhythms support integration of signals to establish a hierarch…
The brain produces rhythms in a variety of frequency bands. Some are likely by-products of neuronal processes; others are thought to be top-down. Produced entirely naturally, these rhythms have clearly recognizable beats, but they are very far from periodic in the sense of mathematics. They produce signals that are broad-band, episodic, wandering in magnitude, in frequency and in phase; the rhythm comes and goes, degrading and regenerating. Rhythms with these characteristics do not match standard dynamical systems paradigms of periodicity, quasi-periodicity, or periodic motion in the presence …
EEG Entrainment to Non-Speech Rhythmic Patterns Associated with Dimensions of Language Performance
University of Granada（格拉纳达大学）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究所）；Research Institute in Information and Communications Technology (CITIC-UGR)（信息与通信技术研究所）；University of Málaga（马拉加大学）
Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-Computer Interface (BCI) technology. The recent progress in deep learning has boosted the study area of synthesizing images from brain signals using Generative Adversarial Networks (GAN). In this work, we have proposed a framework for synthesizing the images from the brain activity recorded by an electroencephalogram (EEG) using small-size EEG datasets. This brain activity is recorded from the subject's head scalp using EEG when they ask to visualiz…
Mental disorders (MD) are among the top most demanding challenges in world-wide health. According to the World Health Organization, the burden of MDs continues to grow with significant impact on health and major social and human rights. A large number of MDs exhibit pathological rhythms, which serve as the disorders characteristic biomarkers. These rhythms are the targets for neurostimulation techniques. Open-loop neurostimulation employs stimulation protocols, which are rather independent of the patients health and brain state in the moment of treatment. Most alternative closed-loop stimulati…
Our current understanding of brain rhythms is based on quantifying their instantaneous or time-averaged characteristics. What remains unexplored, is the actual structure of the waves -- their shapes and patterns over finite timescales. To address this, we used two independent approaches to link wave forms to their physiological functions: the first is based on quantifying their consistency with the underlying mean behavior, and the second assesses "orderliness" of the waves' features. The corresponding measures capture the wave's characteristic and abnormal behaviors, such as atypical periodic…
In recent years, several studies have tended to show a respiratory drive in numerous brain areas so that the respiratory rhythm could be considered as a master clock promoting communication between distant brain areas. However, outside of the olfactory system it is not known if respiration-related oscillation (RRo) could exist in the membrane potential (MP) of neurons neither if it can structure spiking discharge. To fill this gap, we co-recorded MP and LFP activities in different non-olfactory brain areas: median prefrontal cortex (mPFC), primary somatosensory cortex (S1), primary visual cort…
The analysis of brain connectivity aims to understand the emergence of functional networks into the brain. This information can be used in the process of electroencephalographic (EEG) signal analysis and classification for a braincomputer interface (BCI). These systems provide an alternative channel of communication and control to people with motor impairments. In this article, four strategies for using the brain connectivity in a BCI environment as a tool to obtain a deeper understanding of the cerebral mechanisms are proposed, with the principal aim of developing a scheme oriented to neuro-r…
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Motor imagery EEG (MI-EEG) is a kind of most widely focused EEG signals, which reveals a subjects movement intentions without actual actions. Despite the extensive research of MI-EEG in recent years, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals (e.g., low signal noise ratio and incom…
Recent studies have shown that multi-modeling methods can provide new insights into the analysis of brain components that are not possible when each modality is acquired separately. The joint representations of different modalities is a robust model to analyze simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI). Advances in precision instruments have given us the ability to observe the spatiotemporal neural dynamics of the human brain through non-invasive neuroimaging techniques such as EEG & fMRI. Nonlinear fusion methods of streams can extract …
Neurofeedback is a non-invasive brain training with long-term medical and non-medical applications. Despite the existence of several emotion regulation studies using neurofeedback, further investigation is needed to understand interactions of the brain regions involved in the process. We implemented EEG neurofeedback with simultaneous fMRI using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. The results showed increased activity of prefrontal, occipital, parietal, and limbic regions and increased functional connectivity between prefrontal, …
Noninvasive brain stimulation and neuroimaging have revolutionized human neuroscience, with a multitude of applications including diagnostic subtyping, treatment optimization, and relapse prediction. It is therefore particularly relevant to identify robust and clinically valuable brain biomarkers linking symptoms to their underlying neural mechanisms. Brain biomarkers must be reproducible (i.e., have internal reliability) across similar experiments within a laboratory and be generalizable (i.e., have external reliability) across experimental setups, laboratories, brain regions, and disease sta…
We here study a network of synaptic relations mingling excitatory and inhibitory neuron nodes that displays oscillations quite similar to electroencephalogram (EEG) brain waves, and identify abrupt variations brought about by swift synaptic mediations. We thus conclude that corresponding changes in EEG series surely come from the slowdown of the activity in neuron populations due to synaptic restrictions. The latter happens to generate an imbalance between excitation and inhibition causing a quick explosive increase of excitatory activity, which turns out to be a (first-order) transition among…
Recently, we reported an emotion self-regulation study (Zotev et al., 2020), in which patients with major depressive disorder (MDD) used simultaneous real-time fMRI and EEG neurofeedback (rtfMRI-EEG-nf) to upregulate two fMRI and two EEG activity measures, relevant to MDD. The target measures included fMRI activities of the left amygdala and left rostral anterior cingulate cortex, and frontal EEG asymmetries in the alpha band (FAA) and high-beta band (FBA). Here we apply the exact low resolution brain electromagnetic tomography (eLORETA) to investigate EEG source activities during the rtfMRI-E…
Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. Recently, light-weight and portable EEG wearable devices based on low-density montages have increased the convenience and usability of BCI applications. However, loss of EEG decoding performance is often inevitable due to reduced number of electrodes and coverage of scalp regions of a low-density EEG montage. To address this issue, we introduce knowledge distillation (KD), a learning mechanism d…
Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
Univ. of Milan（米兰大学）；Università Campus Bio-Medico di Roma（罗马生物医学大学）；Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma（意大利国家核物理研究所罗马分部）；Center for Data Science, New York University（纽约大学数据科学中心）；Institute for Advanced Simulation (IAS-6), Jülich Research Centre（于利希研究中心高级模拟研究所（IAS-6））
