There are well-documented examples of sudden switches in human cognitive states, including visual illusions. Cognitive switches, however, are much more common than often assumed. In fact, recent research shows that abrupt switches happen several times per second in the human mind. It is of interest to explore the neural mechanisms which may lead to the observed cognitive switches and their significance in intelligent behaviors. In this work, first we briefly summarize the state-of-art of detecting cognitive switches and describe the corresponding neural processes. We argue that rapid switches between relatively stable states are inevitable attributes of brain dynamics and they are the very source of intelligent behaviors.The key question is whether the switching patterns of brain dynamics and human cognition are obscure evolutionary/historical artifacts which should be ignored in engineering designs, or they are crucial manifestations of intelligence to be incorporated in AI systems as well. Our approach is based on the observation that intelligence is neither local nor global, rather it is a delicate balance between integration and fragmentation tendencies, and sequential switches are the expressions of such competing tendencies. This motivates the development of practically useful modeling tools for intelligence using switching oscillatory dynamics. A practical model is based on percolation theory and phase transitions over random graphs, which provide a powerful tool for rigorously describe neural processes as intermittent phase transitions over the cerebral cortex. The introduced results lead to recommendations to use sequential switching patterns as the underlying modus operandi of sustainable AI systems, which can become true partners of human beings in future endeavors.

EEG-based identification of brain states has advanced significantly, enabling cognitive monitoring during daily tasks. Brain dynamics can be studied as a Markovian, Semi-Markovian, or Non-Markovian stochastic process, prescribed by Transition Probability Matrices derived from EEG measurements. This study models brain dynamics as discrete Markov chains using second-by-second dominant frequencies derived from the power spectrum of high-density array EEG signals. By analyzing transition and limiting probabilities across modalities, we reveal distinct neural signatures that differentiate engaged from meditative states. Though preliminary, these findings highlight the method’s potential to enhance BCI systems and deepen our understanding of brain dynamics, brain health and the benefits of meditation in future studies.

Scalp electroencephalography (EEG) provides a practical tool for the identification and characterization of various brain states, including healthy and diseased conditions. We measure brain dynamics on the scalp via a HydroCel Geodesic Sensor Net, 128 electrodes dense-array EEG. We compute the pragmatic information index (PI) by Hilbert analysis of the EEG signals of 20 healthy participants. We compare 6 task modalities, combining different audio-visual stimuli, leading to various mental states, predominantly relaxed versus engaged. We analyze PI values to classify different brain states. We show significant differences between the measured neural signatures depending on the task modalities, based on both qualitative and quantitative analysis. The results can help to develop tools for medical diagnostics of stress-related mental conditions.

The presence of respiration-locked sensory cortical activity has been documented in various mammalian species in recent years, including cognitively-relevant gamma oscillations (30-80 Hz). This work builds on previous evidence suggesting that respiration has a direct influence on oscillatory activity in human sensory, motor and association cortical areas. The entrainment of cortical activity patterns by respiratory phase suggests a direct influence of respiration on cognitive processing, which represents a possible neuronal mechanism behind the well-documented but unexplained effects of respiratory exercises on emotional and cognitive functions. We explore possible interpretation of those findings in the context of the cinematic view of cognition, in particular cortical phase transitions. In addition to traditional Fourier-based correlational analysis of cortical signals, we introduce Hilbert analysis, which allows to monitor rapid phase synchronization-desynchronization transitions. Our results support the hypothesis that respiration-locked cortical activity is linked to phase transitions in the cortex, measured by discontinuities of the instantaneous phase of the analytic signal determined by the Hilbert transform. Taken together, these findings suggest that respiration acts as master clock exerting a subtle but unfailing synchronizing influence on the temporal organization of dynamic cortical activity patterns and the cognitive processes they control.

Non-invasive brain imaging techniques are popular tools for monitoring the cognitive state of human participants. This work builds on our previous studies using the HydroCel Geodesic Sensor Net, 256 electrodes dense-array electro-encephalography (EEG). The studies analyze dominant frequencies of temporal power spectral densities for each of the EEG electrodes. The experiments involve three modalities: Meditation, Math Mind, and (c) Open Eyes condition. Here we perform an analysis of the Shannon entropy index and Pearson's skewness coefficient in order to test their fitness to classify different brain states. The results help to develop a comprehensive methodology to understand brain dynamics.

Cognition and brain dynamics manifests a delicate balance between processes at various temporal and spatial scales. The conversion between microscopic neural pulses and waves of mesoscopic activity of neural masses is a crucial research subject. In this work we analyze the hierarchy of neural structures and dynamics, with an emphasis on pulse-wave-pulse conversion. Our models describe pulse-to-wave conversion, as well as the feedback of action potentials on neurons in the recovery mode. We study the behavior of neural populations in the background state of activity, as well as under perturbations due to external stimuli. Simulations results are employed for the interpretation of electrocorticogram data obtained with rabbits trained using classical conditioning paradigm.

Previous experimental studies on rabbits using electrocorticograms (ECoGs) over the cortical surface indicate spatio-temporal dynamics in the form of amplitude modulation (AM) patterns, which intermittently collapse at theta rates and give rise to rapidly propagating phase modulated (PM) patterns. The observed dynamics have been shown to be of cognitive relevance carrying useful information on the meaning of sensory information perceived by the subject. We have extended these studies to human scalp EEG measurements, which show evidence that cognitively relevant AM and PM patterns are observable by non-intrusive experimental techniques as well. The present work develops experimental techniques for studying cognitively relevant spatio-temporal neural dynamics using a high-density EEG array. Theoretical considerations indicate that the required spatial resolution to detect and categorize amplitude and phase patterns should be in the range of 3–5 mm. A prototype 1-dimensional array (MINDO-48S) has been developed, which has 48 electrodes in a flexible linear array of 5 mm spacing. The present work focuses on the extraction of broadly distributed spatio-temporal patterns, which carry cognitively relevant information. Preliminary analysis of the signal-to-noise ratio indicates that the sensitivity of the experiment allows the predicted AM patterns to be measured.

Signals measured by electroencephalogram (EEG) arrays were decomposed using Hubert Transformations to produce the spatial amplitude and phase modulation (AM and PM) patterns. Spatial PM patterns intermittently exhibit synchronization-desynchronization transitions. During desynchronization, the spatial PM patterns intermittently conform to conic shapes. These phase cones mark the onset of emergent AM patterns, which carry cognitive content. In this work, various temporal band pass filters were applied to study the frequency dependence of phase cones in the beta-gamma range (10-40 Hz). The results are interpreted in the context of the cognitive cycle of knowledge generation.

We apply Hilbert transforms to the analysis of phase relationship in elecrocoticogram (ECoG) signals in order to explore a set of meaningful information theoretic measures. This analysis leads to a methodology to derive meaning from experimentally observed brain dynamics under various states induced by sensory stimuli. We explore the possibility to represent periods of habituation and learning based on instantaneous frequency signals and introducing a new set of parameters, based on the concept of pragmatic information.