Brain Waves Research Articles

Rhythm Facilitates Auditory Working Memory via Beta-Band Encoding and Theta-Band Maintenance.

Rhythm, as a prominent characteristic of auditory experiences such as speech and music, is known to facilitate attention, yet its contribution to working memory (WM) remains unclear. Here, human participants temporarily retained a 12-tone sequence presented rhythmically or arrhythmically in WM and performed a pitch change-detection task. Behaviorally, while having comparable accuracy, rhythmic tone sequences showed a faster response time and lower response boundaries in decision-making. Electroencephalographic recordings revealed that rhythmic sequences elicited enhanced non-phase-locked beta-band (16 Hz-33 Hz) and theta-band (3 Hz-5 Hz) neural oscillations during sensory encoding and WM retention periods, respectively. Importantly, the two-stage neural signatures were correlated with each other and contributed to behavior. As beta-band and theta-band oscillations denote the engagement of motor systems and WM maintenance, respectively, our findings imply that rhythm facilitates auditory WM through intricate oscillation-based interactions between the motor and auditory systems that facilitate predictive attention to auditory sequences.

Emotion Detection using EEG Signals in Image and Video Processing: A Survey

Brain serves as the body's processing of knowledge and management centre. The central nervous system directly produces ElectroEncephaloGram (EEG) physiological signals, which are strongly associated with human emotions. In the upcoming years, there will be an increase in interest for identifying emotion using brain waves by EEG (ElectroEncephaloGraphy) signals. It takes efficient and effective signal processing and feature extraction techniques for detection of emotions from human biological brain signals. Current approaches gather valuable information from a fixed number of ElectroEncephaloGraphy (EEG) channels utilizing variety of methodologies. This work analyses the different difficulties and problems associated with EEG signals for emotion identification and provides a comprehensive summary of several contemporary approaches. Pre-processing, feature extraction, and categorization are the first steps in the process of recognizing emotions from EEG signals. The main goal of this survey is to sought and enhance brain signal-based emotion detection ability by comparing all novel and adaptive channel selection technique that recognize the distinct changes in brain activities that varies between individuals and emotional states.

When sensory input meets spontaneous brain activity

A recent study by Wu, Podvalny, and colleagues investigated how ongoing spontaneous brain activity interacts with sensory input and shapes conscious perception. It reports diverse effects of prestimulus activity in several key networks, revealing new roles of the prefrontal cortex and the default mode network in perception and consciousness.

Abnormal brain-heart electrophysiology in mild and severe orthostatic hypotension.

This study investigated the changes in cardiocerebral electrophysiology in patients with mild orthostatic hypotension (MOH) and severe orthostatic hypotension (SOH) and their relationship with the severity of orthostatic hypotension, psychiatric symptoms, and cognitive dysfunction. This study included 72 nonorthostatic hypotension (NOH), 17 with MOH, and 11 with SOH. Seated resting-state heart rate variability (HRV) and quantitative electroencephalogram parameters were synchronized and recorded. HRV measures in the time and frequency domains were analyzed, along with the peak frequency and power of the brain waves. Abnormal neuronal activity was found in FP1 in patients with MOH, whereas it was more widespread in FP1, FP2, and O2 in patients with SOH (P < 0.05). Cardiac and cerebral electrophysiological abnormalities were significantly associated with orthostatic hypotension severity, psychiatric symptoms, and cognitive dysfunction. Abnormal EEG activity in patients are mainly manifested in the prefrontal and occipital lobes, especially in patients with SOH. These results may help patients to better understand the mechanisms underlying orthostatic hypotension severity and psychiatric and cognitive impairment in orthostatic hypotension.

Does slow oscillation-spindle coupling contribute to sleep-dependent memory consolidation? A Bayesian meta-analysis.

The active system consolidation theory suggests that information transfer between the hippocampus and cortex during sleep underlies memory consolidation. Neural oscillations during sleep, including the temporal coupling between slow oscillations (SO) and sleep spindles (SP), may play a mechanistic role in memory consolidation. However, differences in analytical approaches and the presence of physiological and behavioral moderators have led to inconsistent conclusions. This meta-analysis, comprising 23 studies and 297 effect sizes, focused on four standard phase-amplitude coupling measures including coupling phase, strength, percentage, and SP amplitude, and their relationship with memory retention. We developed a standardized approach to incorporate non-normal circular-linear correlations. We found strong evidence supporting that precise and strong SO-fast SP coupling in the frontal lobe predicts memory consolidation. The strength of this association is mediated by memory type, aging, and dynamic spatio-temporal features, including SP frequency and cortical topography. In conclusion, SO-SP coupling should be considered as a general physiological mechanism for memory consolidation.

Beta bursts in the parkinsonian cortico-basal ganglia network form spatially discrete ensembles

Defining spatial synchronisation of pathological beta oscillations is important, given that many theories linking them to parkinsonian symptoms propose a reduction in the dimensionality of the coding space within and/or across cortico-basal ganglia structures. Such spatial synchronisation could arise from a single process, with widespread entrainment of neurons to the same oscillation. Alternatively, the partially segregated structure of cortico-basal ganglia loops could provide a substrate for multiple ensembles that are independently synchronized at beta frequencies. Addressing this question requires an analytical approach that identifies groups of signals with a statistical tendency for beta synchronisation, which is unachievable using standard pairwise measures. Here, we utilized such an approach on multichannel recordings of background unit activity (BUA) in the external globus pallidus (GP) and subthalamic nucleus (STN) in parkinsonian rats. We employed an adapted version of a principle and independent component analysis-based method commonly used to define assemblies of single neurons (i.e., neurons that are synchronized over short timescales). This analysis enabled us to define whether changes in the power of beta oscillations in local ensembles of neurons (i.e., the BUA recorded from single contacts) consistently covaried over time, forming a “beta ensemble”. Multiple beta ensembles were often present in single recordings and could span brain structures. Membership of a beta ensemble predicted significantly higher levels of short latency (<5 ms) synchrony in the raw BUA signal and phase synchronisation with cortical beta oscillations, suggesting that they comprised clusters of neurons that are functionally connected at multiple levels, despite sometimes being non-contiguous in space. Overall, these findings suggest that beta oscillations do not comprise of a single synchronisation process, but rather multiple independent activities that can bind both spatially contiguous and non-contiguous pools of neurons within and across structures. As previously proposed, such ensembles provide a substrate for beta oscillations to constrain the coding space of cortico-basal ganglia circuits.

EEG During Dynamic Facial Emotion Processing Reveals Neural Activity Patterns Associated with Autistic Traits in Children.

Altered brain connectivity and atypical neural oscillations have been observed in autism, yet their relationship with autistic traits in non-clinical populations remains underexplored. Here, we employ electroencephalography (EEG) to examine functional connectivity, oscillatory power, and broadband aperiodic activity during a dynamic facial emotion processing (FEP) task in 101 typically developing children aged 4-12 years. We investigate associations between these electrophysiological measures of brain dynamics and autistic traits as assessed by the Social Responsiveness Scale, 2nd Edition (SRS-2). Our results revealed that increased FEP-related connectivity across theta (4-7 Hz) and beta (13-30 Hz) frequencies correlated positively with higher SRS-2 scores, predominantly in right-lateralized (theta) and bilateral (beta) cortical networks. Additionally, a steeper 1/f-like aperiodic slope (spectral exponent) across fronto-central electrodes was associated with higher SRS-2 scores. Greater aperiodic-adjusted theta and alpha oscillatory power further correlated with both higher SRS-2 scores and steeper aperiodic slopes. These findings underscore important links between FEP-related brain dynamics and autistic traits in typically developing children. Future work could extend these findings to assess these EEG-derived markers as potential mechanisms underlying behavioural difficulties in autism.

Analysis of the dynamical behavior of discrete memristor-coupled scale-free neural networks

The synchronization of neural networks is crucial for neural information processing and represents a key feature of various functional brain diseases. Memristors are ideal electronic components for mimicking biological synapses, among which discrete memristors have the advantage of fast computing speed and are often used in memristor-based neural networks. For these reasons, this paper proposes a novel discrete memristor-coupled Scale-Free neural network (DMSNN). Phase diagrams and time series of membrane potential are employed to analyze the firing pattern coexistence of individual neurons in the network. Furthermore, Spatiotemporal patterns, heat maps of the Spearman correlation coefficient matrix and the values of neuron membrane potential at a particular time point are adopted to declare the spatio-temporal dynamics of the complex neural network, encompassing asynchronization, chimeric state, synchronization and synchronization transition. The study also identifies the phenomenon of topology-induced coexistence and elucidates the underlying reasons for the emergence of chimeric states in the DMSNN as the coupling strength increases. Finally, a hardware implementation platform is constructed using a highly integrated SSD202 processor to validate the accuracy of the DMSNN. The results are consistent with the numerical simulations.

Unified theory of alpha, mu, and tau rhythms via eigenmodes of brain activity.

A compact description of the frequency structure and topography of human alpha-band rhythms is obtained by use of the first four brain activity eigenmodes previously derived from corticothalamic neural field theory. Just two eigenmodes that overlap in frequency are found to reproduce the observed topography of the classical alpha rhythm for subjects with a single, occipitally concentrated alpha peak in their electroencephalograms. Alpha frequency splitting and relative amplitudes of double alpha peaks are explored analytically and numerically within this four-mode framework using eigenfunction expansion and perturbation methods. These effects are found to result primarily from the different eigenvalues and corticothalamic gains corresponding to the eigenmodes. Three modes with two non-overlapping frequencies suffice to reproduce the observed topography for subjects with a double alpha peak, where the appearance of a distinct second alpha peak requires an increase of the corticothalamic gain of higher eigenmodes relative to the first. Conversely, alpha blocking is inferred to be linked to a relatively small attention-dependent reduction of the gain of the relevant eigenmodes, whose effect is enhanced by the near-critical state of the brain and whose sign is consistent with inferences from neural field theory. The topographies and blocking of the mu and tau rhythms within the alpha-band are explained analogously via eigenmodes. Moreover, the observation of three rhythms in the alpha band is due to there being exactly three members of the first family of spatially nonuniform modes. These results thus provide a simple, unified description of alpha band rhythms and enable experimental observations of spectral structure and topography to be linked directly to theory and underlying physiology.

Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neuromodulation.

Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. We used offline simulation to evaluate the performance of three algorithms (endpoint-corrected Hilbert Transform, Hilbert Transform and phase mapping) on three rhythmic biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha and human essential tremor). First, we found that algorithm performance was more strongly influenced by signal amplitude and frequency variation compared with signal to noise ratio. Second, our simulations showed that the size of the data window for phase estimation was critical for the performance of FFT-based algorithms, where the optimal data window corresponds to the period of the oscillation. We validated this prediction with real time phase detection of hippocampal theta oscillations in freely behaving rats performing spatial navigation. Our findings define the relationship between signal properties and algorithm performance and provide a convenient method for optimizing FFT-based phase detection algorithms.

Using Dual-Coil TMS-EEG to Probe Bilateral Brain Mechanisms in Healthy Aging and Mild Cognitive Impairment.

A widespread observation in the cognitive neuroscience of aging is that older adults show a more bilateral pattern of task-related brain activation. These observations are based on inherently correlational approaches. The current study represents a targeted assessment of the role of bilaterality using repetitive transcranial magnetic stimulation (rTMS). We used a novel bilateral TMS-stimulation paradigm, applied to a group of healthy older adults (hOA) and older adults with mild cognitive impairment (MCI), with two aims: First, to elucidate the neurophysiological effects of bilateral neuromodulation, and second to provide insight into the neurophysiological basis of bilateral brain interactions. Electroencephalography (EEG) was recorded while participants received six forms of transcranial magnetic stimulation (TMS): unilateral and bilateral rTMS trains at an alpha (8 Hz) and beta (18 Hz) frequency, as well as two sham conditions (unilateral, bilateral) mimicking the sounds of TMS. First, time-frequency analyses of oscillatory power induced by TMS revealed that unilateral beta rTMS elicited rhythmic entrainment of cortical oscillations at the same beta-band frequency. Second, both bilateral alpha and bilateral beta stimulation induced a widespread reduction of alpha power. Lastly, healthy older adults showed greater TMS-related reductions in alpha power in response to bilateral rTMS compared to the MCI cohort. Overall, these results demonstrate frequency-specific responses to bilateral rTMS in the aging brain, and provide support for inhibitory models of hemispheric interaction across multiple frequency bands.

Cortical networks of parkinsonian gait: a metabolic and functional connectivity study.

Locomotion is an automated voluntary movement sustained by coordinated neural synchronization across a distributed brain network. The cerebral cortex is central for adapting the locomotion pattern to the environment and alterations of cortical network dynamics can lead to gait impairments. Gait problems are a common symptom with a still unclear pathophysiology and represent an unmet therapeutical need in Parkinson's disease. Little is known about the cortical network dynamics of locomotor control in these patients. We studied the cortical basis of parkinsonian gait by combining metabolic brain imaging with high-density EEG recordings and kinematic measurements performed at rest and during unperturbed overground walking. We found significant changes in functional connectivity between frontal, sensorimotor, and visuomotor cortical areas during walking as compared to resting. Specifically, hypokinetic gait was associated with poor information flow from the supplementary motor area (SMA) to precuneus and from calcarine to lingual gyrus, as well as high information flow from calcarine to cuneus. Our findings support a role for visuomotor integration processes in PD-related hypokinetic gait and suggest that reinforcing visual information may act as a compensatory strategy to allow SMA-mediated feedforward locomotor control in PD.

Comparing the effects of resistance and aerobic exercise on mood-related symptoms and EEG activity in young healthy adults: A non-randomized pilot study

Background and aimsExercise offers several benefits in combating mood-related symptoms in humans. Participation in various types of physical activity improves brain and mental health. However, the different effects and mechanisms depending on the type of exercise on an individual's mood and brain remain unclear. This pilot trial aimed to reveal the feasibility of the effects of resistance and aerobic exercise on mood-related symptoms and explain the different patterns of brain waves. MethodsThis 15-week single-arm pre-test/post-test trial including 3 measurement periods was held in Seoul, Republic of Korea. Overall, 18 healthy participants aged 19–29 years were recruited. After 6 weeks of respective aerobic and resistance exercise, anxiety/depression-related questionnaire scores and electroencephalography (EEG) changes were measured (single blinded outcome assessment). Specifically, changes in frontal alpha asymmetry (FAA), absolute band power of beta waves, and theta waves were measured in the prefrontal and frontal regions, which play a critical role in regulating mood and cognition. ResultsResistance exercise resulted in a significant reduction in Beck Depression Inventory-II scores (p = 0.002, effect size = 0.76) while significant improvement was shown in Beck Anxiety Inventory scores (p = 0.01, effect size = 0.77) after aerobic exercise. In regarding EEG analysis, ten participants completed the assessment. After resistance exercise, there was a significant elevation in frontal beta waves (p = 0.01, effect size = 0.81). Aerobic exercise did not result in significant EEG changes. There were no adverse events related to study participation. ConclusionOur findings suggest that resistance and aerobic exercises may be feasible for the alleviation of mood-related symptoms and EEG activities. As our pilot trial has a small sample size and did not include a control group, future research that accurately reflects the effects of different types of exercises on mood symptoms and changes of EEG activities is required.

SpectroCVT-Net: A convolutional vision transformer architecture and channel attention for classifying Alzheimer’s disease using spectrograms

Dementia arises from various brain-affecting diseases and injuries, with Alzheimer’s disease being the most prevalent, impacting around 55 million people globally. Current clinical diagnosis often relies on biomarkers indicative of Alzheimer’s distinctive features. Electroencephalography (EEG) serves as a cost-effective, user-friendly, and safe biomarker for early Alzheimer’s detection. This study utilizes EEG signals processed with Short-Time Fourier Transform (STFT) to generate spectrograms, facilitating visualization of EEG signal properties. Leveraging the Brainlat database, we propose SpectroCVT-Net, a novel convolutional vision transformer architecture incorporating channel attention mechanisms. SpectroCVT-Net integrates convolutional and attention mechanisms to capture local and global dependencies within spectrograms. Comprising feature extraction and classification stages, the model enhances Alzheimer’s disease classification accuracy compared to transfer learning methods, achieving 92.59 ± 2.3% accuracy across Alzheimer’s, healthy controls, and behavioral variant frontotemporal dementia (bvFTD). This article introduces a new architecture and evaluates its efficacy with unconventional data for Alzheimer’s diagnosis, contributing: SpectroCVT-Net, tailored for EEG spectrogram classification without reliance on transfer learning; a convolutional vision transformer (CVT) module in the classification stage, integrating local feature extraction with attention heads for global context analysis; Grad-CAM analysis for network decision insight, identifying critical layers, frequencies, and electrodes influencing classification; and enhanced interpretability through spectrograms, illuminating key brain wave contributions to Alzheimer’s, frontotemporal dementia, and healthy control classifications, potentially aiding clinical diagnosis and management.

EFFECTS OF INAUDIBLE THETA BINAURAL BEAT ON BRAIN WAVES

Auditory binaural beats (BBs) stimulation is a method used to support the assumption that artificial external stimuli can generate specific brain waves. This study aimed to examine the effects of BBs that induce brain waves only based on a different frequency using an inaudible baseline frequency. The experiment was conducted among 18 adults in their 20s (11 male and 7 female). A baseline frequency of 18,000[Formula: see text]Hz and BBs with a difference frequency of 5[Formula: see text]Hz was presented, and the changes in electroencephalogram were observed. Power spectrum analysis was performed, and the absolute power of the theta-band frequency range (4–7[Formula: see text]Hz) was averaged. Absolute theta power was compared between phases without binaural beats (non-BBs) and with binaural beats ([Formula: see text]-BBs) using the Wilcoxon signed-rank test. Theta waves in [Formula: see text]-BBs significantly increased in most brain regions (central, parietal, temporal, and occipital) compared to those in non-BBs. This study is significant because it focused solely on the effects of the BBs-inducing theta waves using a frequency difference, excluding the effect of sound perception.

Anti-neuroinflammatory effects of the KIOM -patented Polygonum multiflorum maximized root tuber against LPS-stimulated BV2 Cells

The main pathological mechanism of neurodegeneration is neuroinflammation. It is known that the persistent neuroinflammatory response is harmful by causing secondary nerve tissue damage. Meanwhile, P. multiflorum is a traditional oriental medicinal herb. It has been used as a hematopoietic agent and is used to treat a variety of diseases and conditions. The aim of the present study was to compare the anti-inflammatory efficacy between the commonly available P. multiflorum (C1) and the KIOM-patented in vitro-propagated P. multiflorum (K1), which had higher content of active ingredients and biomass, using culture and cultivation conditions of LPS-induced neuroinflammation. After stimulation with LPS and treatment with C1 and K1 in mouse microglial BV-2 cells, nitric oxide (NO) production, pro-inflammatory cytokine secretion, inducible NO synthase (iNOS) expression, MAPK phosphorylation and transcription factor activity were assessed. We examined the antioxidant effect using DPPH and production of nitric oxide (NO). C1 and K1 suppressed the expression of iNOS and COX-2 and the production of pro-inflammatory cytokines. Furthermore, we determined the levels of inflammatory mediators, such as interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α and mitogen-activated protein kinases and IκBα via Western blotting to understand the regulating mechanisms. Additionally, C1 and K1 also inhibited the activation of p38 and nuclear factor-kappa B (NF-κB) in LPS-stimulated BV2 cells. In all experimental results, excellent anti-neuroinflammatory effects were confirmed at a lower dose in K1 than in C1, which is believed to be due to the increased biomass. Therefore, K1 is expected to be more effective than C1 and can be applied more broadly in the development of prevention and treatment of various inflammatory-mediated neurodegenerative diseases.

Influence of Large-Scale Brain State Dynamics on the Evoked Response to Brain Stimulation.

Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG), we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical microstate C was found to induce a greater evoked response for up to 80 milliseconds compared to other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms.Significance Statement Previous findings suggested local spontaneous neural oscillations can influence neurophysiological response to stimuli. However, beyond the local oscillatory activity, the brain state is rapidly fluctuating on a millisecond time resolution on a global spatial scale. We investigated whether these rapid global fluctuations influenced the evoked response to brain stimulation. We used combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) to stimulate the prefrontal cortex while recording global brain states via EEG microstates. The evoked neurophysiological response was significantly larger when stimulation was applied after the occurrence of a specific global brain state (i.e., microstate C) linked to mind-wandering. The finding was selective to active stimulation, replicated for the same individuals in a repeated session, and replicated in an entirely independent dataset.

Prefrontal Excitation/ Inhibition Balance Supports Adolescent Enhancements in Circuit Signal to Noise Ratio.

The development and refinement of neuronal circuitry allow for stabilized and efficient neural recruitment, supporting adult-like behavioral performance. During adolescence, the maturation of PFC is proposed to be a critical period (CP) for executive function, driven by a break in balance between glutamatergic excitation and GABAergic inhibition (E/I) neurotransmission. During CPs, cortical circuitry fine-tunes to improve information processing and reliable responses to stimuli, shifting from spontaneous to evoked activity, enhancing the SNR, and promoting neural synchronization. Harnessing 7T MR spectroscopy and EEG in a longitudinal cohort (N = 164, ages 10-32 years, 283 neuroimaging sessions), we outline associations between age-related changes in glutamate and GABA neurotransmitters and EEG measures of cortical SNR. We find developmental decreases in spontaneous activity and increases in cortical SNR during our auditory steady state task using 40 Hz stimuli. Decreases in spontaneous activity were associated with glutamate levels in DLPFC, while increases in cortical SNR were associated with more balanced Glu and GABA levels. These changes were associated with improvements in working memory performance. This study provides evidence of CP plasticity in the human PFC during adolescence, leading to stabilized circuitry that allows for the optimal recruitment and integration of multisensory input, resulting in improved executive function.

Imaging high-frequency voltage dynamics in multiple neuron classes of behaving mammals.

Fluorescent genetically encoded voltage indicators report transmembrane potentials of targeted cell-types. However, voltage-imaging instrumentation has lacked the sensitivity to track spontaneous or evoked high-frequency voltage oscillations in neural populations. Here we describe two complementary TEMPO voltage-sensing technologies that capture neural oscillations up to ~100 Hz. Fiber-optic TEMPO achieves ~10-fold greater sensitivity than prior photometry systems, allows hour-long recordings, and monitors two neuron-classes per fiber-optic probe in freely moving mice. With it, we uncovered cross-frequency-coupled theta- and gamma-range oscillations and characterized excitatory-inhibitory neural dynamics during hippocampal ripples and visual cortical processing. The TEMPO mesoscope images voltage activity in two cell-classes across a ~8-mm-wide field-of-view in head-fixed animals. In awake mice, it revealed sensory-evoked excitatory-inhibitory neural interactions and traveling gamma and 3-7 Hz waves in the visual cortex, and previously unreported propagation directions for hippocampal theta and beta waves. These technologies have widespread applications probing diverse oscillations and neuron-type interactions in healthy and diseased brains.

Experience with the cochlear implant enhances the neural tracking of spectrotemporal patterns in the Alberti bass

Cochlear implant (CI) users experience diminished music enjoyment due to the technical limitations of the CI. Nonetheless, behavioral studies have reported that rhythmic features are well-transmitted through the CI. Still, the gradual improvement of rhythm perception after the CI switch-on has not yet been determined using neurophysiological measures. To fill this gap, we here reanalyzed the electroencephalographic responses of participants from two previous mismatch negativity studies. These studies included eight recently implanted CI users measured twice, within the first six weeks after CI switch-on and approximately three months later; thirteen experienced CI users with a median experience of 7 years; and fourteen normally hearing (NH) controls. All participants listened to a repetitive four-tone pattern (known in music as Alberti bass) for 35 min. Applying frequency tagging, we aimed to estimate the neural activity synchronized to the periodicities of the Alberti bass. We hypothesized that longer experience with the CI would be reflected in stronger frequency-tagged neural responses approaching the responses of NH controls. We found an increase in the frequency-tagged amplitudes after only 3 months of CI use. This increase in neural synchronization may reflect an early adaptation to the CI stimulation. Moreover, the frequency-tagged amplitudes of experienced CI users were significantly greater than those of recently implanted CI users, but still smaller than those of NH controls. The frequency-tagged neural responses did not just reflect spectrotemporal changes in the stimuli (i.e., intensity or spectral content fluctuating over time), but also showed non-linear transformations that seemed to enhance relevant periodicities of the Alberti bass. Our findings provide neurophysiological evidence indicating a gradual adaptation to the CI, which is noticeable already after three months, resulting in close to NH brain processing of spectrotemporal features of musical rhythms after extended CI use.