Auditory and Visual Gratings Elicit Distinct Gamma Responses.

Sensory stimulation is often accompanied by fluctuations at high frequencies (>30 Hz) in brain signals. These could be "narrowband" oscillations in the gamma band (30-70 Hz) or nonoscillatory "broadband" high-gamma (70-150 Hz) activity. Narrowband gamma oscillations, which are induced by presenting some visual stimuli such as gratings and have been shown to weaken with healthy aging and the onset of Alzheimer's disease, hold promise as potential biomarkers. However, since delivering visual stimuli is cumbersome as it requires head stabilization for eye tracking, an equivalent auditory paradigm could be useful. Although simple auditory stimuli have been shown to produce high-gamma activity, whether specific auditory stimuli can also produce narrowband gamma oscillations is unknown. We tested whether auditory ripple stimuli, which are considered an analog to visual gratings, could elicit narrowband oscillations in auditory areas. We recorded 64-channel electroencephalogram from male and female (18 each) subjects while they either fixated on the monitor while passively viewing static visual gratings or listened to stationary and moving ripples, played using loudspeakers, with their eyes open or closed. We found that while visual gratings induced narrowband gamma oscillations with suppression in the alpha band (8-12 Hz), auditory ripples did not produce narrowband gamma but instead elicited very strong broadband high-gamma response and suppression in the beta band (14-26 Hz). Even though we used equivalent stimuli in both modalities, our findings indicate that the underlying neuronal circuitry may not share ubiquitous strategies for stimulus processing.

Causal functional maps of brain rhythms in working memory.

Human working memory is a key cognitive process that engages multiple functional anatomical nodes across the brain. Despite a plethora of correlative neuroimaging evidence regarding the working memory architecture, our understanding of critical hubs causally controlling overall performance is incomplete. Causal interpretation requires cognitive testing following safe, temporal, and controllable neuromodulation of specific functional anatomical nodes. Such experiments became available in healthy humans with the advance of transcranial alternating current stimulation (tACS). Here, we synthesize findings of 28 placebo-controlled studies (in total, 1,057 participants) that applied frequency-specific noninvasive stimulation of neural oscillations and examined working memory performance in neurotypical adults. We use a computational meta-modeling method to simulate each intervention in realistic virtual brains and test reported behavioral outcomes against the stimulation-induced electric fields in different brain nodes. Our results show that stimulating anterior frontal and medial temporal theta oscillations and occipitoparietal gamma rhythms leads to significant dose-dependent improvement in working memory task performance. Conversely, prefrontal gamma modulation is detrimental to performance. Moreover, we found distinct spatial expression of theta subbands, where working memory changes followed orbitofrontal high-theta modulation and medial temporal low-theta modulation. Finally, all these results are driven by changes in working memory accuracy rather than processing time measures. These findings provide a fresh view of the working memory mechanisms, complementary to neuroimaging research, and propose hypothesis-driven targets for the clinical treatment of working memory deficits.

A mean-field model of gamma-frequency oscillations in networks of excitatory and inhibitory neurons.

Gamma oscillations are widely seen in the cerebral cortex in different states of the wake-sleep cycle and are thought to play a role in sensory processing and cognition. Here, we study the emergence of gamma oscillations at two levels, in networks of spiking neurons, and a mean-field model. At the network level, we consider two different mechanisms to generate gamma oscillations and show that they are best seen if one takes into account the synaptic delay between neurons. At the mean-field level, we show that, by introducing delays, the mean-field can also produce gamma oscillations. The mean-field matches the mean activity of excitatory and inhibitory populations of the spiking network, as well as their oscillation frequencies, for both mechanisms. This mean-field model of gamma oscillations should be a useful tool to investigate large-scale interactions through gamma oscillations in the brain.

Enhancement of low gamma oscillations by volitional conditioning of local field potential in the primary motor and visual cortex of mice.

Volitional control of local field potential oscillations in low gamma band via brain machine interface can not only uncover the relationship between low gamma oscillation and neural synchrony but also suggest a therapeutic potential to reverse abnormal local field potential oscillation in neurocognitive disorders. In nonhuman primates, the volitional control of low gamma oscillations has been demonstrated by brain machine interface techniques in the primary motor and visual cortex. However, it is not clear whether this holds in other brain regions and other species, for which gamma rhythms might involve in highly different neural processes. Here, we established a closed-loop brain-machine interface and succeeded in training mice to volitionally elevate low gamma power of local field potential in the primary motor and visual cortex. We found that the mice accomplished the task in a goal-directed manner and spiking activity exhibited phase-locking to the oscillation in local field potential in both areas. Moreover, long-term training made the power enhancement specific to direct and adjacent channel, and increased the transcriptional levels of NMDA receptors as well as that of hypoxia-inducible factor relevant to metabolism. Our results suggest that volitionally generated low gamma rhythms in different brain regions share similar mechanisms and pave the way for employing brain machine interface in therapy of neurocognitive disorders.

Effect of Esketamine on perioperative anxiety and depression in women with systemic tumors based on big data medical background.

OBJECTIVE: Perioperative anxiety and depression syndrome (PADS) is a common clinical concern among women with systemic tumors. Esketamine has been considered for its potential to alleviate anxiety and depressive symptoms. However, its specific application and effectiveness in PADS among women with systemic tumors remain unclear. This study aimed to analyze the utility of Machine Learning (ML) algorithms based on electroencephalogram (EEG) signals in evaluating perioperative anxiety and depression in women with systemic tumors treated with Esketamine, utilizing a large-scale medical data background. PATIENTS AND METHODS: A single-center, randomized, placebo-controlled (SC-RPC) trial design was adopted. A total of 112 female patients with systemic tumors and PADS who received Esketamine treatment were included as study participants. A moderate dose (0.7 mg/kg) of Esketamine was administered through intravenous infusion over a duration of 60 minutes. EEG signals were collected from all patients, and the EEG signal features of individuals with depression were compared to those without depression. In this study, a Support Vector Machine (SVM)-K-Nearest Neighbour (KNN) hybrid classifier was constructed based on SVM and KNN algorithms. Using the EEG signals, the classifier was utilized to assess the anxiety and depression status of the patients. The predictive performance of the classifier was evaluated using accuracy, sensitivity, and specificity measures. RESULTS: The C2 correntropy feature of the delta rhythm in the left-brain EEG signal was significantly higher in individuals with depression compared to those without depression (p<0.05). Moreover, the C2 correntropy feature of the Alpha, Beta, and Gamma rhythms in the left-brain EEG signal was significantly lower in individuals with depression compared to those without depression (p<0.05). In the right brain EEG signal, the C2 correntropy feature of the delta rhythm was significantly higher in individuals with depression (p<0.05), while the C2 correntropy feature of the alpha and gamma rhythms was significantly lower in individuals with depression compared to those without depression (p<0.05). Additionally, the C1 correntropy feature of the Gamma rhythm in the right brain EEG signal was significantly higher in individuals with depression compared to those without depression (p<0.05). The SVM classifier achieved accuracy, sensitivity, and specificity of 98.23%, 98.10%, and 98.56%, respectively, in recognizing the left-brain EEG signals, with a correlation coefficient of 0.95. In recognizing the right brain EEG signals, the SVM classifier achieved accuracy, sensitivity, and specificity of 98.74%, 98.43%, and 99.03%, respectively, with a correlation coefficient of 0.96. The improved SVM-KNN approach yielded an accuracy, recall, precision, F-score, area over the curve (AOC), and Receiver Operation Characteristics (ROC) of 0.829, 0.811, 0.791, 0.853, 0.787, and 0.877, respectively, in predicting anxiety. For predicting depression, the accuracy, recall, precision, F-score, AOC, and ROC were 0.869, 0.842, 0.831, 0.893, 0.827, and 0.917, respectively. CONCLUSIONS: Significant differences were observed in the brain EEG signals between individuals with depression and those without depression. The improved SVM-KNN algorithm developed in this study demonstrates good predictive capability for anxiety and depression.

Rodents' visual gamma as a biomarker of pathological neural conditions.

Neural gamma oscillations (indicatively 30-100 Hz) are ubiquitous: they are associated with a broad range of functions in multiple cortical areas and across many animal species. Experimental and computational works established gamma rhythms as a global emergent property of neuronal networks generated by the balanced and coordinated interaction of excitation and inhibition. Coherently, gamma activity is strongly influenced by the alterations of synaptic dynamics which are often associated with pathological neural dysfunctions. We argue therefore that these oscillations are an optimal biomarker for probing the mechanism of cortical dysfunctions. Gamma oscillations are also highly sensitive to external stimuli in sensory cortices, especially the primary visual cortex (V1), where the stimulus dependence of gamma oscillations has been thoroughly investigated. Gamma manipulation by visual stimuli tuning is particularly easy in rodents, which have become a standard animal model for investigating the effects of network alterations on gamma oscillations. Overall, gamma in the rodents' visual cortex offers an accessible probe on dysfunctional information processing in pathological conditions. Beyond vision-related dysfunctions, alterations of gamma oscillations in rodents were indeed also reported in neural deficits such as migraine, epilepsy and neurodegenerative or neuropsychiatric conditions such as Alzheimer's, schizophrenia and autism spectrum disorders. Altogether, the connections between visual cortical gamma activity and physio-pathological conditions in rodent models underscore the potential of gamma oscillations as markers of neuronal (dys)functioning.

Multisensory gamma stimulation promotes glymphatic clearance of amyloid.

The glymphatic movement of fluid through the brain removes metabolic waste1-4. Noninvasive 40 Hz stimulation promotes 40 Hz neural activity in multiple brain regions and attenuates pathology in mouse models of Alzheimer's disease5-8. Here we show that multisensory gamma stimulation promotes the influx of cerebrospinal fluid and the efflux of interstitial fluid in the cortex of the 5XFAD mouse model of Alzheimer's disease. Influx of cerebrospinal fluid was associated with increased aquaporin-4 polarization along astrocytic endfeet and dilated meningeal lymphatic vessels. Inhibiting glymphatic clearance abolished the removal of amyloid by multisensory 40 Hz stimulation. Using chemogenetic manipulation and a genetically encoded sensor for neuropeptide signalling, we found that vasoactive intestinal peptide interneurons facilitate glymphatic clearance by regulating arterial pulsatility. Our findings establish novel mechanisms that recruit the glymphatic system to remove brain amyloid.

Gamma oscillatory complexity conveys behavioral information in hippocampal networks.

The hippocampus and entorhinal cortex exhibit rich oscillatory patterns critical for cognitive functions. In the hippocampal region CA1, specific gamma-frequency oscillations, timed at different phases of the ongoing theta rhythm, are hypothesized to facilitate the integration of information from varied sources and contribute to distinct cognitive processes. Here, we show that gamma elements -a multidimensional characterization of transient gamma oscillatory episodes- occur at any frequency or phase relative to the ongoing theta rhythm across all CA1 layers in male mice. Despite their low power and stochastic-like nature, individual gamma elements still carry behavior-related information and computational modeling suggests that they reflect neuronal firing. Our findings challenge the idea of rigid gamma sub-bands, showing that behavior shapes ensembles of irregular gamma elements that evolve with learning and depend on hippocampal layers. Widespread gamma diversity, beyond randomness, may thus reflect complexity, likely functional but invisible to classic average-based analyses.

Synchronized LFP rhythmicity in the social brain reflects the context of social encounters.

Mammalian social behavior is highly context-sensitive. Yet, little is known about the mechanisms that modulate social behavior according to its context. Recent studies have revealed a network of mostly limbic brain regions which regulates social behavior. We hypothesize that coherent theta and gamma rhythms reflect the organization of this network into functional sub-networks in a context-dependent manner. To test this concept, we simultaneously record local field potential (LFP) from multiple social brain regions in adult male mice performing three social discrimination tasks. While LFP rhythmicity across all tasks is dominated by a global internal state, the pattern of theta coherence between the various regions reflect the behavioral task more than other variables. Moreover, Granger causality analysis implicate the ventral dentate gyrus as a main player in coordinating the context-specific rhythmic activity. Thus, our results suggest that the pattern of coordinated rhythmic activity within the network reflects the subject's social context.

Gamma Rhythm and Theta-gamma Coupling Alternation in Chronic Unpredictable Stress (CUS)-induced Depression Rats.

Depression is a debilitating disease, which, in severe cases, can lead to suicide. However, objective and reliable biomarker for the diagnosis of depression is lack. In this preclinical study, we recorded resting local field potentials (LFPs) from chronic unpredictable stress (CUS)-induced depressed (n =20) and control (n = 20) rats and then compared their gamma activities in terms of single-band and cross-frequency coupling patterns. Both theta-gamma coupling and relative power in total gamma band revealed significant abnormalities in gamma rhythm in the right auditory cortex of depressed rats. These findings implied that resting-state gamma rhythms may be a promising objective diagnostic biomarker for depression. Furthermore, our research provided direct evidence from the perspective of source signals in deep brain sites, which might be useful for clinical applications.Clinical Relevance- This research showed that resting gamma in the auditory cortex is a promising biomarker for depression diagnosis.

Left-dominance for resting-state temporal low-gamma power in children with impaired word-decoding and without comorbid ADHD.

One theory of the origins of reading disorders (i.e., dyslexia) is a language network which cannot effectively 'entrain' to speech, with cascading effects on the development of phonological skills. Low-gamma (low-γ, 30-45 Hz) neural activity, particularly in the left hemisphere, is thought to correspond to tracking at phonemic rates in speech. The main goals of the current study were to investigate temporal low-γ band-power during rest in a sample of children and adolescents with and without reading disorder (RD). Using a Bayesian statistical approach to analyze the power spectral density of EEG data, we examined whether (1) resting-state temporal low-γ power was attenuated in the left temporal region in RD; (2) low-γ power covaried with individual reading performance; (3) low-γ temporal lateralization was atypical in RD. Contrary to our expectations, results did not support the hypothesized effects of RD status and poor decoding ability on left hemisphere low-γ power or lateralization: post-hoc tests revealed that the lack of atypicality in the RD group was not due to the inclusion of those with comorbid attentional deficits. However, post-hoc tests also revealed a specific left-dominance for low-γ rhythms in children with reading deficits only, when participants with comorbid attentional deficits were excluded. We also observed an inverse relationship between decoding and left-lateralization in the controls, such that those with better decoding skills were less likely to show left-lateralization. We discuss these unexpected findings in the context of prior theoretical frameworks on temporal sampling. These results may reflect the importance of real-time language processing to evoke gamma rhythms in the phonemic range during childhood and adolescence.

Effects of high-frequency transcranial magnetic stimulation on theta-gamma oscillations and coupling in the prefrontal cortex of rats during working memory task.

High-frequency rTMS has been widely used to improve working memory (WM) impairment; however, the underlying neurophysiological mechanisms are unclear. We evaluated the effect of high-frequency rTMS on behaviors relevant to WM as well as coupling between theta and gamma oscillations in the prefrontal cortex (PFC) of rats. Accordingly, Wistar rats received high-frequency rTMS daily for 14 days (5 Hz, 10 Hz, and 15 Hz stimulation; 600 pulses; n = 6 per group), whereas the control group received sham stimulation. Electrophysiological signals were recorded simultaneously to obtain the local field potential (LFP) from the PFC, while the rats performed T-maze tasks for the evaluation of WM. Phase-amplitude coupling (PAC) was utilized to determine the effect of high-frequency rTMS on the theta-gamma coupling of LFPs. We observed that rats in the rTMS groups needed a smaller number of training days to complete the WM task as compared to the control group. High-frequency rTMS reinforced the coupling connection strength in the PFC of rats. Notably, the effect of rTMS at 15 Hz was the most effective among the three frequencies, i.e., 5 Hz, 10 Hz, and 15 Hz. The results suggested that rTMS can improve WM impairment in rats by modulating the coupling of theta and gamma rhythms. Hence, the current study provides a scientific basis for the optimization of TMS models, which would be relevant for clinical application.

Effects of optogenetic and visual stimulation on gamma activity in the visual cortex.

Studying brain functions and activity during gamma oscillations can be a challenge because it requires careful planning to create the necessary conditions for a controlled experiment. Such an experiment consists of placing the brain into a gamma state and investigating cognitive processing with a careful design. Cortical oscillations in the gamma frequency range (30-80 Hz) play an essential role in a variety of cognitive processes, including visual processing and cognition. The present study aims to investigate the effects of a visual stimulus on the primary visual cortex under gamma oscillations. Specifically, we sought to explore the behavior of gamma oscillations triggered by optogenetic stimulation in the II and IV layers of the visual cortex, both with and without concurrent visual stimulation. Our results show that optogenetic stimulation increases the power of gamma oscillation in both layers of the visual cortex. However, the combined stimuli resulted in a reduction of gamma power in layer II and an increase and reinforcement in gamma power in layer IV. Modelling the results with the Wilson-Cowan model suggests changes in the input of the excitatory population due to the combined stimuli. In addition, our analysis of the data using the Lempel-Ziv complexity method supports our interpretations from the modeling. Thus, our results suggest that optogenetic stimulation enhances low gamma power in both layers of the visual cortex, while simultaneous visual stimulation has differing effects on the two layers, reducing gamma power in layer II and increasing it in layer IV.

Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation.

Hippocampal CA1 neurons generate single spikes and stereotyped bursts of spikes. However, it is unclear how individual neurons dynamically switch between these output modes and whether these two spiking outputs relay distinct information. We performed extracellular recordings in spatially navigating rats and cellular voltage imaging and optogenetics in awake mice. We found that spike bursts are preferentially linked to cellular and network theta rhythms (3-12 Hz) and encode an animal's position via theta phase precession, particularly as animals are entering a place field. In contrast, single spikes exhibit additional coupling to gamma rhythms (30-100 Hz), particularly as animals leave a place field. Biophysical modeling suggests that intracellular properties alone are sufficient to explain the observed input frequency-dependent spike coding. Thus, hippocampal neurons regulate the generation of bursts and single spikes according to frequency-specific network and intracellular dynamics, suggesting that these spiking modes perform distinct computations to support spatial behavior.

Gamma oscillations and episodic memory.

Enhanced gamma oscillatory activity (30-80 Hz) accompanies the successful formation and retrieval of episodic memories. While this co-occurrence is well documented, the mechanistic contributions of gamma oscillatory activity to episodic memory remain unclear. Here, we review how gamma oscillatory activity may facilitate spike timing-dependent plasticity, neural communication, and sequence encoding/retrieval, thereby ensuring the successful formation and/or retrieval of an episodic memory. Based on the evidence reviewed, we propose that multiple, distinct forms of gamma oscillation can be found within the canonical gamma band, each of which has a complementary role in the neural processes listed above. Further exploration of these theories using causal manipulations may be key to elucidating the relevance of gamma oscillatory activity to episodic memory.

Temporal characteristics of gamma rhythm constrain properties of noise in an inhibition-stabilized network model.

Gamma rhythm refers to oscillatory neural activity between 30 and 80 Hz, induced in visual cortex by stimuli such as iso-luminant hues or gratings. The power and peak frequency of gamma depend on the properties of the stimulus such as size and contrast. Gamma waveform is typically arch-shaped, with narrow troughs and broad peaks, and can be replicated in a self-oscillating Wilson-Cowan (WC) model operating in an appropriate regime. However, oscillations in this model are infinitely long, unlike physiological gamma that occurs in short bursts. Further, unlike the model, gamma is faster after stimulus onset and slows down over time. Here, we first characterized gamma burst duration in local field potential data recorded from two monkeys as they viewed full screen iso-luminant hues. We then added different types of noise in the inputs to the WC model and tested how that affected duration and temporal dynamics of gamma. While the model failed with the often-used Poisson noise, Ornstein-Uhlenbeck noise applied to both the excitatory and the inhibitory populations replicated the duration and slowing of gamma and replicated the shape and stimulus dependencies. Thus, the temporal dynamics of gamma oscillations put constraints on the type and properties of underlying neural noise.

On the Functional Role of Gamma Synchronization in the Retinogeniculate System of the Cat.

Fast gamma oscillations, generated within the retina, and transmitted to the cortex via the lateral geniculate nucleus (LGN), are thought to carry information about stimulus size and continuity. This hypothesis relies mainly on studies conducted under anesthesia and the extent to which it holds under more naturalistic conditions remains unclear. Using multielectrode recordings of spiking activity in the retina and the LGN of both male and female cats, we show that visually driven gamma oscillations are absent for awake states and are highly dependent on halothane (or isoflurane). Under ketamine, responses were nonoscillatory, as in the awake condition. Response entrainment to the monitor refresh was commonly observed up to 120 Hz and was superseded by the gamma oscillatory responses induced by halothane. Given that retinal gamma oscillations are contingent on halothane anesthesia and absent in the awake cat, such oscillations should be considered artifactual, thus playing no functional role in vision.SIGNIFICANCE STATEMENT Gamma rhythms have been proposed to be a robust encoding mechanism critical for visual processing. In the retinogeniculate system of the cat, many studies have shown gamma oscillations associated with responses to static stimuli. Here, we extend these observations to dynamic stimuli. An unexpected finding was that retinal gamma responses strongly depend on halothane concentration levels and are absent in the awake cat. These results weaken the notion that gamma in the retina is relevant for vision. Notably, retinal gamma shares many of the properties of cortical gamma. In this respect, oscillations induced by halothane in the retina may serve as a valuable preparation, although artificial, for studying oscillatory dynamics.

Eltoprazine modulated gamma oscillations on ameliorating L-dopa-induced dyskinesia in rats.

AIM: Parkinson's disease (PD) is a pervasive neurodegenerative disease, and levodopa (L-dopa) is its preferred treatment. The pathophysiological mechanism of levodopa-induced dyskinesia (LID), the most common complication of long-term L-dopa administration, remains obscure. Accumulated evidence suggests that the dopaminergic as well as non-dopaminergic systems contribute to LID development. As a 5-hydroxytryptamine 1A/1B receptor agonist, eltoprazine ameliorates dyskinesia, although little is known about its electrophysiological mechanism. The aim of this study was to investigate the cumulative effects of chronic L-dopa administration and the potential mechanism of eltoprazine's amelioration of dyskinesia at the electrophysiological level in rats. METHODS: Neural electrophysiological analysis techniques were conducted on the acquired local field potential (LFP) data from primary motor cortex (M1) and dorsolateral striatum (DLS) during different pathological states to obtain the information of power spectrum density, theta-gamma phase-amplitude coupling (PAC), and functional connectivity. Behavior tests and AIMs scoring were performed to verify PD model establishment and evaluate LID severity. RESULTS: We detected exaggerated gamma activities in the dyskinetic state, with different features and impacts in distinct regions. Gamma oscillations in M1 were narrowband manner, whereas that in DLS had a broadband appearance. Striatal exaggerated theta-gamma PAC in the LID state contributed to broadband gamma oscillation, and aperiodic-corrected cortical beta power correlated robustly with aperiodic-corrected gamma power in M1. M1-DLS coherence and phase-locking values (PLVs) in the gamma band were enhanced following L-dopa administration. Eltoprazine intervention reduced gamma oscillations, theta-gamma PAC in the DLS, and coherence and PLVs in the gamma band to alleviate dyskinesia. CONCLUSION: Excessive cortical gamma oscillation is a compelling clinical indicator of dyskinesia. The detection of enhanced PAC and functional connectivity of gamma-band oscillation can be used to guide and optimize deep brain stimulation parameters. Eltoprazine has potential clinical application for dyskinesia.

Disrupted hippocampal synchrony following maternal immune activation in a rat model.

Maternal immune activation (MIA) is a risk factor for schizophrenia and other neurodevelopmental disorders. MIA in rats models a number of the brain and behavioral changes that are observed in schizophrenia, including impaired memory. Recent studies in the MIA model have shown that the firing of the hippocampal place cells that are involved in memory processes appear relatively normal, but with abnormalities in the temporal ordering of firing. In this study, we re-analyzed data from prior hippocampal electrophysiological recordings of MIA and control animals to determine whether temporal dysfunction was evident. We find that there is a decreased ratio of slow to fast gamma power, resulting from an increase in fast gamma power and a tendency toward reduced slow gamma power in MIA rats. Moreover, we observe a robust reduction in spectral coherence between hippocampal theta and both fast and slow gamma rhythms, as well as changes in the phase of theta at which fast gamma occurs. We also find the phasic organization of place cell phase precession on the theta wave to be abnormal in MIA rats. Lastly, we observe that the local field potential of MIA rats contains more frequent sharp-wave ripple events, and that place cells were more likely to fire spikes during ripples in these animals than control. These findings provide further evidence of desynchrony in MIA animals and may point to circuit-level changes that underlie failures to integrate and encode information in schizophrenia.

EEG rhythm based emotion recognition using multivariate decomposition and ensemble machine learning classifier.

Recently, electroencephalogram (EEG) signals have shown great potential to recognize human emotions. The goal of effective computing is to assist computers in understanding various types of emotions via human-computer interaction (HCI). Multichannel EEG signals are used to measure the electrical activity of the brain in space and time. Automated emotion recognition using multichannel EEG signals is an interesting area of cognitive neuroscience and affective computing research. This research proposes EEG multichannel rhythmic features and ensemble machine learning (EML) classifiers with leave-one-subject-out cross-validation (LOSOCV) for automatic emotion classification from multichannel EEG recordings. Multivariate fast iterative filtering (MvFIF) is used to assess the EEG rhythm sequences. EEG rhythms delta(δ), theta(θ), alpha(α), beta(ß), and gamma(γ) are separated based on the mean frequency of the EEG rhythm sequence. Three Hjorth parameters and nine entropy features were extracted from multichannel EEG rhythms. Extracted features are selected using the minimum redundancy maximum relevance (mRMR) approach. The experimental design was performed on two emotional datasets (GAMEEMO and DREAMER). The validation showed that gamma rhythm multichannel features with EML-based subspace K-nearest neighbor (SS KNN) were as high as 93.5%-99.8%, achieving high classification accuracy. The comparisons of δ, θ, α, ß, and γ rhythms with EML, support vector machine (SVM), and artificial neural network (ANN) were performed. we also analyzed multi-class emotions (HVHA, HVLA, LVHA, LVLA) with an ensemble-based bagging tree on gamma rhythm. It provides a novel solution for multichannel rhythm-specific features in EEG data analysis.