Electroencephalography Signals Research Articles

Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.

Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.

ROLE OF NEUROMARKETING IN UNDERSTANDING CONSUMER BEHAVIOR: A LITERATURE OVERVIEW

Neuromarketing has been widely used to measure consumer behavior through several stimulation, including logo colors of a brand. Neuromarketing is applied using electroencephalography (EEG) signal analysis to the human brain's respons. This article explores the emerging field of neuromarketing and its potential impact on consumer buying behaviour. Neuromarketing combines neuroscience, psychology, and marketing to better understand consumer decision-making processes. The article discusses the various techniques used in neuromarketing research, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and eye-tracking, and the insights they provide into the subconscious factors that influence consumer behaviour. The article also discusses the ethical concerns surrounding neuromarketing, particularly regarding the manipulation of consumer behaviour. Overall, the article suggests that neuromarketing has the potential to revolutionize the way marketers understand and influence consumer behaviour. Neuromarketing has the potential to revolutionize the way businesses understand and engage with consumers. By providing insights into the emotional and psychological factors that influence purchasing decisions, businesses can create more effective marketing campaigns that resonate with consumers on a deeper level. However, it is essential to balance these benefits with ethical considerations and ensure that consumers' privacy is protected.

Fatigue feature extraction and classification algorithm of forehead single-channel electroencephalography signals

Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.

The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

The P3 event-related potential increases when humans learn a strategy for motor adaptation.

Human motor learning involves cognitive strategies in addition to implicit adaptation. Differences in systems-level neurophysiology between strategy-based and implicit learning remain poorly understood. We asked how the P3 event-related potential, an electroencephalography signal known to increase during early motor learning, relates to strategy-based learning and implicit adaptation. We re-analysed data from two experiments, in which participants (n = 64) reached towards a visual target, with online visual feedback replacing vision of their moving hand. We induced learning by rotating the visual feedback. In the first experiment, feedback rotations were turned on during pairs of two consecutive trials, interspersed between non-rotated trials. In one condition, feedback was rotated relative to the actual movement, allowing participants to develop a re-aiming strategy on the second trial of each pair, while it was rotated relative to the target in the other condition, rendering re-aiming futile. P3 amplitude increased in the first rotated trial in both conditions, but this increase was more pronounced in the re-aiming condition. In the second experiment, a constant visuomotor rotation was turned on for many consecutive trials. We instructed one group beforehand how to re-aim successfully, while the other group had to develop a strategy by themselves. P3 amplitude increased during early adaptation only in the latter group. These findings collectively suggest that in the context of motor learning, the P3 ERP is associated with a need to develop, or adjust, a cognitive strategy.

Deep Convolutional Neural Network Model for the Differential Diagnosis of Schizophrenia Using EEG Signals

Objective: One of the serious mental disorders in which people interpret reality in an abnormal situation is schizophrenia. A combination of extremely disordered thoughts, delusions, and hallucinations occurs due to schizophrenia, and the person's daily functions are seriously impaired due to this disease. For general cognitive activity analysis, electroencephalography signals are widely used as a low-resolution diagnostic tool. This study aimed to diagnose schizophrenia using the transfer learning method by including the EEGs of 73 patients diagnosed with schizophrenia, and 67 patients from the healthy group. Material and Method: In the first step of the study, digital electroencephalography signal data was converted into spectrograms to make them usable. In the classification phase, ResNet18, ResNet50 and EfficientNet models, which are FastAI, and Convolutional Neural Network (CNN) based deep learning models, were used. Results: Despite the complexity of electroencephalography data, CNN-based models in the study were successful in capturing different aspects of neurophysiological activity. The best performance was obtained from the ResNet-50 model with an accuracy rate of 97%. Afterwards, the classification process was finalized with 95% ResNet-18, and 83% EfficientNet models, respectively. Conclusion: It is thought that the classification performance of the result obtained in the application is promising, and may be a guide for future studies.

GNN-based multi-source domain prototype representation for cross-subject EEG emotion recognition

Emotion recognition based on electroencephalography (EEG) signals is a major area of affective computing. However, the existence of distributional differences between subjects has greatly hindered the large-scale application of EEG emotion recognition techniques. Most of the existing cross-subject methods primarily concentrate on treating multiple subjects as a single source domain. These methods lead to significant distributional differences within the source domain, which hinder the model’s ability to generalise effectively to target subjects. In this paper, we propose a new method that combines graph neural network-based prototype representation of multiple source domains with clustering similarity loss. It consists of three parts: multi-source domain prototype representation, graph neural network and loss. Multi-source domain prototype representation treats different subjects in the source domain as sub-source domains and extracts prototype features, which learns a more fine-grained feature representation. Graph neural network can better model the association properties between prototypes and samples. In addition, we propose a similarity loss based on clustering idea. The loss makes maximum use of similarity between samples in the target domain while ensuring that the classification performance does not degrade. We conduct extensive experiments on two benchmark datasets, SEED and SEED IV. The experimental results validate the effectiveness of the proposed multi-source domain fusion approach and indicate its superiority over existing methods in cross-subject classification tasks.

Detection of Anxiety-Based Epileptic Seizures in EEG Signals Using Fuzzy Features and Parrot Optimization-Tuned LSTM.

In humans, epilepsy is diagnosed through electroencephalography (EEG) signals. Epileptic seizures (ESs) arise due to anxiety. The detection of anxiety-based seizures is challenging for radiologists, and there is a limited availability of anxiety-based EEG signals. Data augmentation methods are required to increase the number of novel samples. An epileptic seizure arises due to anxiety, which manifests as variations in EEG signal patterns consisting of changes in the size and shape of the signal. In this study, anxiety EEG signals were synthesized by applying data augmentation methods such as random data augmentation (RDA) to existing epileptic seizure signals from the Bonn EEG dataset. The data-augmented anxiety seizure signals were processed using three algorithms-(i) fuzzy C-means-particle swarm optimization-long short-term memory (FCM-PS-LSTM), (ii) particle swarm optimization-long short-term memory (PS-LSTM), and (iii) parrot optimization LSTM (PO-LSTM)-for the detection of anxiety ESs via EEG signals. The predicted accuracies of detecting ESs through EEG signals using the proposed algorithms-namely, (i) FCM-PS-LSTM, (ii) PS-LSTM, and (iii) PO-LSTM-were about 98%, 98.5%, and 96%, respectively.

Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals. BCIs based on code-modulated visual evoked potentials (cVEPs) are based on visual stimuli, thus appropriate visual feedback on the interface is crucial for an effective BCI system. Many previous studies have demonstrated that implementing visual feedback can improve information transfer rate (ITR) and reduce fatigue. This research compares a dynamic interface, where target boxes change their sizes based on detection certainty, with a threshold bar interface in a three-step cVEP speller. In this study, we found that both interfaces perform well, with slight variations in accuracy, ITR, and output characters per minute (OCM). Notably, some participants showed significant performance improvements with the dynamic interface and found it less distracting compared to the threshold bars. These results suggest that while average performance metrics are similar, the dynamic interface can provide significant benefits for certain users. This study underscores the potential for personalized interface choices to enhance BCI user experience and performance. By improving user friendliness, performance, and reducing distraction, dynamic visual feedback could optimize BCI technology for a broader range of users.

An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks.

This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain-computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI).

A MENTAL HEALTH DETECTION MODEL OF COLLEGE STUDENTS UNDER THE BEHAVIOR OF LEARNING BURNOUT

With the increase in academic pressure on college students, some students show signs of academic burnout, which may lead to mental health problems. This study aims to explore the mental health status of college students under the manifestation of academic burnout and to propose a mental health detection model based on electroencephalography (EEG) signals. In this study, college students with the manifestation of study burnout were selected as research subjects, and their EEG signals were collected to identify and analyze their psychological state using a mental health recognition model. In the selection of mental health recognition models, this study used a variety of classical models for comparative analysis, including support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), convolutional neural network (CNN), long and short-term memory network (LSTM), and convolutional neural network-long and short-term memory network (CNN-LSTM). Experimental results show that the CNN-LSTM model performs best in identifying students with mental health problems, and its accuracy and stability are better than other models. Through this study, we conclude that academic burnout is closely related to college students’ mental health problems, and EEG signals combined with the CNN-LSTM model can effectively identify the mental health status of these students. This finding provides a new method and tool for monitoring college students’ mental health, which helps detect and intervene in students’ mental health problems early and promote their healthy growth and development.

Design Of a High Gain Three-Stage CMOS Operational Amplifier for EEG Signal Processing

Recently, research on brain science has led to rapid development in Electroencephalography (EEG) signal processing. However, it is really hard to capture, amplify and measure the EEG signals from the human brain during the signal acquisition and amplification. Therefore, this paper introduces a newly-designed high gain three-stage CMOS operational amplifier to measure and amplify the EEG signals precisely. The main circuit structure is built in CoolSpice with the TSMC 0.18um CMOS technology. During the simulation in CoolSpice, the open-loop DC gain reaches to 90.04dB and its power consumption is around 19.8 mW. With the help of the frequency compensation applied in the circuit, the bandwidth of the op-amp achieves to 363Hz, which is able to amplify all sorts of EEG signals and measure them, and the phase margin with the value of 177 degrees, making the circuit system stable. This three-stage op-amp makes its circuit system stable and avoids the unnecessary oscillations during the EEG signal amplification, which lead to a precise measurement on EEG signals and provide the precise diagnosis for the patients.

Machine Learning Recognizes Frequency-Following Responses in American Adults: Effects of Reference Spectrogram and Stimulus Token.

Electrophysiological research has been widely utilized to study brain responses to acoustic stimuli. The frequency-following response (FFR), a non-invasive reflection of how the brain encodes acoustic stimuli, is a particularly propitious electrophysiologic measure. While the FFR has been studied extensively, there are limitations in obtaining and analyzing FFR recordings that recent machine learning algorithms may address. In this study, we aimed to investigate whether FFRs can be enhanced using an "improved" source-separation machine learning algorithm. For this study, we recruited 28 native speakers of American English with normal hearing. We obtained two separate FFRs from each participant while they listened to two stimulus tokens /i/ and /da/. Electroencephalographic signals were pre-processed and analyzed using a source-separation non-negative matrix factorization (SSNMF) machine learning algorithm. The algorithm was trained using individual, grand-averaged, or stimulus token spectrograms as a reference. A repeated measures analysis of variance revealed that FFRs were significantly enhanced (p < .001) when the "improved" SSNMF algorithm was trained using both individual and grand-averaged spectrograms, but not when utilizing the stimulus token spectrogram. Similar results were observed when extracting FFRs elicited by using either stimulus token, /i/ or /da/. This demonstration shows how the SSNMF machine learning algorithm, using individual and grand-averaged spectrograms as references in training the algorithm, significantly enhanced FFRs. This improvement has important implications for the obtainment and analytical processes of FFR, which may lead to advancements in clinical applications of FFR testing.

User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.

This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.

Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods

A systematic review on machine-learning strategies for improving generalization in electroencephalography-based emotion classification was realized. In particular, cross-subject and cross-session generalization was focused. In this context, the non-stationarity of electroencephalographic (EEG) signals is a critical issue and can lead to the Dataset Shift problem. Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods. In this review, 449 papers were retrieved from the Scopus, IEEE Xplore and PubMed databases through a search query focusing on modern machine learning techniques for generalization in EEG-based emotion assessment. Among these papers, 79 were found eligible based on their relevance to the problem. Studies lacking a specific cross-subject or cross-session validation strategy, or making use of other biosignals as support were excluded. On the basis of the selected papers’ analysis, a taxonomy of the studies employing Machine Learning (ML) methods was proposed, together with a brief discussion of the different ML approaches involved. The studies reporting the best results in terms of average classification accuracy were identified, supporting that transfer learning methods seem to perform better than other approaches. A discussion is proposed on the impact of (i) the emotion theoretical models and (ii) psychological screening of the experimental sample on the classifier performances.

Chaotic nature of the electroencephalogram during shallow and deep anesthesia: From analysis of the Lyapunov exponent

In conscious states, the electrodynamics of the cortex are reported to work near a critical point or phase transition of chaotic dynamics, known as the edge-of-chaos, representing a boundary between stability and chaos. Transitions away from this boundary disrupt cortical information processing and induce a loss of consciousness. The entropy of the electroencephalogram (EEG) is known to decrease as the level of anesthesia deepens. However, whether the chaotic dynamics of electroencephalographic activity shift from this boundary to the side of stability or the side of chaotic enhancement during anesthesia-induced loss of consciousness remains poorly understood. We investigated the chaotic properties of EEGs at two different depths of clinical anesthesia using the maximum Lyapunov exponent, which is mathematically regarded as a formal measure of chaotic nature, using the Rosenstein algorithm. In 14 adult patients, 12 s of electroencephalographic signals were selected during two depths of clinical anesthesia (sevoflurane concentration 2% as relatively deep anesthesia, sevoflurane concentration 0.6% as relatively shallow anesthesia). Lyapunov exponents, correlation dimensions and approximate entropy were calculated from these electroencephalographic signals. As a result, maximum Lyapunov exponent was generally positive during sevoflurane anesthesia, and both maximum Lyapunov exponents and correlation dimensions were significantly greater during deep anesthesia than during shallow anesthesia despite reductions in approximate entropy. The chaotic nature of the EEG might be increased at clinically deeper inhalational anesthesia, despite the decrease in randomness as reflected in the decreased entropy, suggesting a shift to the side of chaotic enhancement under anesthesia.

Classification of Alzheimer’s dementia EEG signals using deep learning

Alzheimer’s dementia (AD) is a predominant neurological disorder arising from corruptions in brain functions and is characterized by a chronic or progressive nature. While the precise etiology of dementia remains incompletely elucidated, its manifestation is frequently associated with discernible structural and chemical alterations in the brain. Living with dementia significantly impacts individuals’ daily lives due to the resultant loss of cognitive functions. This study presents a novel method to monitor and detect AD using advanced signal processing applied to electroencephalography (EEG) signals. The intrinsic time-scale decomposition (ITD) algorithm is employed to extract proper rotation components (PRCs) from EEG signals, utilizing a 5-second EEG segment duration. The proposed method is compared with the detection of 5-second raw EEG segments using a custom one-dimensional convolutional neural network (1D CNN). Additionally, four different quartiles (Quartile 1 (Q1), Q2, Q3, and Q4) of EEG signals are considered to identify the most significant contributor to AD. Experimental results demonstrate that the ITD-based approach yields better detection performance compared to using raw EEG signals. The most promising result is achieved by the EEG-PRCs method in Q1, with an accuracy of 94.00%, sensitivity of 93.50%, and specificity of 93.90%. In contrast, the highest-performing result of the raw EEG segments method is in Q2, with an accuracy of 88.40%, sensitivity of 89.10%, and specificity of 87.60% in terms of detecting AD.

Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery

Functional electrical stimulation (FES) can support functional restoration of a paretic limb post-stroke. Hebbian plasticity depends on temporally coinciding pre- and post-synaptic activity. A tight temporal relationship between motor cortical (MC) activity associated with attempted movement and FES-generated visuo-proprioceptive feedback is hypothesized to enhance motor recovery. Using a brain–computer interface (BCI) to classify MC spectral power in electroencephalographic (EEG) signals to trigger FES-delivery with detection of movement attempts improved motor outcomes in chronic stroke patients. We hypothesized that heightened neural plasticity earlier post-stroke would further enhance corticomuscular functional connectivity and motor recovery. We compared subcortical non-dominant hemisphere stroke patients in BCI-FES and Random-FES (FES temporally independent of MC movement attempt detection) groups. The primary outcome measure was the Fugl-Meyer Assessment, Upper Extremity (FMA-UE). We recorded high-density EEG and transcranial magnetic stimulation-induced motor evoked potentials before and after treatment. The BCI group showed greater: FMA-UE improvement; motor evoked potential amplitude; beta oscillatory power and long-range temporal correlation reduction over contralateral MC; and corticomuscular coherence with contralateral MC. These changes are consistent with enhanced post-stroke motor improvement when movement is synchronized with MC activity reflecting attempted movement.

Cortical activation in REM behavior disorder mimics voluntary movement. An electroencephalography study

ObjectivesMotor symptoms of Parkinson’s disease improve during REM sleep behavior disorder movement episodes. Our aim was to study cortical activity during these movement episodes, in patients with and without Parkinson’s disease, in order to investigate the cortical involvement in the generation of its electromyographic activity and its potential relationship with Parkinson’s disease. MethodsWe looked retrospectively in our polysomnography database for patients with REM sleep behavior disorder, analyzing fifteen patients in total, seven with idiopathic REM sleep behavior disorder and eight associated with Parkinson’s disease. We selected segments of REM sleep with the presence of movements (evidenced by electromyographic activation), and studied movement-related changes in cortical activity by averaging the electroencephalographic signal (premotor potential) and by means of time/frequency transforms. ResultsWe found a premotor potential and an energy decrease of alpha–beta oscillatory activity preceding the onset of electromyographic activity, together with an increase of gamma activity for the duration of the movement. All these changes were similarly present in REM sleep behavior disorder patients with and without Parkinson’s disease. ConclusionsMovement-related changes in electroencephalographic activity observed in REM sleep behavior disorder are similar to those observed during voluntary movements, regardless of the presence of Parkinson’s disease motor symptoms. SignificanceThese results suggest a main involvement of the cortex in the generation of the movements during REM sleep.

The effect of low-dose ketamine on electroencephalographic spectrum during gynecology surgery under desflurane anesthesia.

The perioperative administration of low-dose ketamine has shown potential in postoperative pain management, opioid sparing, and enhancing pain control. This study aimed to investigate the impact of low-dose ketamine on processed electroencephalography (EEG) signals during anesthesia. Forty patients with American Society of Anesthesiologists physical status I-II undergoing elective gynecological surgery were enrolled. EEG monitoring was initiated upon induction of anesthesia. Anesthesia was maintained with desflurane and alfentanil immediately after induction. Fifteen minutes after induction, the ketamine group received a 0.3 mg/kg bolus followed by 0.05 mg/kg/h infusion until completion of surgery. The control group received equivalent saline. Postoperative assessments included pain score (visual analog scale), morphine usage, and quality of recovery. The ketamine group had significantly higher Patient State Index (PSi) values at 10, 20, and 30 minutes after ketamine administration compared to the controls. Ketamine administration led to significant alterations in EEG patterns, including reduced relative power in delta and theta frequency bands, and increased relative power in beta and gamma frequency bands at 10 minutes post-administration. Relative power in the alpha frequency band significantly decreased at 10, 20, and 30 minutes post-administration. However, there were no differences in intraoperative alfentanil consumption, postoperative morphine usage, and pain scores between the two groups. Low-dose ketamine administration during desflurane anesthesia led to notable changes in EEG patterns and PSi values. These findings provide valuable insights into the impact of ketamine on brain activity, and offer essential information for clinical anesthesiologists.