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Alcoholic beverages flavour is complex and unique with different alcohol content, and the application of flavour perception could improve the objectivity of flavour evaluation. This study utilized electroencephalogram (EEG) to assess brain reactions to alcohol percentages (5 %-53 %) and Baijiu's complex flavours. The findings demonstrate the brain's proficiency in discerning between alcohol concentrations, evidenced by increasing physiological signal strength in tandem with alcohol content. When contrasted with alcohol solutions of equivalent concentrations, Baijiu prompts a more significant activation of brain signals, underscoring EEG's capability to detect subtleties due to flavour complexity. Additionally, the study reveals notable correlations, with δ and α wave intensities escalating in response to alcohol stimulation, coupled with substantial activation in the frontal, parietal, and right temporal regions. These insights verify the efficacy of EEG in charting the brain's engagement with alcoholic flavours, setting the stage for more detailed exploration into the neural encoding of these sensory experiences.

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OBJECTIVE: Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain-computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multichannel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance. APPROACH: We explicitly model the inter-channel relationships using the self attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named Spatial-Temporal Fusion Network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships. MAIN RESULTS: The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU. SIGNIFICANCE: The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments.

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BACKGROUND: Studies show that the two peak heights of electroencephalographic bicoherence (pBIC-high, pBIC-low) decrease after incision and are restored by fentanyl administration. We investigated whether pBICs are good indicators for adequacy of analgesia during surgery. METHODS: After local ethical committee approval, we enrolled 50 patients (27-65 years, ASA-PS I or II) who were scheduled elective surgery. Besides standard anesthesia monitors, to assess pBICs, we used a BIS monitor and freeware Bispectrum Analyzer for A2000. Fentanyl 5 µg/kg was completely administered before incision, and anesthesia was maintained with sevoflurane. After skin incision, when the peak of pBIC-high or pBIC-low decreased by 10% in absolute value (named LT10-high and LT10-low groups in order) or when either peak decreased to below 20% (BL20-high and BL20-low groups), an additional 1 g/kg of fentanyl was administered to examine its effect on the peak that showed a decrease. RESULTS: The mean values and standard deviation for pBIC-high 5 min before fentanyl administration, at the time of fentanyl administration, and 5 min after fentanyl administration for LT10-high group were 39.8% (10.9%), 26.9% (10.5%), and 35.7% (12.5%). And those for pBIC-low for LT10-low group were 39.5% (6.0%), 26.8% (6.4%) and 35.0% (7.0%). Those for pBIC-high for BL20-high group were 26.3% (5.6%), 16.5% (2.6%), and 25.7% (7.0%). And those for pBIC-low for BL20-low group were 26.7% (4.8%), 17.4% (1.8%) and 26.9% (5.7%), respectively. Meanwhile, at these trigger points, hemodynamic parameters didn't show significant changes. CONCLUSION: Superior to standard anesthesia monitoring, pBICs are better indicators of analgesia during surgery. TRIAL REGISTRY: Clinical trial Number and registry URL: UMIN ID: UMIN000042843 https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno = R000048907.

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In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring a large dataset for training to build reliable and accurate models. However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs. This leads to the data scarcity problem, which causes overfitting issues to training samples, resulting in reducing generalization performance. To solve the EEG data scarcity problem and improve the performance of the EEG classifiers, we propose a novel EEG data augmentation (DA) framework using conditional generative adversarial networks (cGANs). An experimental study is implemented with two public EEG datasets, including motor imagery (MI) tasks (BCI competition IV IIa and III IVa), to validate the effectiveness of the proposed EEG DA method for the EEG classifiers. To evaluate the proposed cGAN-based DA method, we tested eight EEG classifiers for the experiment, including traditional MLs and state-of-the-art DLs with three existing EEG DA methods. Experimental results showed that most DA methods with proper DA proportion in the training dataset had higher classification performances than without DA. Moreover, applying the proposed DA method showed superior classification performance improvement than the other DA methods. This shows that the proposed method is a promising EEG DA method for enhancing the performances of the EEG classifiers in MI-based BCIs.

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This study aimed to analyze the frequency of unexpected subclinical spikes (USCS) in pediatric patients who underwent high-density electroencephalogram (HD-EEG). Of the 4481 successful HD-EEG studies, 18.5% (829) were abnormal, and 49.7% of these abnormal studies showed SCS, of which 64.1% were USCS. USCS were found to be correlated with attention/concentration deficits and executive dysfunction, often accompanied by the dual psychiatric diagnosis of ADHD. MRI revealed abnormal findings in 32.6% of the subjects with USCS, such as abnormal signal or signal hyperintensity in brain parenchyma, temporal or arachnoid cysts, and vascular malformations. Moreover, the USCS group who received neuropsychiatric testing scored lower than the population mean on Full-Scale Intelligence Quotient, Working Memory Index, and Processing Speed Index. This study highlights the potential of USCS as biomarkers that can lead to changes in clinical management and outcomes, provide valuable information about pathophysiological mechanisms, and suggest potential treatment pathways.

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Objective: Various electroencephalogram-based monitors have been introduced to objectively quantify anaesthesia depth. However, limited data are available on their comparative clinical efficacy in various surgical procedures. Therefore, we planned this study to compare the relative efficacy of patient state index (PSI) vs. Bi-spectral index (BIS) assessment in patients undergoing elective spine surgery under general anaesthesia. Methods: This prospective, parallel-group, single-center study included patients undergoing major spine surgery with neuromonitoring. Patients were randomized into two groups, i.e., group B (undergoing surgery under BIS monitoring) and group P (undergoing surgery under PSI monitoring). The primary objective was to compare the time to eye opening after stopping anaesthetic drug infusions. Results: The mean propofol dose required for induction in group B was 130.45±26.579, whereas that in group P, it was 139.28±17.86 (P value 0.085). The maintenance doses of propofol and fentanyl required for surgery were also comparable between the groups. Time to eye opening was 12.2±4.973 in group B and 12.93±4.19 in group P, with a P value of 0.2664 (U-statistic-684.50). Conclusion: The intraoperative PSI and BIS had similar clinical efficacy in terms of the dose of propofol required for induction, time of induction, maintenance dose of propofol and fentanyl, time of eye opening, and recovery profile in patients undergoing elective spine surgery under neuromonitoring.

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Frequency analysis via electroencephalography (EEG) during general anesthesia is used to develop techniques for measuring anesthesia depth. Variational mode decomposition (VMD) enables mathematical optimization methods to decompose EEG signals into natural number intrinsic mode functions with distinct narrow bands. However, the analysis requires the a priori determination of hyperparameters, including the decomposition number (K) and the penalty factor (PF). In the VMD analysis of EEGs derived from a noninterventional and noninvasive retrospective observational study, we adapted the grey wolf optimizer (GWO) to determine the K and PF hyperparameters of the VMD. As a metric for optimization, we calculated the envelope function of the IMF decomposed via the VMD method and used its envelope entropy as the fitness function. The K and PF values varied in each epoch, with one epoch being the analytical unit of EEG; however, the fitness values showed convergence at an early stage in the GWO algorithm. The K value was set to 2 to capture the α wave enhancement observed during the maintenance phase of general anesthesia in intrinsic mode function 2 (IMF-2). This study suggests that using the GWO to optimize VMD hyperparameters enables the construction of a robust analytical model for examining the EEG frequency characteristics involved in the effects of general anesthesia.

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Bai et al investigate the predictive value of T lymphocyte proportion in Alzheimer's disease (AD) prognosis. Through a retrospective study involving 62 AD patients, they found that a decrease in T lymphocyte proportion correlated with a poorer prognosis, as indicated by higher modified Rankin scale scores. While the study highlights the potential of T lymphocyte proportion as a prognostic marker, it suggests the need for larger, multicenter studies to enhance generalizability and validity. Additionally, future research could use cognitive exams when evaluating prognosis and delve into immune mechanisms underlying AD progression. Despite limitations inherent in retrospective designs, Bai et al's work contributes to understanding the immune system's role in AD prognosis, paving the way for further exploration in this under-researched area.

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BACKGROUND: Type A aortic dissection presents challenges with postoperative cerebral complications, and this study evaluates the predictive value of quantitative electroencephalography for perioperative brain function prognosis. METHODS AND RESULTS: Amplitude-integrated electroencephalography (aEEG) processes raw signals through filtering, amplitude integration, and time compression, displaying the data in a semilogarithmic format. Using this method, postoperative relative band power (post-RBP) α% and dynamic aEEG (ΔaEEG) grade were significantly associated with neurological dysfunction in univariate and multivariable analyses, with area under the receiver operating characteristic curve of 0.876 (95% CI, 0.825-0.926) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with adverse outcomes, with area under the receiver operating characteristic curve of 0.903 (95% CI, 0.835-0.971) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with transient neurological dysfunction and stroke, with areas under the receiver operating characteristic curve of 0.818 (95% CI, 0.760-0.876) and 0.868 (95% CI, 0.810-0.926) for transient neurological dysfunction, and 0.815 (95% CI, 0.743-0.886) and 0.831 (95% CI, 0.746-0.916) for stroke. Among 56 patients, the Alberta Stroke Program Early Computed Tomography score was superior to ΔaEEG in predicting neurological outcomes (area under the receiver operating characteristic curve of 0.872 versus 0.708 [95% CI, 0.633-0.783]; P<0.05). CONCLUSIONS: Perioperative quantitative electroencephalography monitoring offers valuable insights into brain function changes in patients with type A aortic dissection. ∆aEEG grades can aid in early detection of adverse outcomes, while postoperative relative band power and ∆aEEG grades predict transient neurological dysfunction. Quantitative electroencephalography can assist cardiac surgeons in assessing brain function and improving outcomes in patients with type A aortic dissection. REGISTRATION: URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2200055980.

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General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.

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OBJECTIVE: Early identification of infants at risk of cerebral palsy (CP) enables interventions to optimize outcomes. Central sleep spindles reflect thalamocortical sensorimotor circuit function. We hypothesized that abnormal infant central spindle activity would predict later contralateral CP. METHODS: We trained and validated an automated detector to measure spindle rate, duration, and percentage from central electroencephalogram (EEG) channels in high-risk infants (n = 35) and age-matched controls (n = 42). Neonatal magnetic resonance imaging (MRI) findings, infant motor exam, and CP outcomes were obtained from chart review. Using univariable and multivariable logistic regression models, we examined whether spindle activity, MRI abnormalities, and/or motor exam predicted future contralateral CP. RESULTS: The detector had excellent performance (F1 = 0.50). Spindle rate (p = 0.005, p = 0.0004), duration (p < 0.001, p < 0.001), and percentage (p < 0.001, p < 0.001) were decreased in hemispheres corresponding to future CP compared to those without. In this cohort, PLIC abnormality (p = 0.004) and any MRI abnormality (p = 0.004) also predicted subsequent CP. After controlling for MRI findings, spindle features remained significant predictors and improved model fit (p < 0.001, all tests). Using both spindle duration and MRI findings had highest accuracy to classify hemispheres corresponding to future CP (F1 = 0.98, AUC 0.999). CONCLUSION: Decreased central spindle activity improves the prediction of future CP in high-risk infants beyond early MRI or clinical exam alone. SIGNIFICANCE: Decreased central spindle activity provides an early biomarker for CP.

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The MIMED dataset is a dataset that provides raw electroencephalogram signal data for activities: raising the right-hand, lowering the right-hand, raising the left-hand, lowering the left-hand, standing, and sitting. In addition to raw data, this dataset provides feature data that undergoes a baseline reduction process. The baseline reduction process is a process to increase the value of EEG signal features. The feature values ​​of the enhanced EEG signal can be easily recognized in the classification process. The device used is Emotiv Epoc X, which consists of 14 channels. Participants involved in this experiment were 30 students from the Bali region in Indonesia. Four recording scenarios were carried out on the first day and four further scenarios on the second day. Two datasets were obtained based on the recording scenario: the motor movement and image datasets. The duration of motor execution is 40 minutes, while motor imagery is 8 minutes for each scenario.

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BACKGROUND: Childhood adversity has been associated with alterations in threat-related information processing, including heightened perceptual sensitivity and attention bias towards threatening facial expressions, as well as hostile attributions of neutral faces, although there is a large degree of variability and inconsistency in reported findings. METHODS: Here, we aimed to implicitly measure neural facial expression processing in 120 adolescents between 12 and 16 years old with and without exposure to childhood adversity. Participants were excluded if they had any major medical or neurological disorder or intellectual disability, were pregnant, used psychotropic medication or reported acute suicidality or an ongoing abusive situation. We combined fast periodic visual stimulation with electroencephalography in two separate paradigms to assess the neural sensitivity and responsivity towards neutral and expressive, i.e. happy and angry, faces. Linear mixed effects models were used to assess the impact of childhood adversity on facial expression processing. RESULTS: Sixty-six girls, 53 boys and one adolescent who identified as 'other', between 12 and 16 years old (M = 13.93), participated in the current study. Of those, 64 participants were exposed to childhood adversity. In contrast to our hypotheses, adolescents exposed to adversity show lower expression-discrimination responses for angry faces presented in between neutral faces and higher expression-discrimination responses for happy faces presented in between neutral faces than unexposed controls. Moreover, adolescents exposed to adversity, but not unexposed controls, showed lower neural responsivity to both angry and neutral faces that were simultaneously presented. CONCLUSIONS: We therefore conclude that childhood adversity is associated with a hostile attribution of neutral faces, thereby reducing the dissimilarity between neutral and angry faces. This reduced threat-safety discrimination may increase risk for psychopathology in individuals exposed to childhood adversity.

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Background and objectives: Several studies have reported on the resting-state electroencephalogram (EEG) power in patients with schizophrenia, with a decrease in α (especially α2) and an increase in δ and ß1 power compared with healthy control; however, reports on at-risk mental states (ARMS) are few. In this study, we measured the resting-state EEG power in ARMS, and investigated its features and the relationship between the power of the frequency bands and their diagnostic outcomes. Methods: Patients with ARMS who were not on any psychotropic medication and met the Comprehensive Assessment of At-Risk Mental State criteria were included. Patients who developed psychotic disorders were labeled as the ARMS-P group, while patients with ARMS who were followed up prospectively for more than 2 years and did not develop psychotic disorders were classified as the ARMS-NP group. EEGs were measured in the resting state, and frequencies were analyzed using standardized low-resolution brain electromagnetic tomography (sLORETA). Seven bands (δ, θ, α1, α2, ß1-3) underwent analysis. The sLORETA values (current source density [CSD]) were compared between the ARMS-P and ARMS-NP groups. Clinical symptoms were assessed at the time of EEG measurements using the Positive and Negative Syndrome Scale (PANSS). Results: Of the 39 patients included (25 males, 14 females, 18.8 ± 4.5 years old), eight developed psychotic disorders (ARMS-P). The ARMS-P group exhibited significantly higher CSD in the ß1 power within areas of the left middle frontal gyrus (MFG) compared with the ARMS-NP group (best match: X = -35, Y = 25, Z = 50 [MNI coordinates], Area 8, CSD = 2.33, p < 0.05). There was a significant positive correlation between the ß1/α ratio of the CSD at left MFG and the Somatic concern score measured by the PANSS. Discussion: Increased ß1 power was observed in the resting EEG before the onset of psychosis and correlated with a symptom. This suggests that resting EEG power may be a useful marker for predicting future conversion to psychosis and clinical symptoms in patients with ARMS.

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Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person's comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum Disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

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Near-death experience (NDE) is a transcendent mental event of uncertain etiology that arises on the cusp of biological death. Since the discovery of NDE in the mid-1970s, multiple neuroscientific theories have been developed in an attempt to account for it in strictly materialistic or reductionistic terms. Therefore, in this conception, NDE is at most an extraordinary hallucination without any otherworldly, spiritual, or supernatural denotations. During the last decade or so, a number of animal and clinical studies have emerged which reported that about the time of death, there may be a surge of high frequency electroencephalogram (EEG) at a time when cortical electrical activity is otherwise at a very low ebb. This oscillatory rhythm falls within the range of the enigmatic brain wave-labelled gamma-band activity (GBA). Therefore, it has been proposed that this brief, paradoxical, and perimortem burst of the GBA may represent the neural foundation of the NDE. This study examines three separate but related questions concerning this phenomenon. The first problem pertains to the electrogenesis of standard GBA and the extent to which authentic cerebral activity has been contaminated by myogenic artifacts. The second problem involves the question of whether agents that can mimic NDE are also underlain by GBA. The third question concerns the electrogenesis of the surge in GBA itself. It has been contended that this is neither cortical nor myogenic in origin. Rather, it arises in a subcortical (amygdaloid) location but is recorded at the cortex via volume conduction, thereby mimicking standard GBA. Although this surge of GBA contains genuine electrophysiological activity and is an intriguing and provocative finding, there is little evidence to suggest that it could act as a kind of neurobiological skeleton for a phenomenon such as NDE.

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Introduction: the objective of the study was to find out the microstate map topographies and their parameters generated during the resting state and during listening to North Indian classical Music Raag 'the Raag Bilawal'. It was hypothesized that in the resting state and during listening to music conditions, there would be a difference in microstate parameters i.e. mean duration, global explained variance (GEV), and time coverage. Methods: a 128-channel electroencephalogram (EEG) was recorded for 12 Indian subjects (average age 26.1+1.4 years) while resting and listening to music using the EEG microstate investigation. Investigation and comparison of the microstate parameters were the mean duration, global explained variance (GEV), and time coverage between both conditions were performed. Results: seven microstate maps were found to represent the resting state and listening to music condition, four canonical and three novel maps. No statistically significant difference was found between the two conditions for time coverage and mean duration. The statistical significance levels of the map-1, map-2, map-3, map-4, map-5, map-6, and map-7 for the mean duration were 0.4, 0.6, 0.97, 0.34, 0.32, 0.69, and 0.29 respectively; and for time coverage were 0.92, 0.92, 0.96, 0.64, 0.78, 0.38, and 0.76 respectively. Map-1, map-4, and map-7 were the three novel maps we found in our study. Conclusion: similarities regarding stability and predominance of maps with small vulnerability exist in both conditions indicating that phonological, visual, and dorsal attention networks may be activated in both resting state and listening to music condition.

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Purpose: Stroke is the second leading cause of global deaths. Post-stroke seizures (PSS) can lead to lasting complications, such as prolonged hospitalizations, increased disability rates, and higher mortality. Our study investigates the associated factors that contribute to post-stroke seizures in patients at a local tertiary hospital. Patients and Methods: We designed a case-control study where patients admitted with PSS were recruited with consent. Controls admitted for stroke without seizure were then included. Suitability based on exclusion criteria was ensured before recording their sociodemographic and clinical data. An EEG was performed and read by two certified neurologists before the data was analyzed. Results: We recruited 180 participants, 90 cases and 90 matched controls. Gender (p=0.013), race (p=0.015), dyslipidemia (p<0.001), prior stroke (p<0.031), large artery atherosclerosis (p<0.001), small vessel occlusions (p<0.001), blood pressure on presentation (p<0.028) and thrombolysis administration (p<0.029) were significantly associated with the occurrence of PSS. An increase in odds of PSS was observed in the male gender (1.974), dyslipidemia (3.480), small vessel occlusions (4.578), and in participants with epileptiform changes on EEG (3.630). Conversely, lower odds of PSS were seen in participants with high blood pressure on presentation (0.505), large artery atherosclerosis (0.266), and those who underwent thrombolysis (0.319). Conclusion: This study emphasized that identifying post-stroke seizures may be aided by EEGs and recognizing at-risk groups, which include males of Chinese descent in Asia, dyslipidemia, small vessel occlusions, those with low to normal blood pressure on presentation, and epileptiform changes in EEGs.

The research aims to establish the risk factors associated with post-stroke seizures in an Asian population and their similarity to the Western literature. Our findings highlight the critical risk factors to identify in at-risk patients, which may prompt changes in guidelines in future to enhance patient outcomes and improve the quality of care.

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Individuals with motor dysfunction caused by damage to the central nervous system are unable to transmit voluntary movement commands to their muscles, resulting in a reduced ability to control their limbs. However, traditional rehabilitation methods have problems such as long treatment cycles and high labor costs. Functional electrical stimulation (FES) based on brain-computer interface (BCI) connects the patient's intentions with muscle contraction, and helps to promote the reconstruction of nerve function by recognizing nerve signals and stimulating the moving muscle group with electrical impulses to produce muscle convulsions or limb movements. It is an effective treatment for sequelae of neurological diseases such as stroke and spinal cord injury. This article reviewed the current research status of BCI-based FES from three aspects: BCI paradigms, FES parameters and rehabilitation efficacy, and looked forward to the future development trend of this technology, in order to improve the understanding of BCI-based FES.

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Background: Holmes tremor (HT) is a rare motor disorder characterized by high-amplitude and low-frequency resting, intentional, and postural tremors. HT typically arises from disruptions in neural pathways, including the dopaminergic system. Its causes include cerebrovascular incidents, neoplasms, demyelination, and infections. Diagnosis involves thorough clinical, neurophysiological, and neuroimaging assessments. Our report details the clinical profile, neuroimaging and EEG results and levodopa treatment response of an HT patient after cerebral arteriovenous malformation (AVM) surgery. Case Report: A female patient who underwent AVM surgery developed head tremor and dystonia. Neuroimaging revealed left thalamus involvement. Video electroencephalography (EEG) revealed high-amplitude, low-frequency tremors. The patient responded well to levodopa treatment. Conclusions: Involuntary rhythmic or non-rhythmic movements are a primary clinical feature of HT. A differential diagnosis of epilepsy and HT can be achieved through neurophysiological monitoring, avoiding the overuse of antiepileptic drugs. Symptoms can be alleviated with levodopa intervention.