Application of electroencephalogram (EEG) in the study of the influence of different contents of alcohol and Baijiu on brain perception.

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.

Insights into brain perceptions of the different taste qualities and hedonic valence of food via scalp electroencephalogram.

Investigating brain activity is essential for exploring taste-experience related cues. The paper aimed to explore implicit (unconscious) emotional or physiological responses related to taste experiences using scalp electroencephalogram (EEG). We performed implicit measures of tastants of differing perceptual types (bitter, salty, sour and sweet) and intensities (low, medium, and high). The results showed that subjects were partially sensitive to different sensory intensities, i.e., for high intensities, taste stimuli could induce activation of different rhythm signals in the brain, with α and θ bands possibly being more sensitive to different taste types. Furthermore, the neural representations and corresponding sensory qualities (e.g., "sweet: pleasant" or "bitter: unpleasant") of different tastes could be discriminated at 250-1,500 ms after stimulus onset, and different tastes exhibited distinct temporal dynamic differences. Source localization indicated that different taste types activate brain areas associated with emotional eating, reward processing, and motivated tendencies, etc. Overall, our findings reveal a larger sophisticated taste map that accounted for the diversity of taste types in the human brain and assesses the emotion, reward, and motivated behavior represented by different tastes. This study provided basic insights and a perceptual foundation for the relationship between taste experience-related decisions and the prediction of brain activity.

Observation of respiration-entrained brain oscillations with scalp EEG.

In animal models, oscillations of local field potentials are entrained by nasal respiration at the frequency of breathing cycle in olfactory brain regions, such as the olfactory bulb and piriform cortex, as well as in the other brain regions. Studies in humans also confirmed these respiration-entrained oscillations in several brain regions using intracranial electroencephalogram (EEG). Here we extend these findings by analyzing coherence between cortical activity and respiration using high-density scalp EEG in twenty-seven healthy human subjects. Results indicated the occurrence of significant coherence between scalp EEG and respiration signals, although the number and locations of electrodes showing significant coherence were different among subjects. These findings suggest that scalp EEG can detect respiration-entrained oscillations. It remained to be determined whether these oscillations are volume conducted from the olfactory brain regions or reflect the local cortical activity.

Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions.

Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of great significance for improving the quality of life of patients with epilepsy. In recent years, a number of studies based on deep learning methods have been proposed to address this issue and achieve excellent performance. However, most studies on epileptic seizure prediction by EEG fail to take full advantage of temporal-spatial multi-scale features of EEG signals, while EEG signals carry information in multiple temporal and spatial scales. To this end, in this study, we proposed an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction. Methods: Specifically, the model divides the EEG processing pipeline into two stages: the temporal multi-scale stage and the spatial multi-scale stage. In each stage, we firstly extract the multi-scale features along the corresponding dimension. A dilated convolution block is then conducted on these features to expand our model's receptive fields further and systematically aggregate global information. Furthermore, we adopt a feature-weighted fusion strategy based on an attention mechanism to achieve better feature fusion and eliminate redundancy in the dilated convolution block. Results: The proposed model obtains an average sensitivity of 93.3%, an average false prediction rate of 0.007 per hour, and an average proportion of time-in-warning of 6.3% testing in 16 patients from the CHB-MIT dataset with the leave-one-out method. Conclusion: Our model achieves superior performance in comparison to state-of-the-art methods, providing a promising solution for EEG-based seizure prediction.

Epileptic Seizure Prediction Using Deep Transformer Model.

The electroencephalogram (EEG) is the most promising and efficient technique to study epilepsy and record all the electrical activity going in our brain. Automated screening of epilepsy through data-driven algorithms reduces the manual workload of doctors to diagnose epilepsy. New algorithms are biased either towards signal processing or deep learning, which holds subjective advantages and disadvantages. The proposed pipeline is an end-to-end automated seizure prediction framework with a Fourier transform feature extraction and deep learning-based transformer model, a blend of signal processing and deep learning - this imbibes the potential features to automatically identify the attentive regions in EEG signals for effective screening. The proposed pipeline has demonstrated superior performance on the benchmark dataset with average sensitivity and false-positive rate per hour (FPR/h) as 98.46%, 94.83% and 0.12439, 0, respectively. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.

[Intelligence-aided diagnosis of Parkinson's disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network].

Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram.

Objective.Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencephalography (EEG). Because the visual detection of IEDs has various limitations, including high time consumption and high subjectivity, a faster, more robust, and automated IED detector is strongly in demand.Approach.Based on deep learning, we proposed an end-to-end framework with multi-scale morphologic features in the time domain and correlation in sensor space to recognize IEDs from raw scalp EEG.Main Results.Based on a balanced dataset of 30 patients with epilepsy, the results of the five-fold (leave-6-patients-out) cross-validation shows that our model achieved state-of-the-art detection performance (accuracy: 0.951, precision: 0.973, sensitivity: 0.938, specificity: 0.968, F1 score: 0.954, AUC: 0.973). Furthermore, our model maintained excellent IED detection rates in an independent test on three datasets.Significance.The proposed model could be used to assist neurologists in clinical EEG interpretation of patients with epilepsy. Additionally, this approach combines multi-level output and correlation among EEG sensors and provides new ideas for epileptic biomarker detection in scalp EEG.

Intelligence-aided diagnosis of Parkinson's disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network / 生物医学工程学杂志

Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.

Theta oscillation and functional connectivity alterations related to executive control in temporal lobe epilepsy with comorbid depression.

OBJECTIVE: Depression is widely acknowledged as the most common comorbidity of temporal lobe epilepsy (TLE), and executive control (EC) may be particularly impaired in patients with TLE with comorbid depression. The purpose of this study was to investigate brain network alterations in patients with TLE with or without depression using scalp electroencephalography (EEG), and to explore the potential mechanisms of TLE with comorbid depression. METHODS: Forty patients with TLE and 20 healthy controls (HC) were recruited for administered the BDI-II and HAMD-17 surveys. The patients with TLE were divided into those with depression (PDS, n = 20) and those without depression (nPDS, n = 20) according to the surveys. Neural oscillations and functional connectivity during performance of EC tasks were calculated during EEG. RESULTS: Theta oscillation and functional connectivity were significantly weakened in the PDS group compared to the nPDS and HC groups. Furthermore, the PDS group showed more serious EC dysfunction than nPDS group. CONCLUSIONS: Our results indicated that weakened theta oscillation and functional connectivity in the frontal lobe may be a mechanism of EC dysfunction in TLE with comorbid depression. SIGNIFICANCE: The present results suggest that the alterations in frontal lobe connections may help predict the depression in patients with TLE.

Functional brain network mechanism of executive control dysfunction in temporal lobe epilepsy.

BACKGROUND: Executive control dysfunction is observed in a sizable number of patients with temporal lobe epilepsy (TLE). Neural oscillations in the theta band are increasingly recognized as having a crucial role in executive control network. The purpose of this study was to investigate the alterations in the theta band in executive control network and explore the functional brain network mechanisms of executive control dysfunction in TLE patients. METHODS: A total of 20 TLE patients and 20 matched healthy controls (HCs) were recruited in the present study. All participants were trained to perform the executive control task by attention network test while the scalp electroencephalogram (EEG) data were recorded. The resting state signals were collected from the EEG in the subjects with quiet and closed eyes conditions. Functional connectivity among EEGs in the executive control network and resting state network were respectively calculated. RESULTS: We found the significant executive control impairment in the TLE group. Compared to the HCs, the TLE group showed significantly weaker functional connectivity among EEGs in the executive control network. Moreover, in the TLE group, we found that the functional connectivity was significantly positively correlated with accuracy and negatively correlated with EC_effect. In addition, the functional connectivity of the executive control network was significantly higher than that of the resting state network in the HCs. In the TLE group, however, there was no significant change in functional connectivity strengths between the executive control network and resting state network. CONCLUSION: Our results indicate that the decreased functional connectivity in theta band may provide a potential mechanism for executive control deficits in TLE patients.

Autoencoding of long-term scalp electroencephalogram to detect epileptic seizure for diagnosis support system.

INTRODUCTION: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among patients makes it difficult to develop universal features that are applicable for automatic seizure detection in all cases, and the rarity of seizures results in a lack of sufficient training data for classifiers. METHODS: To overcome these problems, we utilized an autoencoder (AE), which is often used for anomaly detection in the field of machine learning, to perform seizure detection. We hypothesized that multichannel EEG signals are compressible by AE owing to their spatio-temporal coupling and that the AE should be able to detect seizures as anomalous events from an interictal EEG. RESULTS: Through experiments, we found that the AE error was able to classify seizure and nonseizure states with a sensitivity of 100% in 22 out of 24 available test subjects and that the AE was better than the commercially available software BESA and Persyst for half of the test subjects. CONCLUSIONS: These results suggest that the AE error is a feasible candidate for a universal seizure detection feature.

Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images.

We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detection could benefit from convolutional neural networks because their visual recognition ability is comparable to that of humans. We explored image-based seizure detection by applying convolutional neural networks to long-term EEG that included epileptic seizures. After filtering, EEG data were divided into short segments based on a given time window and converted into plot EEG images, each of which was classified by convolutional neural networks as 'seizure' or 'non-seizure'. These resultant labels were then used to design a clinically practical index for seizure detection. The best true positive rate was obtained using a 1-s time window. The median true positive rate of convolutional neural networks labelling by seconds was 74%, which was higher than that of commercially available seizure detection software (20% by BESA and 31% by Persyst). For practical use, the median of detected seizure rate by minutes was 100% by convolutional neural networks, which was higher than the 73.3% by BESA and 81.7% by Persyst. The false alarm of convolutional neural networks' seizure detection was issued at 0.2 per hour, which appears acceptable for clinical practice. Moreover, we demonstrated that seizure detection improved when training was performed using EEG patterns similar to those of testing data, suggesting that adding a variety of seizure patterns to the training dataset will improve our method. Thus, artificial visual recognition by convolutional neural networks allows for seizure detection, which otherwise currently relies on skillful visual inspection by expert epileptologists during clinical diagnosis.

Recent advance in high frequency oscillation in scalp EEG signals in diagnoses and treatments of epilepsy / 中华神经医学杂志

For the last two decades, high-frequency oscillations have been considered highly correlated with brain tissue epileptic activity. High frequency oscillations are usually detected from electroencephalography (EEG) signals recorded by intracranial electrodes. Recent studies have shown that high frequency oscillations can also be detected in scalp EEG. As a safe, non-invasive and simple recording method, the study of scalp EEG in detecting high frequency oscillations has attracted wide attention. In this paper, we summarize the research progress and clinical significance of high-frequency oscillations in scalp EEG in the diagnoses and treatments of epilepsy.

Alterations of theta oscillation in executive control in temporal lobe epilepsy patients.

Attention dysfunction, especially executive control has been investigated within many types of diseases of the central nervous system. The present study aims to clarify alterations of the executive control (EC) network in patients with temporal lobe epilepsy (TLE). Twenty patients with TLE and 20 matched healthy control subjects participated in the attention network test (ANT), and scalp electroencephalogram (EEG) recordings were set up. The ANT was used to evaluate attention network behavior deficits. Power spectral density (PSD), coherence and correlation were used to detect power and oscillation alterations of attention network in patients with TLE. The most significant differences in executive control were found between patients with TLE and healthy control subjects. Power spectral density in the theta band, and coherence and correlation in the theta band in the frontal area were decreased in patients with TLE. Our results indicate that patients with TLE have severe attention dysfunction, especially in executive control. In addition, brain theta oscillation impairment in frontal area might be connected with poor executive control behavior. These findings will provide new insight into diagnosing and treating patients with temporal lobe epilepsy.