The perception of salty taste is crucial for individuals to make healthy food choices.Yet,the brain electrophysiological signals underlying salty taste perception have been poorly described.In this study,electroenceph...The perception of salty taste is crucial for individuals to make healthy food choices.Yet,the brain electrophysiological signals underlying salty taste perception have been poorly described.In this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response signals.Applying this method to the EEG processing workflow can form a complete data processing framework.The results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the stimulation.Moreover,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.展开更多
Attention detection using electroencephalogram(EEG)signals has become a popular topic.However,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning method...Attention detection using electroencephalogram(EEG)signals has become a popular topic.However,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG signals.Therefore,this survey outlines recent advances in EEG-based attention detection within the past five years,with a primary focus on auditory attention detection(AAD)and attention level classification.First,we provide a brief overview of commonly used paradigms,preprocessing techniques,and artifact-handling methods,as well as listing accessible datasets used in these studies.Next,we summarize the machine learning methods for classification in this field and divide them into two categories:traditional machine learning methods and deep learning methods.We also analyse the most frequently used methods and discuss the factors influencing each technique′s performance and applicability.Finally,we discuss the existing challenges and future trends in this field.展开更多
Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Metho...Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.展开更多
Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall h...Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall health. However, there have been no studies on the effects of implant treatment on electroencephalography (EEG). In this study, we investigated the effects of restoration of masticatory function by implant treatment on EEG and stress. Methods: 13 subjects (6 males, 7 females, age 64.1 ± 5.8 years) who had lost masticatory function due to tooth loss and 11 healthy subjects (6 males, 5 females, age 47.6 ± 2.4 years) as a control group. EEG (θ, α, β waves, α/β ratio) and salivary cortisol were measured before immediate dental implant treatment and every month of treatment for 6 months. EEG (θ, α, β waves, α/β ratio) was measured with a simple electroencephalograph miniature DAQ terminal (Intercross-410, Intercross Co., Ltd., Japan) in a resting closed-eye condition, and salivary cortisol was measured using an ELISA kit. Results: Compared to the control group, the appearance of θ and α waves were significantly decreased and β waves were increased, and α/β ratio was significantly decreased. The cortisol level of the subject group was significantly higher compared with the control group. With the course of implant treatment, the appearance of θ and α waves of the subject group increased, while β waves decreased. However, no significant difference was observed. The α/β ratio of the subject group increased from the first month after implant treatment and increased significantly after 5 and 6 months (0 vs. 5 months: p < 0.05, 0 vs. 6 months: p < 0.01). The cortisol levels in the subject group decreased from the first month after implant treatment and significantly decreased after 3 or 4 months (0 vs. 3 months: p < 0.05, 0 vs. 4 months: p < 0.01). These results suggest that tooth loss causes mental stress, which decreases brain stimulation and affects function. Restoration of masticatory function by implants was suggested to alleviate the effects on brain function and stress.展开更多
基金supported in part by the National Natural Science Foundation of China(31871882)National Key Research and Development Program of China(2021YFC2800403,YF-SHJD2101-3)。
文摘The perception of salty taste is crucial for individuals to make healthy food choices.Yet,the brain electrophysiological signals underlying salty taste perception have been poorly described.In this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response signals.Applying this method to the EEG processing workflow can form a complete data processing framework.The results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the stimulation.Moreover,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.
基金supported by the National Natural Science Foundation of China(Nos.62136004,62276130 and 62406131)the National Key R&D Program of China(No.2023YFF1204803)Key Research and Development Plan of Jiangsu Province,China(No.BE2022842).
文摘Attention detection using electroencephalogram(EEG)signals has become a popular topic.However,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG signals.Therefore,this survey outlines recent advances in EEG-based attention detection within the past five years,with a primary focus on auditory attention detection(AAD)and attention level classification.First,we provide a brief overview of commonly used paradigms,preprocessing techniques,and artifact-handling methods,as well as listing accessible datasets used in these studies.Next,we summarize the machine learning methods for classification in this field and divide them into two categories:traditional machine learning methods and deep learning methods.We also analyse the most frequently used methods and discuss the factors influencing each technique′s performance and applicability.Finally,we discuss the existing challenges and future trends in this field.
基金This study received financial support from the Jilin Province Health and Technology Capacity Enhancement Project(Project Number:222Lc132).
文摘Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.
文摘Purpose: Implant therapy restores masticatory function by restoring lost tooth morphology. It has been shown that mastication contributes not only to food intake and digestion, but also to the improvement of overall health. However, there have been no studies on the effects of implant treatment on electroencephalography (EEG). In this study, we investigated the effects of restoration of masticatory function by implant treatment on EEG and stress. Methods: 13 subjects (6 males, 7 females, age 64.1 ± 5.8 years) who had lost masticatory function due to tooth loss and 11 healthy subjects (6 males, 5 females, age 47.6 ± 2.4 years) as a control group. EEG (θ, α, β waves, α/β ratio) and salivary cortisol were measured before immediate dental implant treatment and every month of treatment for 6 months. EEG (θ, α, β waves, α/β ratio) was measured with a simple electroencephalograph miniature DAQ terminal (Intercross-410, Intercross Co., Ltd., Japan) in a resting closed-eye condition, and salivary cortisol was measured using an ELISA kit. Results: Compared to the control group, the appearance of θ and α waves were significantly decreased and β waves were increased, and α/β ratio was significantly decreased. The cortisol level of the subject group was significantly higher compared with the control group. With the course of implant treatment, the appearance of θ and α waves of the subject group increased, while β waves decreased. However, no significant difference was observed. The α/β ratio of the subject group increased from the first month after implant treatment and increased significantly after 5 and 6 months (0 vs. 5 months: p < 0.05, 0 vs. 6 months: p < 0.01). The cortisol levels in the subject group decreased from the first month after implant treatment and significantly decreased after 3 or 4 months (0 vs. 3 months: p < 0.05, 0 vs. 4 months: p < 0.01). These results suggest that tooth loss causes mental stress, which decreases brain stimulation and affects function. Restoration of masticatory function by implants was suggested to alleviate the effects on brain function and stress.
