As the relevance between left and right brain neurons when transmitting electrical signals of umami taste is unknown,the aim of this work was to investigate responsive regions of the brain to the umami tastant monosod...As the relevance between left and right brain neurons when transmitting electrical signals of umami taste is unknown,the aim of this work was to investigate responsive regions of the brain to the umami tastant monosodium glutamate(MSG)by using scalp-electroencephalogram(EEG)to identify the most responsive brain regions to MSG.Three concentrations of MSG(0.05,0.12,0.26 g/100 mL)were provided to participants for tasting while recoding their responsive reaction times and brain activities.The results indicated that the most responsive frequency to MSG was at 2 Hz,while the most responsive brain regions were T4 CzA2,F8 CzA2,and Fp2 CzA2.Moreover,the sensitivity of the brain to MSG was significantly higher in the right brain region.This study shows the potential of using EEG to investigate the relevance between different brains response to umami taste,which contributes to better understanding the mechanism of umami perception.展开更多
In present work,EEG and BP were used as the indexes to observe the relationbetween the change of EEG and the change of BP in the endotoxic shocked rats。At maintainingshock for 1 hr,dysrhythmia of EEG appeared in 38/4...In present work,EEG and BP were used as the indexes to observe the relationbetween the change of EEG and the change of BP in the endotoxic shocked rats。At maintainingshock for 1 hr,dysrhythmia of EEG appeared in 38/46 cases.Simultaneously,there was a markeddrop in Bp,P【0.05.Following the shocked time prolonged,dysrhythmia was getting severe。AfterEA”Rengzhong"(n=14)or“Zusanli”(n=12),BP was significantly increased(P【0.05),anddysrhythmia of EEG showed clear improvement in most of the rats。There was a close relation be-tween the changes of EEG and BP,the change of EEG had a direct bearing on the change of BP.展开更多
Objective To investigate relationship between prognosis of infant spasm and electroencephalogram(EEG) and head CT.Method 47 infants underwent EEG and head CT.Follow up was performed to compare the prognosis during dif...Objective To investigate relationship between prognosis of infant spasm and electroencephalogram(EEG) and head CT.Method 47 infants underwent EEG and head CT.Follow up was performed to compare the prognosis during different periods.Result Among 31 infants with abnormal head CT,2 infants were cured,17 were improved and effective rate was 61.3%. Among 16 patients with normal head CT,6 were cured,8 were improved,and effective rate was 87.5%. Among 34 infants with high rhythm disorder,8 were cured,21 were improved,effective rate was 85.29%. For 13 infants with abnormal EEG of other types,no infants were cured,4 were improved,and effective rate was 30.8%.Conclusion Changed head CT not various EEG has no significant effect on prognosis of infant spasm(P >0.05).Prognosis is favorable in infants with high rhythm disorder(P<0.01).展开更多
Background: EEG could be normal or atypical in spite of suggestive clinical features and positive measles Ab of SSPE cases which could have typical EEG pattern after Benzodiazepine. Objectives: The purpose of the pres...Background: EEG could be normal or atypical in spite of suggestive clinical features and positive measles Ab of SSPE cases which could have typical EEG pattern after Benzodiazepine. Objectives: The purpose of the present study was to find out the necessity of administration of benzodiazepine during EEG recording of SSPE cases as well as to compare the efficacy of diazepam and midazolam in eliciting EEG pattern. Methodology: This double blind, parallel, single centered, non-randomized clinical trial was conducted in the Department of Pediatric Neurology at National Institute of Neurosciences, Dhaka, Bangladesh from July 2014 to June 2015 for a period of 1 (one) year. All the clinical and investigational suspected cases of sub-acute Sclerosing Panencephalitis (SSPE) children in both sexes were included as study population. Others neurodegenerative diseases including Wilson’s disease were excluded from this study. Patients were divided into two groups named as group A who were given diazepam and the other group B was given midazolam in IV during EEG recording. The clinical outcomes were measured and were recorded in a pre-designed data sheet. Result: The characteristic typical periodic slow wave complex (PSWC) was found only in 8 (30.8%) patients among the 26 (100.0%) before intervention with benzodiazepines. The remaining 18 (69.2%) had non-typical PSWC of which 10 (38.5%) were normal, 3 (11.5%) with atypical PSWC and 5 (19.2%) were with other EEG findings. After intervention with benzodiazepines, 23 (88.5%) had shown typical PSWC and only 3 (11.5%) had non-typical PSWC. Among the typical PSWC cases after intervention, 8 (30.8%) had normal EEG initially, 5 (19.2%) had other EEG finding, 2 (7.7%) had non-typical PSWC and 8 (30.8%) had typical PSWC from the beginning. Of the 3 (11.5%) of the non-typical PSWC of intervention group, 2 (7.7%) had shown no changes in EEG from the beginning and 1 (3.8%) had shown other EEG finding. The difference between before and after intervention was actually statistically extremely significant (p 0.05). Conclusion: The role of benzodiazepine is very obvious in eliciting the typical EEG pattern in SSPE patients which has shown the characteristic PSWC in EEG in most cases.展开更多
The detection of alcoholism is of great importance due to its effects on individuals and society.Automatic alcoholism detection system(AADS)based on electroencephalogram(EEG)signals is effective,but the design of a ro...The detection of alcoholism is of great importance due to its effects on individuals and society.Automatic alcoholism detection system(AADS)based on electroencephalogram(EEG)signals is effective,but the design of a robust AADS is a challenging problem.AADS’current designs are based on conventional,hand-engineered methods and restricted performance.Driven by the excellent deep learning(DL)success in many recognition tasks,we implement an AAD system based on EEG signals using DL.A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD problem.In order to solve this problem,we propose a multi-channel Pyramidal neural convolutional(MP-CNN)network that requires a less number of learnable parameters.Using the deep CNN model,we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or normal.We validate the robustness and effectiveness of proposed AADS using KDD,a benchmark dataset for alcoholism detection problem.In order to find the brain region that contributes significant role in AAD,we investigated the effects of selected 19 EEG channels(SC-19),those from the whole brain(ALL-61),and 05 brain regions,i.e.,TEMP,OCCIP,CENT,FRONT,and PERI.The results show that SC-19 contributes significant role in AAD with the accuracy of 100%.The comparison reveals that the state-of-the-art systems are outperformed by the AADS.The proposed AADS will be useful in medical diagnosis research and health care systems.展开更多
Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the brain.An EEG headset is a wearable device that records electrophysiological data from the brain.This paper presents the design ...Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the brain.An EEG headset is a wearable device that records electrophysiological data from the brain.This paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV system.The electrode placement locations are modified under a 10–20 standard system.The fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and cost.First,the data is recorded from 20 subjects who used the EEG Headset,and signals were recorded.Secondly,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide changing.The raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,thesefindings have been compared to previously reported studies.We concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 percent.We also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use.展开更多
基金supported by the National Natural Science Foundation of China(31972198,31622042)the National Key R&D Program of China(2016YFD0400803,2016YFD0401501)。
文摘As the relevance between left and right brain neurons when transmitting electrical signals of umami taste is unknown,the aim of this work was to investigate responsive regions of the brain to the umami tastant monosodium glutamate(MSG)by using scalp-electroencephalogram(EEG)to identify the most responsive brain regions to MSG.Three concentrations of MSG(0.05,0.12,0.26 g/100 mL)were provided to participants for tasting while recoding their responsive reaction times and brain activities.The results indicated that the most responsive frequency to MSG was at 2 Hz,while the most responsive brain regions were T4 CzA2,F8 CzA2,and Fp2 CzA2.Moreover,the sensitivity of the brain to MSG was significantly higher in the right brain region.This study shows the potential of using EEG to investigate the relevance between different brains response to umami taste,which contributes to better understanding the mechanism of umami perception.
基金The Project Supported by National Natural Science Foundation of China
文摘In present work,EEG and BP were used as the indexes to observe the relationbetween the change of EEG and the change of BP in the endotoxic shocked rats。At maintainingshock for 1 hr,dysrhythmia of EEG appeared in 38/46 cases.Simultaneously,there was a markeddrop in Bp,P【0.05.Following the shocked time prolonged,dysrhythmia was getting severe。AfterEA”Rengzhong"(n=14)or“Zusanli”(n=12),BP was significantly increased(P【0.05),anddysrhythmia of EEG showed clear improvement in most of the rats。There was a close relation be-tween the changes of EEG and BP,the change of EEG had a direct bearing on the change of BP.
文摘Objective To investigate relationship between prognosis of infant spasm and electroencephalogram(EEG) and head CT.Method 47 infants underwent EEG and head CT.Follow up was performed to compare the prognosis during different periods.Result Among 31 infants with abnormal head CT,2 infants were cured,17 were improved and effective rate was 61.3%. Among 16 patients with normal head CT,6 were cured,8 were improved,and effective rate was 87.5%. Among 34 infants with high rhythm disorder,8 were cured,21 were improved,effective rate was 85.29%. For 13 infants with abnormal EEG of other types,no infants were cured,4 were improved,and effective rate was 30.8%.Conclusion Changed head CT not various EEG has no significant effect on prognosis of infant spasm(P >0.05).Prognosis is favorable in infants with high rhythm disorder(P<0.01).
文摘Background: EEG could be normal or atypical in spite of suggestive clinical features and positive measles Ab of SSPE cases which could have typical EEG pattern after Benzodiazepine. Objectives: The purpose of the present study was to find out the necessity of administration of benzodiazepine during EEG recording of SSPE cases as well as to compare the efficacy of diazepam and midazolam in eliciting EEG pattern. Methodology: This double blind, parallel, single centered, non-randomized clinical trial was conducted in the Department of Pediatric Neurology at National Institute of Neurosciences, Dhaka, Bangladesh from July 2014 to June 2015 for a period of 1 (one) year. All the clinical and investigational suspected cases of sub-acute Sclerosing Panencephalitis (SSPE) children in both sexes were included as study population. Others neurodegenerative diseases including Wilson’s disease were excluded from this study. Patients were divided into two groups named as group A who were given diazepam and the other group B was given midazolam in IV during EEG recording. The clinical outcomes were measured and were recorded in a pre-designed data sheet. Result: The characteristic typical periodic slow wave complex (PSWC) was found only in 8 (30.8%) patients among the 26 (100.0%) before intervention with benzodiazepines. The remaining 18 (69.2%) had non-typical PSWC of which 10 (38.5%) were normal, 3 (11.5%) with atypical PSWC and 5 (19.2%) were with other EEG findings. After intervention with benzodiazepines, 23 (88.5%) had shown typical PSWC and only 3 (11.5%) had non-typical PSWC. Among the typical PSWC cases after intervention, 8 (30.8%) had normal EEG initially, 5 (19.2%) had other EEG finding, 2 (7.7%) had non-typical PSWC and 8 (30.8%) had typical PSWC from the beginning. Of the 3 (11.5%) of the non-typical PSWC of intervention group, 2 (7.7%) had shown no changes in EEG from the beginning and 1 (3.8%) had shown other EEG finding. The difference between before and after intervention was actually statistically extremely significant (p 0.05). Conclusion: The role of benzodiazepine is very obvious in eliciting the typical EEG pattern in SSPE patients which has shown the characteristic PSWC in EEG in most cases.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,“Ministry of Education”in Saudi Arabia for funding this research work through the Project No.IFKSURG-1439-067.
文摘The detection of alcoholism is of great importance due to its effects on individuals and society.Automatic alcoholism detection system(AADS)based on electroencephalogram(EEG)signals is effective,but the design of a robust AADS is a challenging problem.AADS’current designs are based on conventional,hand-engineered methods and restricted performance.Driven by the excellent deep learning(DL)success in many recognition tasks,we implement an AAD system based on EEG signals using DL.A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD problem.In order to solve this problem,we propose a multi-channel Pyramidal neural convolutional(MP-CNN)network that requires a less number of learnable parameters.Using the deep CNN model,we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or normal.We validate the robustness and effectiveness of proposed AADS using KDD,a benchmark dataset for alcoholism detection problem.In order to find the brain region that contributes significant role in AAD,we investigated the effects of selected 19 EEG channels(SC-19),those from the whole brain(ALL-61),and 05 brain regions,i.e.,TEMP,OCCIP,CENT,FRONT,and PERI.The results show that SC-19 contributes significant role in AAD with the accuracy of 100%.The comparison reveals that the state-of-the-art systems are outperformed by the AADS.The proposed AADS will be useful in medical diagnosis research and health care systems.
基金funded this work(DSR),King Abdulaziz University,Jeddah,Saudi Arabia,under grant no.(RG-18-130-43).
文摘Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the brain.An EEG headset is a wearable device that records electrophysiological data from the brain.This paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV system.The electrode placement locations are modified under a 10–20 standard system.The fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and cost.First,the data is recorded from 20 subjects who used the EEG Headset,and signals were recorded.Secondly,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide changing.The raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,thesefindings have been compared to previously reported studies.We concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 percent.We also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use.
