Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fund...Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.展开更多
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
文摘Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.
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.
