International Journal of Neural Systems最新文献_第9页

Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery. 基于深度卷积生成对抗网络的脑卒中后运动图像康复脑电数据增强。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-09-01 Epub Date: 2022-07-25 DOI: 10.1142/S0129065722500393

Fangzhou Xu, Gege Dong, Jincheng Li, Qingbo Yang, Lei Wang, Yanna Zhao, Yihao Yan, Jinzhao Zhao, Shaopeng Pang, Dongju Guo, Yang Zhang, Jiancai Leng

{"title":"Deep Convolution Generative Adversarial Network-Based Electroencephalogram Data Augmentation for Post-Stroke Rehabilitation with Motor Imagery.","authors":"Fangzhou Xu, Gege Dong, Jincheng Li, Qingbo Yang, Lei Wang, Yanna Zhao, Yihao Yan, Jinzhao Zhao, Shaopeng Pang, Dongju Guo, Yang Zhang, Jiancai Leng","doi":"10.1142/S0129065722500393","DOIUrl":"https://doi.org/10.1142/S0129065722500393","url":null,"abstract":"The motor imagery brain-computer interface (MI-BCI) system is currently one of the most advanced rehabilitation technologies, and it can be used to restore the motor function of stroke patients. The deep learning algorithms in the MI-BCI system require lots of training samples, but the electroencephalogram (EEG) data of stroke patients is quite scarce. Therefore, the expansion of EEG data has become an important part of stroke clinical rehabilitation research. In this paper, a deep convolution generative adversarial network (DCGAN) model is proposed to generate artificial EEG data and further expand the scale of the stroke dataset. First, multichannel one-dimensional EEG data is converted into a two-dimensional EEG spectrogram using EEG2Image based on the modified S-transform. Then, DCGAN is used to artificially generate EEG data based on MI. Finally, the validity of the generated artificial EEG data is proved. This paper preliminarily indicates that generating artificial stroke data is a promising strategy, which contributes to the further development of stroke clinical rehabilitation.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"32 9","pages":"2250039"},"PeriodicalIF":8.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40555015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods. 基于信号分解和机器学习方法的阿尔茨海默氏痴呆症检测。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-09-01 Epub Date: 2022-08-09 DOI: 10.1142/S0129065722500423

Ozlem Karabiber Cura, Aydin Akan, Gulce Cosku Yilmaz, Hatice Sabiha Ture

{"title":"Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods.","authors":"Ozlem Karabiber Cura, Aydin Akan, Gulce Cosku Yilmaz, Hatice Sabiha Ture","doi":"10.1142/S0129065722500423","DOIUrl":"https://doi.org/10.1142/S0129065722500423","url":null,"abstract":"Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer's dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"32 9","pages":"2250042"},"PeriodicalIF":8.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40596752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Parallel Binary Image Cryptosystem Via Spiking Neural Networks Variants. 基于脉冲神经网络变体的并行二值图像密码系统。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-08-01 Epub Date: 2021-02-26 DOI: 10.1142/S0129065721500143

Mingzhe Liu, Feixiang Zhao, Xin Jiang, Hong Zhang, Helen Zhou

引用次数: 5

A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-Based Manipulation Tasks. 在基于视觉的操作任务中探索尖峰小脑模型和机械臂的动态相互作用的神经机器人实施例。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-08-01 Epub Date: 2021-05-18 DOI: 10.1142/S0129065721500283

Omar Zahra, David Navarro-Alarcon, Silvia Tolu

{"title":"A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-Based Manipulation Tasks.","authors":"Omar Zahra, David Navarro-Alarcon, Silvia Tolu","doi":"10.1142/S0129065721500283","DOIUrl":"https://doi.org/10.1142/S0129065721500283","url":null,"abstract":"While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behavior. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepancies from the reference values. While most studies consider providing the teaching signal as error in joint-space, few studies consider the error in task-space and even fewer consider the spiking nature of the cerebellum on the cellular-level. In this study, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and Basket cells which are usually neglected in previous studies. To preserve the biological features of the cerebellum in the developed model, a hyperparameter optimization method tunes the network accordingly. The efficiency and biological plausibility of the proposed cerebellar-based controller is then demonstrated under different robotic manipulation tasks reproducing motor behavior observed in human reaching experiments.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"32 8","pages":"2150028"},"PeriodicalIF":8.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38911715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Announcement: The 2022 Hojjat Adeli Award for Outstanding Contributions in Neural Systems. 公告：2022年霍贾特·阿德利神经系统杰出贡献奖。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-07-16 DOI: 10.1142/s0129065714820012

引用次数: 0

Automated Interictal Epileptiform Discharge Detection From Scalp EEG Using Scalable Time-series Classification Approaches 基于可扩展时间序列分类方法的头皮脑电图间期癫痫样放电自动检测

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-07-10 DOI: 10.1101/2022.07.06.22277287

D. Nhu, M. Janmohamed, L. Shakhatreh, O. Gonen, P. Perucca, A. Gilligan, P. Kwan, T. O'Brien, C. Tan, L. Kuhlmann

{"title":"Automated Interictal Epileptiform Discharge Detection From Scalp EEG Using Scalable Time-series Classification Approaches","authors":"D. Nhu, M. Janmohamed, L. Shakhatreh, O. Gonen, P. Perucca, A. Gilligan, P. Kwan, T. O'Brien, C. Tan, L. Kuhlmann","doi":"10.1101/2022.07.06.22277287","DOIUrl":"https://doi.org/10.1101/2022.07.06.22277287","url":null,"abstract":"Deep learning for automated interictal epileptiform discharge (IED) detection has been topical with many published papers in recent years. All existing work viewed EEG signals as time-series and developed specific models for IED classification; however, general time-series classification (TSC) methods were not considered. Moreover, none of these methods were evaluated on any public datasets, making direct comparisons challenging. This paper explored two state-of-the-art convolutional-based TSC algorithms, InceptionTime and Minirocket, on IED detection. We fine-tuned and cross-evaluated them on two private and public (Temple University Events - TUEV) datasets and provided ready metrics for benchmarking future work. We observed that the optimal parameters correlated with the clinical duration of an IED and achieved the best AUC, AUPRC and F1 scores of 0.98, 0.80 and 0.77 on the private datasets, respectively. The AUC, AUPRC and F1 on TUEV were 0.99, 0.99 and 0.97, respectively. While algorithms trained on the private sets maintained the performance when tested on the TUEV data, those trained on TUEV could not generalise well to the private data. These results emerge from differences in the class distributions across datasets and indicate a need for public datasets with a better diversity of IED waveforms, background activities and artifacts to facilitate standardisation and benchmarking of algorithms.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"1 1","pages":"2350001"},"PeriodicalIF":8.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43519315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Epileptic Seizure Prediction Using Deep Neural Networks Via Transfer Learning and Multi-Feature Fusion 基于迁移学习和多特征融合的深度神经网络癫痫发作预测

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-06-11 DOI: 10.1142/S0129065722500320

Zuyi Yu, L. Albera, R. Bouquin-Jeannès, A. Kachenoura, A. Karfoul, Chunfeng Yang, H. Shu

{"title":"Epileptic Seizure Prediction Using Deep Neural Networks Via Transfer Learning and Multi-Feature Fusion","authors":"Zuyi Yu, L. Albera, R. Bouquin-Jeannès, A. Kachenoura, A. Karfoul, Chunfeng Yang, H. Shu","doi":"10.1142/S0129065722500320","DOIUrl":"https://doi.org/10.1142/S0129065722500320","url":null,"abstract":"Epilepsy is one of the most common neurological diseases, which can seriously affect the patient's psychological well-being and quality of life. An accurate and reliable seizure prediction system can generate alarm before epileptic seizures to provide patients and their caregivers with sufficient time to take appropriate action. This study proposes an efficient seizure prediction system based on deep learning in order to anticipate the onset of the seizure as early as possible. Handcrafted features extracted based on the prior knowledge and hidden deep features are complementarily fused through the feature fusion module, and then the hybrid features are fed into the multiplicative long short-term memory (MLSTM) to explore the temporal dependency in EEG signals. A one-dimensional channel attention mechanism is implemented to emphasize the more representative information in the multi-channel output of the MLSTM. Finally, a transfer learning strategy is proposed to transfer the weights of the base model trained on the EEG data of all patients to the target patient model, and the latter is then continuously trained using the EEG data of the target patient. The proposed method achieves an average sensitivity of 95.56% and a false positive rate (FPR) of 0.27/h on the SWEC-ETHZ intracranial EEG data. For the more challenging CHB-MIT scalp EEG database, an average sensitivity of 89.47% and a FPR of 0.34/h are obtained. Experimental results demonstrate that the proposed method has good robustness and generalization ability in both intracranial and scalp EEG signals.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"1 1","pages":"2250032"},"PeriodicalIF":8.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49100745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Patient-Independent Seizure Detection Based on Channel-Perturbation Convolutional Neural Network and Bidirectional Long Short-Term Memory. 基于通道摄动卷积神经网络和双向长短期记忆的患者独立癫痫检测。

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-06-01 Epub Date: 2021-11-15 DOI: 10.1142/S0129065721500519

Guoyang Liu, Lan Tian, Weidong Zhou

{"title":"Patient-Independent Seizure Detection Based on Channel-Perturbation Convolutional Neural Network and Bidirectional Long Short-Term Memory.","authors":"Guoyang Liu, Lan Tian, Weidong Zhou","doi":"10.1142/S0129065721500519","DOIUrl":"https://doi.org/10.1142/S0129065721500519","url":null,"abstract":"Automatic seizure detection is of great significance for epilepsy diagnosis and alleviating the massive burden caused by manual inspection of long-term EEG. At present, most seizure detection methods are highly patient-dependent and have poor generalization performance. In this study, a novel patient-independent approach is proposed to effectively detect seizure onsets. First, the multi-channel EEG recordings are preprocessed by wavelet decomposition. Then, the Convolutional Neural Network (CNN) with proper depth works as an EEG feature extractor. Next, the obtained features are fed into a Bidirectional Long Short-Term Memory (BiLSTM) network to further capture the temporal variation characteristics. Finally, aiming to reduce the false detection rate (FDR) and improve the sensitivity, the postprocessing, including smoothing and collar, is performed on the outputs of the model. During the training stage, a novel channel perturbation technique is introduced to enhance the model generalization ability. The proposed approach is comprehensively evaluated on the CHB-MIT public scalp EEG database as well as a more challenging SH-SDU scalp EEG database we collected. Segment-based average accuracies of 97.51% and 93.70%, event-based average sensitivities of 86.51% and 89.89%, and average AUC-ROC of 90.82% and 90.75% are yielded on the CHB-MIT database and SH-SDU database, respectively.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"32 6","pages":"2150051"},"PeriodicalIF":8.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39880280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Characterizing Fractal Genetic Variation in the Human Genome from the Hapmap Project 人类基因组分形遗传变异的特征

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-05-12 DOI: 10.1142/S0129065722500289

A. Borri, A. Cerasa, P. Tonin, L. Citrigno, C. Porcaro

{"title":"Characterizing Fractal Genetic Variation in the Human Genome from the Hapmap Project","authors":"A. Borri, A. Cerasa, P. Tonin, L. Citrigno, C. Porcaro","doi":"10.1142/S0129065722500289","DOIUrl":"https://doi.org/10.1142/S0129065722500289","url":null,"abstract":"Over the last decades, the exuberant development of next-generation sequencing has revolutionized gene discovery. These technologies have boosted the mapping of single nucleotide polymorphisms (SNPs) across the human genome, providing a complex universe of heterogeneity characterizing individuals worldwide. Fractal dimension (FD) measures the degree of geometric irregularity, quantifying how \"complex\" a self-similar natural phenomenon is. We compared two FD algorithms, box-counting dimension (BCD) and Higuchi's fractal dimension (HFD), to characterize genome-wide patterns of SNPs extracted from the HapMap data set, which includes data from 1184 healthy subjects of eleven populations. In addition, we have used cluster and classification analysis to relate the genetic distances within chromosomes based on FD similarities to the geographical distances among the 11 global populations. We found that HFD outperformed BCD at both grand average clusterization analysis by the cophenetic correlation coefficient, in which the closest value to 1 represents the most accurate clustering solution (0.981 for the HFD and 0.956 for the BCD) and classification (79.0% accuracy, 61.7% sensitivity, and 96.4% specificity for the HFD with respect to 69.1% accuracy, 43.2% sensitivity, and 94.9% specificity for the BCD) of the 11 populations present in the HapMap data set. These results support the evidence that HFD is a reliable measure helpful in representing individual variations within all chromosomes and categorizing individuals and global populations.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"1 1","pages":"2250028"},"PeriodicalIF":8.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42332014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Hybrid Time-Distributed Deep Neural Architecture for Speech Emotion Recognition 一种用于语音情感识别的混合时间分布深度神经结构

IF 8 2区计算机科学

International Journal of Neural Systems Pub Date : 2022-05-12 DOI: 10.1142/S0129065722500241

J. Lope, M. Graña

{"title":"A Hybrid Time-Distributed Deep Neural Architecture for Speech Emotion Recognition","authors":"J. Lope, M. Graña","doi":"10.1142/S0129065722500241","DOIUrl":"https://doi.org/10.1142/S0129065722500241","url":null,"abstract":"In recent years, speech emotion recognition (SER) has emerged as one of the most active human-machine interaction research areas. Innovative electronic devices, services and applications are increasingly aiming to check the user emotional state either to issue alerts under some predefined conditions or to adapt the system responses to the user emotions. Voice expression is a very rich and noninvasive source of information for emotion assessment. This paper presents a novel SER approach based on that is a hybrid of a time-distributed convolutional neural network (TD-CNN) and a long short-term memory (LSTM) network. Mel-frequency log-power spectrograms (MFLPSs) extracted from audio recordings are parsed by a sliding window that selects the input for the TD-CNN. The TD-CNN transforms the input image data into a sequence of high-level features that are feed to the LSTM, which carries out the overall signal interpretation. In order to reduce overfitting, the MFLPS representation allows innovative image data augmentation techniques that have no immediate equivalent on the original audio signal. Validation of the proposed hybrid architecture achieves an average recognition accuracy of 73.98% on the most widely and hardest publicly distributed database for SER benchmarking. A permutation test confirms that this result is significantly different from random classification ([Formula: see text]). The proposed architecture outperforms state-of-the-art deep learning models as well as conventional machine learning techniques evaluated on the same database trying to identify the same number of emotions.","PeriodicalId":50305,"journal":{"name":"International Journal of Neural Systems","volume":"1 1","pages":"2250024"},"PeriodicalIF":8.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41965395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2