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Subtype classification of attention deficit hyperactivity disorder with hierarchical binary hypothesis testing framework. 用层次二元假设检验框架对注意缺陷多动障碍进行亚型分类。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-22 DOI: 10.1088/1741-2552/acf523
Yuan Gao, Huaqing Ni, Ying Chen, Yibin Tang, Xiaofeng Liu

Objective. The diagnosis of attention deficit hyperactivity disorder (ADHD) subtypes is important for the refined treatment of ADHD children. Although automated diagnosis methods based on machine learning are performed with structural and functional magnetic resonance imaging (sMRI and fMRI) data which have full observation of brains, they are not satisfactory with the accuracy of less than80%for the ADHD subtype diagnosis.Approach. To improve the accuracy and obtain the biomarker of ADHD subtypes, we proposed a hierarchical binary hypothesis testing (H-BHT) framework by using brain functional connectivity (FC) as input bio-signals. The framework includes a two-stage procedure with a decision tree strategy and thus becomes suitable for the subtype classification. Also, typical FC is extracted in both two stages of identifying ADHD subtypes. That means the important FC is found out for the subtype recognition.Main results. We apply the proposed H-BHT framework to resting state fMRI datasets from ADHD-200 consortium. The results are achieved with the average accuracy97.1%and an average kappa score 0.947. Discriminative FC between ADHD subtypes is found by comparing the P-values of typical FC.Significance. The proposed framework not only is an effective structure for ADHD subtype classification, but also provides useful reference for multiclass classification of mental disease subtypes.

客观的注意力缺陷多动障碍(ADHD)亚型的诊断对ADHD儿童的精细治疗很重要。尽管基于机器学习的自动诊断方法是用对大脑进行全面观察的结构和功能磁共振成像(sMRI和fMRI)数据进行的,但它们对ADHD亚型诊断的准确率低于80%并不令人满意。方法为了提高准确性并获得ADHD亚型的生物标志物,我们提出了一种利用大脑功能连接(FC)作为输入生物信号的分层二元假设检验(H-BHT)框架。该框架包括一个具有决策树策略的两阶段过程,因此适用于子类型分类。此外,在识别多动症亚型的两个阶段都提取了典型的FC。这意味着找到了用于子类型识别的重要FC。主要结果。我们将所提出的H-BHT框架应用于ADHD-200联盟的静息态fMRI数据集。结果的平均准确率为97.1%,平均kappa评分为0.947。通过比较典型FC的P值,发现了ADHD亚型之间的判别性FC。该框架不仅是ADHD亚类型分类的有效结构,而且为精神疾病亚型的多类别分类提供了有用的参考。
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引用次数: 0
Inferring cognitive state underlying conflict choices in verbal Stroop task using heterogeneous input discriminative-generative decoder model. 使用异质输入判别生成解码器模型推断言语Stroop任务中冲突选择的认知状态。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-22 DOI: 10.1088/1741-2552/ace932
Mohammad Reza Rezaei, Haseul Jeoung, Ayda Ghahramani, Uptal Saha, Venkat Bhat, Milos R Popovic, Ali Yousefi, Robert E W Chen, Milad Lankarany

Objective. The subthalamic nucleus (STN) of the basal ganglia interacts with the medial prefrontal cortex (mPFC) and shapes a control loop, specifically when the brain receives contradictory information from either different sensory systems or conflicting information from sensory inputs and prior knowledge that developed in the brain. Experimental studies demonstrated that significant increases in theta activities (2-8 Hz) in both the STN and mPFC as well as increased phase synchronization between mPFC and STN are prominent features of conflict processing. While these neural features reflect the importance of STN-mPFC circuitry in conflict processing, a low-dimensional representation of the mPFC-STN interaction referred to as a cognitive state, that links neural activities generated by these sub-regions to behavioral signals (e.g. the response time), remains to be identified.Approach. Here, we propose a new model, namely, the heterogeneous input discriminative-generative decoder (HI-DGD) model, to infer a cognitive state underlying decision-making based on neural activities (STN and mPFC) and behavioral signals (individuals' response time) recorded in ten Parkinson's disease (PD) patients while they performed a Stroop task. PD patients may have conflict processing which is quantitatively (may be qualitative in some) different from healthy populations.Main results. Using extensive synthetic and experimental data, we showed that the HI-DGD model can diffuse information from neural and behavioral data simultaneously and estimate cognitive states underlying conflict and non-conflict trials significantly better than traditional methods. Additionally, the HI-DGD model identified which neural features made significant contributions to conflict and non-conflict choices. Interestingly, the estimated features match well with those reported in experimental studies.Significance. Finally, we highlight the capability of the HI-DGD model in estimating a cognitive state from a single trial of observation, which makes it appropriate to be utilized in closed-loop neuromodulation systems.

客观的基底神经节的丘脑底核(STN)与内侧前额叶皮层(mPFC)相互作用,形成控制回路,特别是当大脑从不同的感觉系统接收到矛盾的信息,或从大脑中形成的感觉输入和先验知识接收到矛盾信息时。实验研究表明θ活性显著增加(2-8 Hz)以及mPFC和STN之间增加的相位同步是冲突处理的显著特征。虽然这些神经特征反映了STN-mPFC电路在冲突处理中的重要性,但被称为认知状态的mPFC-STN相互作用的低维表示仍有待确定,该低维表示将这些子区域产生的神经活动与行为信号(例如响应时间)联系起来。方法在这里,我们提出了一个新的模型,即异质输入判别生成解码器(HI-DGD)模型,以基于10名帕金森病(PD)患者在执行Stroop任务时记录的神经活动(STN和mPFC)和行为信号(个体的反应时间)来推断决策的认知状态。帕金森病患者可能具有与健康人群在数量上(在某些情况下可能是定性的)不同的冲突处理。主要结果。使用大量的合成和实验数据,我们表明HI-DGD模型可以同时扩散来自神经和行为数据的信息,并比传统方法更好地估计冲突和非冲突试验的认知状态。此外,HI-DGD模型确定了哪些神经特征对冲突和非冲突选择做出了重大贡献。有趣的是,估计的特征与实验研究中报道的特征非常吻合。意义最后,我们强调了HI-DGD-模型从一次观察试验中估计认知状态的能力,这使其适合用于闭环神经调控系统。
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引用次数: 0
Suppression of cortical electrostimulation artifacts using pre-whitening and null projection. 使用预白化和零投影抑制皮层电刺激伪影。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-22 DOI: 10.1088/1741-2552/acf68b
Jeffrey Lim, Po T Wang, Luke Bashford, Spencer Kellis, Susan J Shaw, Hui Gong, Michelle Armacost, Payam Heydari, An H Do, Richard A Andersen, Charles Y Liu, Zoran Nenadic

Objective.Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods.Approach.We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects.Main results.In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%-80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement.Significance.PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.

目的:有创脑机接口(BCI)在恢复因神经损伤而瘫痪的患者的运动功能方面显示出了前景。这些系统还具有通过皮层电刺激恢复感觉的能力。皮层刺激会产生强烈的伪影,这些伪影可能会模糊神经信号或使记录放大器饱和。虽然前端硬件技术可以缓解这个问题,但残留的伪影通常会持续存在,必须通过后端方法来抑制。方法:我们开发了一种基于预白化和零投影(PWNP)的技术,并测试了其抑制五名受试者脑电图(EEG)、皮层电图(ECoG)和微电极阵列(MEA)信号中刺激伪影的能力。主要结果。在被窄带刺激伪影污染的EEG信号中,PWNP方法实现了32到34之间的平均伪影抑制 dB,如通过信号干扰比的增加来测量的。在被宽带刺激伪影污染的ECoG和MEA信号中,我们的方法通过干扰指数的降低分别抑制了78%-80%和85%的伪影。与被认为是最先进的伪影抑制技术的独立分量分析相比,我们的方法取得了优异的结果,同时也更容易实现。值得注意的是,PWNP可能是一种有效的伪影抑制方法,可以在双向脑机接口中同时刺激和记录,以仿生恢复运动功能。
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引用次数: 0
BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition. 脑电波散射网:一种用于基于脑电图的运动图像识别的轻量级网络。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-22 DOI: 10.1088/1741-2552/acf78a
Konstantinos Barmpas, Yannis Panagakis, Dimitrios A Adamos, Nikolaos Laskaris, Stefanos Zafeiriou

Objective.Brain-computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one's neural activity, measured by electroencephalography (EEG) signals. In recent years, convolutional neural networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However, their undeniable benefits are counterbalanced by the lack of interpretability properties as well as the inability to perform sufficiently when only limited amount of training data is available.Approach.In this work, we introduce a novel, lightweight, fully-learnable neural network architecture that relies on Gabor filters to delocalize EEG signal information into scattering decomposition paths along frequency and slow-varying temporal modulations.Main results.We utilize our network in two distinct modeling settings, for building either a generic (training across subjects) or a personalized (training within a subject) classifier.Significance.In both cases, using two different publicly available datasets and one in-house collected dataset, we demonstrate high performance for our model with considerably less number of trainable parameters as well as shorter training time compared to other state-of-the-art deep architectures. Moreover, our network demonstrates enhanced interpretability properties emerging at the level of the temporal filtering operation and enables us to train efficient personalized BCI models with limited amount of training data.

脑机接口(BCI)通过脑电图(EEG)信号测量一个人的神经活动,实现大脑与外部世界的直接通信。近年来,卷积神经网络(CNNs)已被广泛用于在各种基于EEG的任务中进行自动特征提取和分类。然而,它们不可否认的好处被缺乏可解释性属性以及在只有有限数量的训练数据可用时无法充分执行所抵消。方法。在这项工作中,我们介绍了一种新的、轻量级的、完全可学习的神经网络架构,该架构依赖于Gabor滤波器将EEG信号信息沿频率和缓慢变化的时间调制离域到散射分解路径中。主要结果。我们在两种不同的建模设置中使用我们的网络,用于构建通用(跨主题训练)或个性化(在主题内训练)分类器。意义。在这两种情况下,使用两个不同的公开可用数据集和一个内部收集的数据集,与其他最先进的深度架构相比,我们展示了我们的模型的高性能,可训练参数的数量要少得多,训练时间也更短。此外,我们的网络展示了在时间滤波操作层面出现的增强的可解释性特性,并使我们能够用有限的训练数据训练高效的个性化脑机接口模型。
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引用次数: 0
Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model. 使用EEG Transformer模型基于EEG信号对注意力缺陷/多动障碍进行分类*。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-21 DOI: 10.1088/1741-2552/acf7f5
Yuchao He, Xin Wang, Zijian Yang, Lingbin Xue, Yuming Chen, Junyu Ji, Feng Wan, Subhas Chandra Mukhopadhyay, Lina Men, Chi Fai Michael Tong, Guanglin Li, Shixiong Chen

Objective. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in adolescents that can seriously impair a person's attention function, cognitive processes, and learning ability. Currently, clinicians primarily diagnose patients based on the subjective assessments of the Diagnostic and Statistical Manual of Mental Disorders-5, which can lead to delayed diagnosis of ADHD and even misdiagnosis due to low diagnostic efficiency and lack of well-trained diagnostic experts. Deep learning of electroencephalogram (EEG) signals recorded from ADHD patients could provide an objective and accurate method to assist physicians in clinical diagnosis.Approach. This paper proposes the EEG-Transformer deep learning model, which is based on the attention mechanism in the traditional Transformer model, and can perform feature extraction and signal classification processing for the characteristics of EEG signals. A comprehensive comparison was made between the proposed transformer model and three existing convolutional neural network models.Main results. The results showed that the proposed EEG-Transformer model achieved an average accuracy of 95.85% and an average AUC value of 0.9926 with the fastest convergence speed, outperforming the other three models. The function and relationship of each module of the model are studied by ablation experiments. The model with optimal performance was identified by the optimization experiment.Significance. The EEG-Transformer model proposed in this paper can be used as an auxiliary tool for clinical diagnosis of ADHD, and at the same time provides a basic model for transferable learning in the field of EEG signal classification.

客观的注意力缺陷/多动障碍(ADHD)是青少年最常见的神经发育障碍,会严重损害一个人的注意力功能、认知过程和学习能力。目前,临床医生主要根据《精神障碍诊断与统计手册》-5的主观评估来诊断患者,由于诊断效率低和缺乏训练有素的诊断专家,这可能导致多动症的诊断延迟,甚至误诊。对ADHD患者脑电图(EEG)信号的深度学习可以为医生的临床诊断提供一种客观准确的方法。方法本文提出了脑电变压器深度学习模型,该模型基于传统变压器模型中的注意力机制,可以对脑电信号的特征进行特征提取和信号分类处理。将所提出的变换器模型与现有的三个卷积神经网络模型进行了全面比较。主要结果。结果表明,所提出的EEG Transformer模型以最快的收敛速度实现了95.85%的平均准确率和0.9926的平均AUC值,优于其他三个模型。通过烧蚀实验研究了该模型各模块的作用及相互关系。通过优化实验确定了性能最优的模型。意义本文提出的脑电变换器模型可以作为ADHD临床诊断的辅助工具,同时为脑电信号分类领域的可转移学习提供了一个基本模型。
{"title":"Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model<sup>∗</sup>.","authors":"Yuchao He,&nbsp;Xin Wang,&nbsp;Zijian Yang,&nbsp;Lingbin Xue,&nbsp;Yuming Chen,&nbsp;Junyu Ji,&nbsp;Feng Wan,&nbsp;Subhas Chandra Mukhopadhyay,&nbsp;Lina Men,&nbsp;Chi Fai Michael Tong,&nbsp;Guanglin Li,&nbsp;Shixiong Chen","doi":"10.1088/1741-2552/acf7f5","DOIUrl":"10.1088/1741-2552/acf7f5","url":null,"abstract":"<p><p><i>Objective</i>. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in adolescents that can seriously impair a person's attention function, cognitive processes, and learning ability. Currently, clinicians primarily diagnose patients based on the subjective assessments of the Diagnostic and Statistical Manual of Mental Disorders-5, which can lead to delayed diagnosis of ADHD and even misdiagnosis due to low diagnostic efficiency and lack of well-trained diagnostic experts. Deep learning of electroencephalogram (EEG) signals recorded from ADHD patients could provide an objective and accurate method to assist physicians in clinical diagnosis.<i>Approach</i>. This paper proposes the EEG-Transformer deep learning model, which is based on the attention mechanism in the traditional Transformer model, and can perform feature extraction and signal classification processing for the characteristics of EEG signals. A comprehensive comparison was made between the proposed transformer model and three existing convolutional neural network models.<i>Main results</i>. The results showed that the proposed EEG-Transformer model achieved an average accuracy of 95.85% and an average AUC value of 0.9926 with the fastest convergence speed, outperforming the other three models. The function and relationship of each module of the model are studied by ablation experiments. The model with optimal performance was identified by the optimization experiment.<i>Significance</i>. The EEG-Transformer model proposed in this paper can be used as an auxiliary tool for clinical diagnosis of ADHD, and at the same time provides a basic model for transferable learning in the field of EEG signal classification.</p>","PeriodicalId":16753,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10178275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models. 通过优化的深度学习模型从感觉运动大脑活动直接重建语音。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-20 DOI: 10.1088/1741-2552/ace8be
Julia Berezutskaya, Zachary V Freudenburg, Mariska J Vansteensel, Erik J Aarnoutse, Nick F Ramsey, Marcel A J van Gerven

Objective.Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field.Approach.In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task.Main results.We show that (1) dedicated machine learning optimization of reconstruction models is key for achieving the best reconstruction performance; (2) individual word decoding in reconstructed speech achieves 92%-100% accuracy (chance level is 8%); (3) direct reconstruction from sensorimotor brain activity produces intelligible speech.Significance.These results underline the need for model optimization in achieving best speech decoding results and highlight the potential that reconstruction-based speech decoding from sensorimotor cortex can offer for development of next-generation BCI technology for communication.

目的:脑机接口(BCI)技术的发展是使因严重运动麻痹而丧失语言能力的人能够进行交流的关键。一种越来越受关注的脑机接口控制策略采用了来自神经数据的语音解码。最近的研究表明,直接神经记录和高级计算模型的结合可以提供有希望的结果。了解哪些解码策略可以提供最佳且直接适用的结果,对于推进该领域至关重要。方法。在本文中,我们优化并验证了一种基于语音重建的解码方法,该方法直接从语音产生任务中感觉运动皮层的高密度皮层电图记录中重建。主要结果。我们证明:(1)重建模型的专用机器学习优化是实现最佳重建性能的关键;(2) 重构语音中的单个单词解码准确率达到92%-100%(概率水平为8%);(3) 根据感觉运动大脑活动的直接重建产生可理解的语音。意义。这些结果强调了在实现最佳语音解码结果方面进行模型优化的必要性,并强调了基于重建的感觉运动皮层语音解码可以为下一代脑机接口通信技术的发展提供的潜力。
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引用次数: 0
Deepening the role of excitation/inhibition balance in human iPSCs-derived neuronal networks coupled to MEAs during long-term development. 在长期发展过程中,加深人类iPSC衍生的神经元网络与MEA耦合的兴奋/抑制平衡的作用。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-19 DOI: 10.1088/1741-2552/acf78b
Giulia Parodi, Martina Brofiga, Vito Paolo Pastore, Michela Chiappalone, Sergio Martinoia

Objective.The purpose of this study is to investigate whether and how the balance between excitation and inhibition ('E/I balance') influences the spontaneous development of human-derived neuronal networksin vitro. To achieve that goal, we performed a long-term (98 d) characterization of both homogeneous (only excitatory or inhibitory neurons) and heterogeneous (mixed neuronal types) cultures with controlled E/I ratios (i.e. E:I 0:100, 25:75, 50:50, 75:25, 100:0) by recording their electrophysiological activity using micro-electrode arrays.Approach.Excitatory and inhibitory neurons were derived from human induced pluripotent stem cells (hiPSCs). We realized five different configurations by systematically varying the glutamatergic and GABAergic percentages.Main results.We successfully built both homogeneous and heterogeneous neuronal cultures from hiPSCs finely controlling the E/I ratios; we were able to maintain them for up to 3 months. Homogeneity differentially impacted purely inhibitory (no bursts) and purely excitatory (few bursts) networks, deviating from the typical traits of heterogeneous cultures (burst dominated). Increased inhibition in heterogeneous cultures strongly affected the duration and organization of bursting and network bursting activity. Spike-based functional connectivity and image-based deep learning analysis further confirmed all the above.Significance.Healthy neuronal activity is controlled by a well-defined E/I balance whose alteration could lead to the onset of neurodevelopmental disorders like schizophrenia or epilepsy. Most of the commonly usedin vitromodels are animal-derived or too simplified and thus far from thein vivohuman condition. In this work, by performing a long-term study of hiPSCs-derived neuronal networks obtained from healthy human subjects, we demonstrated the feasibility of a robustin vitromodel which can be further exploited for investigating pathological conditions where the E/I balance is impaired.

客观的本研究的目的是研究兴奋和抑制之间的平衡(“E/I平衡”)是否以及如何影响体外人源性神经元网络的自发发展。为了实现这一目标,我们对具有受控E/I比(即E:I 0:100、25:75、50:50、75:25、100:0)的同质(仅兴奋性或抑制性神经元)和异质(混合神经元类型)培养物进行了长期(98天)表征,方法是使用微电极阵列记录其电生理活性。方法兴奋性和抑制性神经元来源于人类诱导多能干细胞。通过系统地改变谷氨酸能和GABA能的百分比,我们实现了五种不同的构型。主要结果。我们成功地从精细控制E/I比率的hiPSC构建了同质和异质神经元培养物;我们能够维护它们长达3个月。同质性对纯抑制性(无爆发)和纯兴奋性(很少爆发)网络产生了不同的影响,偏离了异质文化的典型特征(爆发主导)。异质培养中抑制作用的增加强烈影响爆发和网络爆发活动的持续时间和组织。基于Spike的功能连接和基于图像的深度学习分析进一步证实了上述一切。意义健康的神经元活动由明确的E/I平衡控制,其改变可能导致精神分裂症或癫痫等神经发育障碍的发作。大多数常用的玻璃体模型都是动物来源的,或者过于简化,因此与活体人类条件相去甚远。在这项工作中,通过对从健康人类受试者获得的hiPSCs衍生的神经元网络进行长期研究,我们证明了robustin玻璃体模型的可行性,该模型可进一步用于研究E/I平衡受损的病理条件。
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引用次数: 0
Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making. 脑电决策置信度的后刺激编码:面向决策的脑机接口。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-19 DOI: 10.1088/1741-2552/acec14
Nitin Sadras, Omid G Sani, Parima Ahmadipour, Maryam M Shanechi

Objective.When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.Approach.We investigate the neural correlates of confidence by collecting high-density electroencephalography (EEG) during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.Main results.We perform event-related potential and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-locked activity from confounding stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.Significance.Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.

客观。在做出决定时,人类可以评估他们正确的可能性。如果这种主观置信度能够从大脑活动中可靠地解码,那么就有可能建立一个脑机接口(BCI),通过根据用户的置信度在需要时自动向用户提供更多信息来提高决策性能。但这种可能性取决于是否可以在刺激出现后和反应前立即解码置信度,以便及时采取纠正措施。尽管先前的工作已经表明决策置信度在大脑信号中表示,但尚不清楚该表示是刺激锁定还是反应锁定,以及刺激锁定预反应解码是否足够准确,以实现这种脑机接口方法。我们通过在具有现实刺激的感知决策任务中收集高密度脑电图(EEG)来研究置信度的神经相关性。重要的是,我们将我们的任务设计为包括一个刺激后缺口,以防止刺激锁定活动与反应锁定活动混淆,反之亦然,然后与没有这个缺口的任务进行比较。主要结果。我们进行事件相关电位和源定位分析。我们的分析表明,信心的神经相关性是刺激锁定的,刺激后间隙的缺失可能会导致这些相关性错误地显示为反应锁定。通过防止反应锁定活动混淆刺激锁定活动,我们证明了可以从单独的试验刺激锁定反应前脑电图中可靠地解码置信度。我们还通过比较一组算法来确定一种高性能的分类算法。最后,我们设计了一个模拟的脑机接口框架,以表明EEG分类足够准确,可以建立脑机接口,并且解码的置信度可以用来提高决策性能,特别是在任务难度和错误成本较高的情况下。意义。我们的研究结果表明了基于脑电的无创脑机接口改善人类决策的可行性。
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引用次数: 1
Compression strategies for large-scale electrophysiology data. 大规模电生理数据的压缩策略。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-18 DOI: 10.1088/1741-2552/acf5a4
Alessio P Buccino, Olivier Winter, David Bryant, David Feng, Karel Svoboda, Joshua H Siegle

Objective.With the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across the field are growing at unprecedented rates. For example, a one-hour recording with a 384-channel Neuropixels probe generates over 80 GB of raw data. These large data volumes carry a high cost, especially if researchers plan to store and analyze their data in the cloud. Thus, there is a pressing need for strategies that can reduce the data footprint of each experiment.Approach.Here, we establish a set of benchmarks for comparing the performance of various compression algorithms on experimental and simulated recordings from Neuropixels 1.0 (NP1) and 2.0 (NP2) probes.Main results.For lossless compression, audio codecs (FLACandWavPack) achieve compression ratios (CRs) 6% higher for NP1 and 10% higher for NP2 than the best general-purpose codecs, at the expense of decompression speed. For lossy compression, theWavPackalgorithm in 'hybrid mode' increases the CR from 3.59 to 7.08 for NP1 and from 2.27 to 7.04 for NP2 (compressed file size of ∼14% for both types of probes), without adverse effects on spike sorting accuracy or spike waveforms.Significance.Along with the tools we have developed to make compression easier to deploy, these results should encourage all electrophysiologists to apply compression as part of their standard analysis workflows.

目的:随着高密度电极阵列用于记录神经活动的快速采用,实验室内和整个领域的电生理学数据量正以前所未有的速度增长。例如,使用384通道Neuropixels探针进行一小时的记录,可生成超过80GB的原始数据。这些大数据量的成本很高,尤其是如果研究人员计划在云中存储和分析他们的数据。因此,迫切需要能够减少每个实验的数据足迹的策略。方法。在这里,我们建立了一组基准,用于比较各种压缩算法在Neuropixels 1.0(NP1)和2.0(NP2)探针的实验和模拟记录上的性能。主要结果。对于无损压缩,音频编解码器(FLACandWavPack)以牺牲解压缩速度为代价,实现了NP1比最佳通用编解码器高6%和NP2高10%的压缩比(CR)。对于有损压缩,“混合模式”下的WavPack算法将NP1的CR从3.59增加到7.08,将NP2的CR从2.27增加到7.04(两种类型的探针的压缩文件大小都为~14%),不会对尖峰排序精度或尖峰波形产生不利影响。意义。除了我们开发的使压缩更容易部署的工具外,这些结果应该鼓励所有电生理学家将压缩作为其标准分析工作流程的一部分。
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引用次数: 0
Real-time low latency estimation of brain rhythms with deep neural networks. 利用深度神经网络实时低延迟估计大脑节律。
IF 4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2023-09-18 DOI: 10.1088/1741-2552/acf7f3
Ilia Semenkov, Nikita Fedosov, Ilya Makarov, Alexei Ossadtchi

Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits.Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was then trained to simultaneously filter and forecast EEG data. We compared it against the state-of-the-art techniques using synthetic and real data from 25 subjects.Main results.The temporal convolutional network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios>90% rhythm's envelope correlation with<10 ms effective delay and<20∘circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture.Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.

目的:神经反馈和脑机接口技术为建立与人脑的交互式闭环实时通信开辟了令人兴奋的机会。这需要解释大脑的节律性活动,并及时向大脑产生反馈。神经元事件和适当反馈之间的较低延迟增加了这种相互作用的功效。对于需要与大脑回路即时交互的场景,需要能够跟踪大脑节律的相位和包络的新的更有效的方法。方法。隔离窄带信号会导致基本延迟。在某种程度上,它们可以使用预测模型进行补偿。鉴于现代时间序列预测神经网络的高质量,我们探索了它们在低延迟提取大脑节律参数方面的实用性。我们测试了五种具有概念上不同架构的神经网络,用于预测合成脑电图节律。然后训练最强的架构来同时过滤和预测EEG数据。我们使用来自25名受试者的合成和真实数据将其与最先进的技术进行了比较。主要结果。时间卷积网络(TCN)仍然是在大多数测试场景中实现的最强预测模型,其节律包络相关性>90%,具有显著性。在这里,我们首次证明了神经网络方法在大脑活动信号的低延迟窄带滤波中的实用性。我们提出的方法与有效的实现相结合,增强了大脑状态依赖范式在各种应用中的有效性。此外,我们的脑电信号预测框架有望研究大脑活动的可预测性,为围绕大脑功能组织和层次信息处理特性的基本问题提供有价值的见解。
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Journal of neural engineering
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