EEG decoding systems based on deep neural networks have been widely used in decision making of brain computer interfaces (BCI). Their predictions, however, can be unreliable given the significant variance and noise in EEG signals. Previous works on EEG analysis mainly focus on the exploration of noise pattern in the source signal, while the uncertainty during the decoding process is largely unexplored. Automatically detecting and reducing such decoding uncertainty is important for BCI motor imagery applications such as robotic arm control etc. In this work, we proposed an uncertainty estimation and reduction model (UNCER) to quantify and mitigate the uncertainty during the EEG decoding process. It utilized a combination of dropout oriented method and Bayesian neural network for uncertainty estimation to incorporate both the uncertainty in the input signal and the uncertainty in the model parameters. We further proposed a data augmentation based approach for uncertainty reduction. The model can be integrated into current widely used EEG neural decoders without change of architecture. We performed extensive experiments for uncertainty estimation and its reduction in both intra-subject EEG decoding and cross-subject EEG decoding on two public motor imagery datasets, where the proposed model achieves significant improvement both on the quality of estimated uncertainty and the effectiveness of uncertainty reduction.
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）
Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task. However, these CNN-based algorithms generally have limitations in perceiving global temporal dependencies of EEG signals. Besides, they also ignore the diverse contributions of different EEG channels to the classification task. To address such issues, a novel channel attention based MLP-Mixer network (CAMLP-Net) is proposed for EEG-based MI decoding. Specifically, the MLP-based architecture is applied in this network to capture the tem…
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Institute of Neural Engineering, Graz University of Technology（技术大学神经工程研究所）；Center for Al in Medicine (C-AlM)（医学人工智能中心）
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（教育部图像处理与智能控制重点实验室，人工智能与自动化学院，华中科技大学）；Zhongguancun Academy（中关村学院）
Do EEG Foundation Models Transfer to Speech? A Benchmark on Overt and Imagined Speech Decoding
University of Granada, Spain（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR), Spain（信息与通信技术研究中心）；University of Chouaib Doukkali, Morocco（舒艾卜·杜卡利大学）；Brain, Mind, and Behavior Research Center (CIMCYC), University of Granada, Spain（大脑、心智与行为研究中心）
At present, people usually use some methods based on convolutional neural networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in perceiving global dependencies, which is not adequate for common EEG paradigms with a strong overall relationship. Regarding this issue, we propose a novel EEG decoding method that mainly relies on the attention mechanism. The EEG data is firstly preprocessed and spatially filtered. And then, we apply attention transforming on the feature-channel dimension so that the model can enhance more relevant spatial features. The most cruc…
Lehigh University（理海大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；South China University of Technology（华南理工大学）；Stanford University（斯坦福大学）；West China Hospital of Sichuan University（四川大学华西医院）；Guangdong University of Technology（广东工业大学）；Tokyo University of Agriculture and Technology（东京农工大学）；Nicolaus Copernicus University（尼古拉·哥白尼大学）
Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. However, existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution, and hence can only yield a suboptimal decoding accuracy. To uncover the intrinsic distributi…
Medical Center - University of Freiburg（弗莱堡大学医学中心）；University of Freiburg（弗莱堡大学）
In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals above chance. Further ideas for their regularization towards better decoding accuracies are discussed.
Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL)-based EEG decoders offer improved performances, the development of geometric learning (GL) has attracted much attention for offering exceptional robustness in decoding noisy EEG data. However, there is a lack of studies on the merged use of deep neural networks (DNNs) and geometric learning for EEG decoding. We herein propose a manifold attention network (mAtt), a novel geometric deep learning (GDL)-based model, featurin…
Meta AI；West Virginia University（西弗吉尼亚大学）；University of Maryland, College Park（马里兰大学帕克分校）；University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-imagery EEG and compare the results with existing knowledge of neuroscience. The primary objective of ExBrainable is to provide a fast, simplified, and user-friendly solution of EEG decoding for investig…
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；College of Artifical Intelligence and Automation, Hohai University（河海大学人工智能与自动化学院）；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；Brainnetcome Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所脑网络中心）；Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计）
Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet) in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples.…
EEG-to-Gait Decoding via Phase-Aware Representation Learning
College of Computing and Data Science, Nanyang Technological University, Singapore 639798（计算与数据科学学院，南洋理工大学，新加坡）；Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China（计算机科学与工程系，上海交通大学，上海）；Institute of Neural Engineering, Graz University of Technology, Graz, Austria（神经工程研究所，格拉茨技术大学，奥地利）；Centre of AI in Medicine (C-AIM), Nanyang Technological University, Singapore（医学人工智能中心（C-AIM），南洋理工大学，新加坡）
EEG-CLIP : Learning EEG representations from natural language descriptions
Medical Physics, Department of Diagnostic and Interventional Radiology（医学物理，诊断与介入放射科）；Medical Center—University of Freiburg, Faculty of Medicine University of Freiburg, Germany（弗赖堡大学医学中心—弗赖堡大学医学院，弗赖堡，德国）；Neuromedical A.I. Lab, Department of Neurosurgery（神经医学人工智能实验室，神经外科部门）；BrainLinks-BrainTools, IMBIT University of Freiburg, Germany（BrainLinks-BrainTools，IMBIT弗赖堡大学，德国）
Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. Recently, light-weight and portable EEG wearable devices based on low-density montages have increased the convenience and usability of BCI applications. However, loss of EEG decoding performance is often inevitable due to reduced number of electrodes and coverage of scalp regions of a low-density EEG montage. To address this issue, we introduce knowledge distillation (KD), a learning mechanism d…
A brain-computer interface (BCI) is used not only to control external devices for healthy people but also to rehabilitate motor functions for motor-disabled patients. Decoding movement intention is one of the most significant aspects for performing arm movement tasks using brain signals. Decoding movement execution (ME) from electroencephalogram (EEG) signals have shown high performance in previous works, however movement imagination (MI) paradigm-based intention decoding has so far failed to achieve sufficient accuracy. In this study, we focused on a robust MI decoding method with transfer le…
Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
Department of Computer Science, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校计算机科学系）；Department of Computer Science, Dartmouth College（达特茅斯学院计算机科学系）；School of Computing, University of Georgia（佐治亚大学计算科学学院）；School of Electrical and Computer Engineering, University of Georgia（佐治亚大学电气与计算机工程学院）
CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
State Key Laboratory of Brain-machine Intelligence（脑机智能国家重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；Alibaba Group（阿里巴巴集团）；Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine（神经生物学系、附属心理健康中心及杭州第七人民医院，浙江大学医学院）；MOE Frontier Science Center for Brain Science and Brain-machine Integration（教育部脑科学与脑机集成前沿科学中心）
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity. Traditional EEG decoding methods have achieved moderate success when applied to data acquired in static, well-controlled lab environments. However, an open-world environment is a more realistic setting, where situations affecting EEG recordings can emerge unexpectedly, significantly weakening the robustness of existing methods. In recent years, deep learning (DL) has emerged as a potential solution for such problems due to it…
We introduce here the idea of Meta-Learning for training EEG BCI decoders. Meta-Learning is a way of training machine learning systems so they learn to learn. We apply here meta-learning to a simple Deep Learning BCI architecture and compare it to transfer learning on the same architecture. Our Meta-learning strategy operates by finding optimal parameters for the BCI decoder so that it can quickly generalise between different users and recording sessions -- thereby also generalising to new users or new sessions quickly. We tested our algorithm on the Physionet EEG motor imagery dataset. Our ap…
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California（明希斯电气与计算机工程系，南加州大学）；Alfred E. Mann Department of Biomedical Engineering, University of Southern California（阿尔弗雷德·E·曼生物医学工程系，南加州大学）；Thomas Lord Department of Computer Science, University of Southern California（托马斯·劳德计算机科学系，南加州大学）
The auditory attention decoding (AAD) approach was proposed to determine the identity of the attended talker in a multi-talker scenario by analyzing electroencephalography (EEG) data. Although the linear model-based method has been widely used in AAD, the linear assumption was considered oversimplified and the decoding accuracy remained lower for shorter decoding windows. Recently, nonlinear models based on deep neural networks (DNN) have been proposed to solve this problem. However, these models did not fully utilize both the spatial and temporal features of EEG, and the interpretability of D…
Indian Institute of Science（印度科学学院）；Voice and Speech Systems（语音与言语系统公司）
The recent advances in the field of deep learning have not been fully utilised for decoding imagined speech primarily because of the unavailability of sufficient training samples to train a deep network. In this paper, we present a novel architecture that employs deep neural network (DNN) for classifying the words "in" and "cooperate" from the corresponding EEG signals in the ASU imagined speech dataset. Nine EEG channels, which best capture the underlying cortical activity, are chosen using common spatial pattern (CSP) and are treated as independent data vectors. Discrete wavelet transform (D…
Auditory attention decoding (AAD) algorithms decode the auditory attention from electroencephalography (EEG) signals that capture the listener's neural activity. Such AAD methods are believed to be an important ingredient towards so-called neuro-steered assistive hearing devices. For example, traditional AAD decoders allow detecting to which of multiple speakers a listener is attending to by reconstructing the amplitude envelope of the attended speech signal from the EEG signals. Recently, an alternative paradigm to this stimulus reconstruction approach was proposed, in which the directional f…
