Comparison of Attention-based Deep Learning Models for EEG Classification
University of Padova（帕多瓦大学）；National, Inter-University Consortium for Telecommunications (CNIT)（全国大学校际电信联盟（CNIT））；National Center of Neurology and Psychiatry（国立精神神经医疗研究中心）；University of Milano-Bicocca（米兰比可卡大学）；Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）
Objective: To evaluate the impact on Electroencephalography (EEG) classification of different kinds of attention mechanisms in Deep Learning (DL) models. Methods: We compared three attention-enhanced DL models, the brand-new InstaGATs, an LSTM with attention and a CNN with attention. We used these models to classify normal and abnormal (i.e., artifactual or pathological) EEG patterns. Results: We achieved the state of the art in all classification problems, regardless the large variability of the datasets and the simple architecture of the attention-enhanced models. We could also prove that, depending on how the attention mechanism is applied and where the attention layer is located in the model, we can alternatively leverage the information contained in the time, frequency or space domain of the dataset. Conclusions: with this work, we shed light over the role of different attention mechanisms in the classification of normal and abnormal EEG patterns. Moreover, we discussed how they can exploit the intrinsic relationships in the temporal, frequency and spatial domains of our brain activity. Significance: Attention represents a promising strategy to evaluate the quality of the EEG information, and its relevance, in different real-world scenarios. Moreover, it can make it easier to parallelize the computation and, thus, to speed up the analysis of big electrophysiological (e.g., EEG) datasets.
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…
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces
The School of Biomedical Engineering（生物医学工程学院）；The Guangdong Provincial Key Laboratory of Biomedical Measurements（生物医学测量与超声成像广东省重点实验室）；Shien-Ming Wu School of Intelligent Engineering（沈铭武智能工程学院）；Institute for Super Robotics（超机器人研究所）
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…
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…
Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the information contained in different domains of EEG signals. In this paper, we present a novel method, called four-dimensional attention-based neural network (4D-aNN) for EEG emotion recognition. First, raw EEG signals are transformed into 4D spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts spectral and spatial attention mechanisms to adaptively assign the …
University of Shanghai for Science and Technology（上海理工大学）
Technique of emotion recognition enables computers to classify human affective states into discrete categories. However, the emotion may fluctuate instead of maintaining a stable state even within a short time interval. There is also a difficulty to take the full use of the EEG spatial distribution due to its 3-D topology structure. To tackle the above issues, we proposed a locally temporal-spatial pattern learning graph attention network (LTS-GAT) in the present study. In the LTS-GAT, a divide-and-conquer scheme was used to examine local information on temporal and spatial dimensions of EEG p…
Classifying limb movements using brain activity is an important task in Brain-computer Interfaces (BCI) that has been successfully used in multiple application domains, ranging from human-computer interaction to medical and biomedical applications. This paper proposes a novel solution for classification of left/right hand movement by exploiting a Long Short-Term Memory (LSTM) network with attention mechanism to learn the electroencephalogram (EEG) time-series information. To this end, a wide range of time and frequency domain features are extracted from the EEG signals and used to train an LST…
Accurate detection of a drivers attention state can help develop assistive technologies that respond to unexpected hazards in real time and therefore improve road safety. This study compares the performance of several attention classifiers trained on participants brain activity. Participants performed a driving task in an immersive simulator where the car randomly deviated from the cruising lane. They had to correct the deviation and their response time was considered as an indicator of attention level. Participants repeated the task in two sessions; in one session they received kinesthetic fe…
Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms
Institute of Biomedical Engineering, Ulm University（生物医学工程研究所，乌尔姆大学）；Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zurich（移动健康系统实验室，健康科学与技术系，苏黎世联邦理工学院）
INESC-ID（里斯本系统与计算机工程研究所）；Instituto Superior Técnico（高等技术学院）
Topographical structures represent connections between entities and provide a comprehensive design of complex systems. Currently these structures are used to discover correlates of neuronal and haemodynamical activity. In this work, we incorporate them with neural processing techniques to perform regression, using electrophysiological activity to retrieve haemodynamics. To this end, we use Fourier features, attention mechanisms, shared space between modalities and incorporation of style in the latent representation. By combining these techniques, we propose several models that significantly ou…
ADHDeepNet From Raw EEG to Diagnosis: Improving ADHD Diagnosis through Temporal-Spatial Processing, Adaptive Attention Mechanisms, and Explainability in Raw EEG Signals
Centre of Real Time Computer Systems, Faculty of Informatics（信息学院实时计算机系统中心）；Kaunas University of Technology（考纳斯技术大学）；University of Central Florida（中央佛罗里达大学）；Amirkabir University of Technology（阿米尔卡比尔技术大学）；Shahed University（沙赫德大学）
Daegu Gyeongbuk Institute of Science and Technology (DGIST)（大邱庆北科学技术院（DGIST））
Brain-Computer Interfaces (BCI) based on Electroencephalography (EEG) signals, in particular motor imagery (MI) data have received a lot of attention and show the potential towards the design of key technologies both in healthcare and other industries. MI data is generated when a subject imagines movement of limbs and can be used to aid rehabilitation as well as in autonomous driving scenarios. Thus, classification of MI signals is vital for EEG-based BCI systems. Recently, MI EEG classification techniques using deep learning have shown improved performance over conventional techniques. Howeve…
Hunan University（湖南大学）；University of Warwick（华威大学）；University at Buffalo（纽约州立大学布法罗分校）；IBM Research AI（IBM研究院人工智能实验室）
The "mind-controlling" capability has always been in mankind's fantasy. With the recent advancements of electroencephalograph (EEG) techniques, brain-computer interface (BCI) researchers have explored various solutions to allow individuals to perform various tasks using their minds. However, the commercial off-the-shelf devices to run accurate EGG signal collection are usually expensive and the comparably cheaper devices can only present coarse results, which prevents the practical application of these devices in domestic services. To tackle this challenge, we propose and develop an end-to-end…
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…
Deep comparisons of Neural Networks from the EEGNet family
Roska Tamás Doctoral School of Sciences and Technology（罗斯卡·陶马什科学技术博士学院）；Faculty of Information Technology and Bionics, Pázmány Péter Catholic University（帕兹马尼·彼得天主教大学信息技术与仿生学院）；Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences（自然科学研究中心认知神经科学与心理学研究所）
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms
Institute of Signal Processing and System Theory, University of Stuttgart（斯图加特大学信号处理与系统理论研究所）；Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所IPA）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）
Northeast Electric Power University（东北电力大学）；Changchun University of Science and Technology（长春理工大学）；University of Sydney（悉尼大学）
Recognition accuracy and response time are both critically essential ahead of building practical electroencephalography (EEG) based brain-computer interface (BCI). Recent approaches, however, have either compromised in the classification accuracy or responding time. This paper presents a novel deep learning approach designed towards remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG. Bidirectional Long Short-term Memory (BiLSTM) with the Attention mechanism manages to derive relevant features from raw EEG signals. The connected graph convolutional neural netwo…
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
École de technologie supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Dibrugarh University（Dibrugarh大学）；Vytautas Magnus University（维陶塔斯马格努斯大学）；University of Southern Queensland（南昆士兰大学）
DAH-Net: A Dual-Attention Hybrid Network for Interpretable and Robust EEG-Based Emotion Recognition
Department of Electrical \& Computer Engineering University of Missouri Columbia, Missouri, USA；Department of Computer Science；Engineering City University Savar, Dhaka-1340, Bangladesh；Department of Computer Science University of Missouri Columbia, Missouri, USA
GCMCG: A Clustering-Aware Graph Attention and Expert Fusion Network for Multi-Paradigm, Multi-task, and Cross-Subject EEG Decoding
Faculty of frontier sciences, Harbin Institute of Technology, Shenzhen（前沿科学学院，哈尔滨工业大学深圳校区）；Doumaomao NetTech Co., Ltd.（杜马莫莫网络技术有限公司）；University of Melbourne（墨尔本大学）；The University of New South Wales（新南威尔士大学）
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possible to identify the target speaker from single-trial EEG recordings using auditory attention decoding (AAD) methods. AAD methods reconstruct the attended speech envelope from EEG recordings, based on a linear least-squares cost function or non-linear neural networks, and then directly compare the reconstructed envelope with the speech envelopes of speakers to identify the attended speaker using Pearson correlation coef…
EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. Hence, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attentio…
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes. However, the latest neuroscience has suggested brain network connectivity. Thus, the exhibited interaction between EEG channels might not be appropriately measured via Euclidean distance. To fill the gap, an attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents from raw EEG signals, where the topological structure of EEG electrodes was built as a graph. Meanwhile, deep r…
University of Mons（蒙斯大学）；IMT Nord Europe（北欧国立高等矿业电信学院）；CNRS（法国国家科学研究中心）
For many years now, understanding the brain mechanism has been a great research subject in many different fields. Brain signal processing and especially electroencephalogram (EEG) has recently known a growing interest both in academia and industry. One of the main examples is the increasing number of Brain-Computer Interfaces (BCI) aiming to link brains and computers. In this paper, we present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals. While previous methods often consider the spatial relationship in EEG th…
Northwestern Polytechnic University（西北工业大学）；Shaanxi Normal University（陕西师范大学）
One of the most significant challenges of EEG-based emotion recognition is the cross-subject EEG variations, leading to poor performance and generalizability. This paper proposes a novel EEG-based emotion recognition model called the domain adversarial graph attention model (DAGAM). The basic idea is to generate a graph to model multichannel EEG signals using biological topology. Graph theory can topologically describe and analyze relationships and mutual dependency between channels of EEG. Then, unlike other graph convolutional networks, self-attention pooling is applied to benefit salient EE…
Tianjin University（天津大学）；University of Electronic Science and Technology of China（电子科技大学）；Tianjin University of Traditional Chinese Medicine（天津中医药大学）
Road rage is a social problem that deserves attention, but little research has been done so far. In this paper, based on the biological topology of multi-channel EEG signals,we propose a model which combines transferable attention (TA) and regularized graph neural network (RGNN). First, topology-aware information aggregation is performed on EEG signals, and complex relationships between channels are dynamically learned. Then, the transferability of each channel is quantified based on the results of the node-wise domain classifier, which is used as attention score. We recruited 10 subjects and …
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…
Recognizing the feelings of human beings plays a critical role in our daily communication. Neuroscience has demonstrated that different emotion states present different degrees of activation in different brain regions, EEG frequency bands and temporal stamps. In this paper, we propose a novel structure to explore the informative EEG features for emotion recognition. The proposed module, denoted by PST-Attention, consists of Positional, Spectral and Temporal Attention modules to explore more discriminative EEG features. Specifically, the Positional Attention module is to capture the activate re…
Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions
Advanced Institute of Convergence Knowledge Informatics, Tohoku University, Japan（东京东京大学融合知识信息研究院）；Graduate School of Information Sciences, Tohoku University, Japan（东京东京大学信息科学研究生院）；Center for Data-Driven Science and Artificial Intelligence, Tohoku University, Japan（东京东京大学数据驱动科学与人工智能研究中心）；Research Institute of Electrical Communication, Tohoku University, Japan（东京东京大学电气通信研究所）
Tsinghua University（清华大学）；Institute of Process Engineering, Chinese Academy of Sciences（中国科学院过程工程研究所）
This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG). A bias-driven pruning method is proposed together with group convolution, global average pooling (GAP), near-zero pruning, weight clustering and quantization for the model compression, achieving a total compression ratio of 183.11x. The compressed SaleNet obtains a state-of-the-art subject-independent sustained attention level classification accuracy of 84.2% on the recorded 6-subject EEG database in this work. The SaleNet is imple…
BITS Pilani K K Birla Goa Campus（BITS Pilani K K Birla 果阿校区）
Auditory attention is a selective type of hearing in which people focus their attention intentionally on a specific source of a sound or spoken words whilst ignoring or inhibiting other auditory stimuli. In some sense, the auditory attention score of an individual shows the focus the person can have in auditory tasks. The recent advancements in deep learning and in the non-invasive technologies recording neural activity beg the question, can deep learning along with technologies such as electroencephalography (EEG) be used to predict the auditory attention score of an individual? In this paper…
Affective computing is an important branch of artificial intelligence, and with the rapid development of brain computer interface technology, emotion recognition based on EEG signals has received broad attention. It is still a great challenge to effectively explore the multi-dimensional information in the EEG data in spite of a large number of deep learning methods. In this paper, we propose a deep model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can exploit the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimens…
