Deep temporal networks for eeg based motor imagery recognition Recently, a lot of efforts have been made to improve MI signal classification using a combination of Jan 1, 2024 · Sun et al (Sun et al. Oct 8, 2020 · Optimal Deep Learning-Based Recognition for EEG Signal Motor Imagery (ODLR-EEGSM) is a novel approach presented in this article that aims to improve the recognition of motor imagery from EEG signals. The MSTM extracts deep temporal information of the EEG signal in parallel, thus obtaining dynamic temporal information of the EEG Dec 29, 2023 · A multi-branch 3D convolutional neural network for EEG-based motor imagery classification. Recently, a lot of efforts have been made to improve MI signal classification using a A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification Jie Jiao, Meiyan Xu, Qingqing chen, Hefang Zhou, Wangliang Zhou, Second B. 11 (b) shows the ERD/ERS analysis of the ECoG signals of the left-hand and right-hand motor imagery on Mar 1, 2024 · We used the 3D representation of EEG data suggested in [] in order to fully utilize the spatial-temporal information of MI-based EEG data. [41]further proposed an EEG channel active inference neural network (EEG-ARNN) based on graph neural networks, which fully leverages the correlations of signals in both temporal and spatial domains. , 19 ( 2 ) ( 2022 ) , pp. : Deep CNN model based on serial-parallel structure optimization for four-class motor imagery EEG classification. In recent studies, MI-EEG has been used in the rehabilitation process of paralyzed patients, therefore, decoding MI-EEG signals accurately is an important task, and it is difficult task due to the low signal-to-noise ratio and the variation of brain waves between Dec 1, 2023 · Motor Imagery (MI) is a primary paradigm in the field of electroencephalogram (EEG) based brain-computer interface (BCI), which does not rely on the traditional brain information output pathways but uses engineering techniques to become a bridge between the brain and the external devices, providing a better and quality life for the people with disabilities [1] or dyskinesia [2, 3]. 1145/3349341. Jun 1, 2024 · Swin-TCNet: Swin-based temporal-channel cascade network for motor imagery iEEG signal recognition Biomedical Signal Processing and Control , Vol. Biomedical Signal Processing and Control, 63:102144, 2021. In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. N Sharma, A Upadhyay, M Sharma, A Singhal An efficient approach for recognition of motor imagery Jul 12, 2019 · Ma X, Qiu S, Du C, et al. have used six layers of Dense, resulting in a Nov 1, 2024 · In our work, the temporal representation of EEG signals is extracted by superimposing convolutional layers, similar to most deep learning-based EEG classification methods [2, 3]. Therefore, there is a need for a systematic review and categorisation of these approaches. Deep temporal features of the EEG Sep 7, 2023 · For example, Amin et al. EEG-based BCI for real-world applications has recently attracted increasing interest, including robots [4] and mind-controlled wheelchairs [5]. [34] used STFT to convert EEG signals into time–frequency images, used 2D CNN for feature extraction, and then fed into deep network VAE for classification. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine. Sep 25, 2024 · The EEG signal detects brain activity in various patterns, one popular pattern being motor imagery (MI) (Autthasan et al. The BCI system decodes the EEG signals of the subject. For example, the variability and complexity of EEG signals across different study participants, tasks, and contexts; the lack of a clear and consistent definition and measurement of emotions; ethical and privacy issues associated with collecting and processing sensitive personal data; and the Jul 18, 2022 · PDF | On Jul 18, 2022, Yaxin Ma and others published A novel hybrid CNN-Transformer model for EEG Motor Imagery classification | Find, read and cite all the research you need on ResearchGate Aug 22, 2023 · Brain-Computer Interface (BCI) enables human beings to interact with the outside world through brain intention. Feb 1, 2024 · The two datasets, MI-EEG and ME-EEG, represent motor imagery EEG and motor execution EEG tasks, respectively. 1534 - 1545 Feb 1, 2025 · Xie et al. Although deep learning models have been widely applied in recent years for MI based EEG signal recognition, they often function as black boxes and struggle to pre-cisely localize ERD/ERS, a crucial factor in motor imagery recognition. Recently, a lot of efforts have been made to improve MI signal classification using a combination of Mar 1, 2025 · Physics-informed attention temporal convolutional network for EEG-based motor imagery classification IEEE Trans. 4(Kappa) 75. Biomed. Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. 期刊：Nature 主题：MI-EEG-Transformer. Sep 14, 2017 · In this paper we apply some deep learning models in the domain of EEG analysis, mainly focusing on the classification problem of motor imagery EEG data. 2. Deep learning methods can be used to learn distributed representations of EEG signals automatically across temporal dimension among different channels. Oct 1, 2024 · A channel selection approach based on convolutional neural network for multi-channelchannel EEG motor imagery decoding, in: 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 2019. Inform. , 29 ( 2021 ) , pp. , 2023) are also becoming more prevalent. 3 days ago · Electroencephalography (EEG)-based motor imagery (MI) is extensively utilized in clinical rehabilitation and virtual reality-based movement control. Feb 27, 2024 · A novel decoding framework for motor imagery EEG signals, known as the Spatial Filter Temporal Graph Convolutional Network (SF-TGCN), is proposed in this study. However, numerous studies have demonstrated that the optimal convolution scale varies across subjects and even within different sessions for the same subject. Sep 1, 2023 · Deep spatial-temporal neural network for classification of EEG-based motor imagery Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science ( 2019 ) , pp. e. Decoding EEG-based MI signals is challenging Sep 15, 2024 · By incorporating spatial and temporal Mamba encoders, STMambaNet effectively captures the intricate dynamics in both space and time, significantly enhancing the decoding performance of EEG signals for MI classification. Aug 1, 2022 · In Zhao et al. Feb 1, 2025 · A temporal-spectral-based squeeze-and-excitation feature fusion network for motor imagery EEG decoding IEEE Trans. 57%: tf: Code: 之前使用的代码 Feb 1, 2020 · A spatio-temporal energy maps generation scheme followed by deep learning classification model and Long-Short-Term-Memory based neural network has been proposed to classify the temporal series of energy maps. 265 - 272 A major challenge in electroencephalogram (EEG)-based BCI development and research is the cross-subject classification of motor imagery data. Aug 9, 2020 · In emerging research field of interdisciplinary studies, EEG plays an important role in brain-computer interface due to the good portability, low cost and high temporal resolution of EEG devices. , 2008) is used in the data acquisition stage. Nov 20, 2020 · The EEG signals acquired by the EEG headset reflect different fluctuation patterns as subjects perform motor imagery or implement specific actions. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. It can be viewed as a psychological rehearsal of motor actions without any actual movement output, primarily associated with the brain’s motor cortex mu (8–12 Hz) and beta (18–26 Hz) EEG signal rhythms. Nov 1, 2023 · Proposed a deep temporal network-based model for motor recognition on unprocessed (raw) MI EEG signal. It can be used to decode the intention of users. This dataset is intended for research on brain–computer interfaces (BCIs) based on motor imagery. SNNs utilize spike sequences to characterize and convey information, offering a more bio-interpretable approach and consuming less energy than artificial neural networks (ANNs). These include neurological rehabilitation, stroke recovery, virtual games, [6] and robotic arm control [7]. 背景 (信号分解+机器学习)在（多类大数据）集表现不佳; LSTM无法对非常长期的依赖关系建模; NLP变压网络解决长期依赖性问题; 目的 Deep recurrent spatio-temporal neural network for motor imagery based BCI. In this paper, we propose a novel feature extraction method which leads to promising MI classification performance. This paper proposes a novel architecture of a deep neural network for EEG-based motor imagery classification that allows us to analyze the learned network weights from a neurophysiological perspective, thus providing an insight into the underlying patterns inherent in motor imagery induced EEG signals. Neural Syst. Oct 1, 2024 · EEG based on motor imagery (MI-EEG) has been applied in motion control, medicine, entertainment, and other fields. Aug 30, 2024 · In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Motor imagery (MI) based Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing movement intention of severely disabled persons. proposed a novel deep learning framework based on graph convolutional neural networks (GCNs), which effectively improves the decoding performance of original EEG signals in different types of motor imagery tasks by capturing functional topological relationships of EEG electrodes [29]. In this paper, we propose a spatio-temporal energy May 13, 2023 · Finally, the deep representations extracted from the network are domain-invariant, thus achieving transfers for the classification of motor imagery. Jan 1, 2024 · The attention module helps to calibrate the features so that the model selectively captures valuable feature channels and suppresses useless feature channels, thus improving the discriminative power of the network. , 2021). Addressing computational and efficiency challenges in self-attention mechanisms of transformer models, effectively modeling both local and global contexts AbstractThe electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. (2022) proposed a parallel Transformer-based and three-dimensional convolutional neural network (3D-CNN) based multi-channel EEG emotion recognition model. Aug 1, 2023 · To exploit the temporal and spatial relationship of iEEG signals, we propose a Swin-Based Temporal Cascade Channel Network (Swin-TCNet) for motor imagery classification tasks, which is composed of three modules: Temporal-Swin (TS), Channel-Swin (CS) and Classifier. Human-computer interaction (HCI) based on electroencephalogram (EEG) has become the main research direction in the field of BCI. Song et al. Motor Imagery: EEG-based motor imagery classification using convolutional neural networks with local reparameterization trick: CNN: ESWA: 2022: Motor Imagery: A framework for motor imagery with LSTM neural network: LSTM: CMPB: 2022: Motor Imagery: Motor imagery based brain-computer interface: improving the EEG classification using Delta rhythm Dec 15, 2024 · Motor imagery (MI) classification is key for brain-computer interfaces (BCIs). This paper proposes a model based on deep learning network to decode EEG-based MI actions, combining deep separation convolution network (DSCNN) and bidirectional long short-term memory (BLSTM) neural network. Eng. The ability to correctly analyze a subject’s intention from a scene is one of the key factors in realizing the MI-BCI. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. However, with the exponential growth of all digital data forms (time-series signals, images, and videos); it is soon realized the inadequacy of those neural networks to cover all states of diversity within a large amount of data [18]. 27(10), 2164–2177 (2019) Article Google Scholar Zhao, X. However, current classification methods Nov 3, 2023 · The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. Abstract. Feb 1, 2025 · To capture long-term dependencies in longer sequence data and further decode advanced temporal information within EEG signals, we designed a deep temporal network model. 265–272. A wide range of methods have been proposed to design GNN-based classifiers. CNN represents a deep learning architecture capable of discerning valuable patterns from unprocessed EEG signals, thereby diminishing the necessity for manual feature extraction, and is widely used because of this advantage [18]. Due to the highly individualized nature of EEG signals, it has been difficult to develop a cross-subject classification method that achieves sufficiently high accuracy when predicting the subject’s is imperative for bolstering subject-independent motor imagery EEG recognition per-formance. A research at the intersection of neuroscience, machine learning, and signal processing, aiming to enhance the accuracy and efficiency of EEG-based classification systems and to push the boundaries of BCI research. As the EEG Jan 1, 2024 · As the new generation of artificial neural networks, spiking neural networks (SNNs) have demonstrated their capability in signal processing and target recognition by mimicking the behavior of biological neural systems [11], especially in accurately modeling temporal dynamics data. 1016/j. [9] Ruilong Zhang, Qun Zong, Liqian Dou, Xinyi Zhao, Yifan Tang, and Zhiyu Li. The effectiveness of MLFF features is demonstrated not only in motor imagery decoding but also in motor execution decoding tasks. 015%(10 fold CV) BCIC_IV_2a: 3D CNN: Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion Sep 1, 2023 · Hou et al. [7] proposed an attention-inception method of CNN and LSTM network fusion for motor imagery task classification, which extracts spatial features by CNN and temporal features by LSTM, and then merges all features into a fully connected layer, providing a new idea for feature extraction and classification research of Feb 18, 2025 · The brain-computer interface (BCI) is an emerging technology that enables people with physical disabilities to control and interact with devices only by using their minds and without being dependent on healthy people. Ind. [41] further proposed an EEG channel active inference neural network (EEG-ARNN) based on graph neural networks, which fully leverages the correlations of signals in both temporal and spatial domains. Comparison of the proposed networks with existing state-of-the-art methods (with or without pre-processing steps) on raw MI EEG data. A significant challenge in Jan 1, 2025 · Meanwhile, with the rapid advancements in deep learning within the field of pattern recognition, an increasing number of studies have begun to apply deep learning methods, such as convolutional neural networks (CNN) and residual networks (ResNet), to the classification of motor imagery EEG signals [10]. [7] proposed an attention-inception method of CNN and LSTM network fusion for motor imagery task classification, which extracts spatial features by CNN and temporal features by LSTM, and then merges all features into a fully connected layer, providing a new idea for feature extraction and classification research of Motor imagery (MI) is a key paradigm in EEG-based BCIs, where individuals mentally rehearse movements (Savaki & Raos, 2019), inducing alterations in mu and beta rhythms in the sensorimotor cortex, resulting in specific patterns of event-related desynchronization (ERD) and event-related synchronization (ERS) (Pezzulo et al. ’s paper [50], in order to introduce a new three-dimensional representation of EEG, and propose a deep learning network M3DCNN based on multi-branch 3D convolutional neural network and achieve 75. Performance comparison of LSTM and transformer in binary and multi-class scenarios. Oct 1, 2024 · Motor imagery (MI), one of the most pivotal paradigms used in BCIs, represents a form of motor intention. Feb 4, 2025 · Motor imagery (MI) is currently one of the most researched brain‒computer interface (BCI) paradigms, with convolutional neural networks (CNNs) being extensively used for decoding electroencephalogram (EEG) signals. Aug 1, 2023 · Download Citation | On Aug 1, 2023, Mingyue Xu and others published Swin-TCNet: Swin-based temporal-channel cascade network for motor imagery iEEG signal recognition | Find, read and cite all the Jun 14, 2023 · Two classical deep learning models are the convolutional neural network (CNN) and recurrent neural network (RNN), which are widely used for EEG classification in motor imagery. 1(a) and 1(b). , 2021), and attention-based models (Sibilano et al. There are extensive studies about MI-based intention recognition, most of which heavily rely on staged handcrafted EEG feature extraction and classifier design. , 2022b). Improving EEG-Based Motor Imagery Classification via Spatial and Temporal Recurrent Neural Networks{C}// 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). py file loads and divides the dataset based on two approaches:. Although a number of studies have been carried out for the extraction of hidden patterns and classification of EEG signals, temporal information has rarely been incorporated. Mar 25, 2021 · Motor imaging EEG signal recognition is an important and challenging research problem in human-computer interaction. Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science. 104885 Jan 15, 2024 · The authors of (Hsu and Cheng, 2023) considered the feature information of EEG signals in spatial, temporal and spectral domains and proposed a wavelet-based temporal-spectral-attentional correlation coefficient approach to achieve more accurate MI-EEG recognition. Apr 17, 2024 · Sun et al. Deep spatial-temporal neural network for classification of EEG-based motor imagery. Recently, many deep learning methods have been widely used Jun 1, 2024 · Motor imagery of different parts of the body or other exclusive mind-controlled commands would be reflected in different EEG patterns; from these responses, human intentions could be learned. Until recent years, numerous models had been proposed, ranging from classical algorithms like Common Spatial Pattern (CSP) to deep learning models such as convolutional neural networks (CNNs) and transformers. In this paper, a new neural network model called parallel deep neural Physics-informed attention temporal convolutional network for EEG-based motor imagery classification: 2022: bci2a: tf: Code: 有attention可借鉴: RNN_LSTM: bci: tf: Code: EEG-DL: GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals: 2020: Physionet 88. First, we mapped the channels of the EEG data into a 3D array based on the electrode distributions shown in Fig. In Proceedings of the 2019 International Conference on Artificial Intelligence and Computer May 1, 2024 · Despite its promise, EEG-based emotion identification faces significant challenges and limitations. Oct-2019: IEEE Transactions on Neural Systems and Rehabilitation Engineering: Paper Code: 64. Sensors, 20(12):3496, 2020. May 10, 2022 · So far, EEG-based BCI applications have been developed using neurophysiological patterns, including steady-state visual evoked potential (SSVEP), event-related potential (ERP), movement-related cortical potentials (MRCPs), and motor imagery (MI) [9,10,11,12]. Author Jr. Jun 3, 2024 · The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. Subject-specific (subject-dependent) approach. The temporal and spatial features of EEG were retrieved by creating parallel channel EEG data and positional reconstruction of EEG sequence data, then using the Transformer and 3D Feb 1, 2025 · It uses adaptive weights to integrate branch outputs for increased accuracy. Firstly, the happening and acquisition of EEG signals involve complex neural activities, which can describe the spatial-temporal dependence of neural activities between brain regions. 2249 - 2258 Google Scholar Mar 1, 2023 · Our method uses three branches to extract features with different depths, which extracts more useful features than Xu et al. Author, and Third C. Motor imagery (MI) classification is key for brain-computer interfaces (BCIs). Ko W, Yoon J, Kang E, et al. Appl Deep temporal networks for EEG-based motor imagery recognition. Jan 27, 2025 · Background: Brain–computer interface (BCI) technology opens up new avenues for human–machine interaction and rehabilitation by connecting the brain to machines. MI refers to a pattern of brain activity in which the subject imagines moving a specific part of their body (such as the left or right hand, tongue, or foot) without physically moving it. proposed a multi-channel EEG emotion recognition model based on a parallel transformer and a three-dimensional convolutional neural network (3D-CNN), which can effectively retrieve the temporal and spatial fusion features of EEG. Firstly, based on the spatio-temporal characteristics of EEG, a 5-layer CNN model is built to classify MI tasks (left hand and right hand movement); then the CNN model is applied Sep 1, 2024 · Sun et al. Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition, Wenjie Li, Haoyu Li, Xinlin Sun, Huicong Kang, Shan An, Guoxin Wang, Zhongke Gao Feb 1, 2017 · In this paper, we propose a new method based on the deep convolutional neural network (CNN) to perform feature extraction and classification for single-trial MI EEG. Jan 1, 2021 · Deep spatial-temporal neural network for classification of EEG-based motor imagery ACM International Conference Proceeding Series ( 2019 ) , pp. The model incorporates a multi-domain convolutional block, a multihead self-attention block, and a temporal convolutional block. 2023. Mar 15, 2024 · A novel decoding framework for motor imagery EEG signals, known as the Spatial Filter Temporal Graph Convolutional Network (SF-TGCN), is proposed in this study. bspc. 70 GHz, the result of the proposed method (TL6) for EEG spatio-temporal pattern recognition is compared with four baseline methods, which are CSP feature-based adaptive WT-SVM classifier with or without EEG data alignment (TL1, TL2), TL6 without hyper-parameters optimization (TL3), TL6 without Traditional models combining Convolutional Neural Networks (CNNs) and Transformers for decoding Motor Imagery Electroencephalography (MI-EEG) signals often struggle to capture the crucial interrelationships between local and global features effectively, resulting in suboptimal performance. Nov 1, 2024 · Despite the success of deep learning in various BCI paradigms, there is still room for improvement in EEG decoding. , Member, IEEE Abstract—There is a correlation between adjacent chan-nels of electroencephalogram (EEG), and how to repre- Mar 1, 2023 · Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. proposed a hybrid deep learning model May 1, 2022 · From the resting state to the motor imagery, it is obvious from the figure that the amplitude of C3 rises and the amplitude of C4 decreases when the left-hand movement imagines, and when the right-hand movement imagines is the opposite Fig. proposed a transformer-based spatial–temporal feature learning method ( Song et al. , 2019, Chye et al Feb 1, 2025 · In contrast to some other EEG signals, Motor Imagery EEG (MI-EEG) signals are spontaneously generated without the need for external stimuli. MI-BCI technology holds wide-ranging applications. Existing CNN models can be divided into two types based on the different input forms of the model. 1534 - 1545 Sep 1, 2023 · Deep learning methods such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Recurrent Neural Networks (RNNs) are mainstream algorithms for EEG signal classification (Altaheri et al. , 2021 ) for EEG decoding, which applies attention transformation on the channel dimension and slices the data in the time dimension to obtain a Dec 25, 2022 · Qiao, W. Despite the A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification: Xinqiao Zhao, et al. The dataset was collected from 54 healthy subjects and includes left-hand (L) and right-hand (R) MI-EEG samples. In recent years, researchers have focused on EEG-based intention recognition. Sep 1, 2024 · The EEG Motor Imagery B C I C I V _ 2 a dataset (Brunner et al. In addition, due to inter-individual differences in EEG signals, this discrepancy results in new subjects need spend a amount of calibration time for EEG-based motor imagery brain-computer interface. Sep 9, 2024 · Introducing Temporal-FocalNets, a new framework that enhances the decoding of Motor Imagery EEG signals using a temporal focal modulation system inspired by image recognition techniques. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. In this paper, we propose a novel architecture of a deep neural network for EEG-based motor Feb 1, 2022 · Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer interaction. 76% via the open physiological motor imagery data set EEGMMIDB, which is superior to some advanced research methods for motor imagery task recognition at present and helpful to restore the rehabilitation ability of patients with brain injury. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Feb 1, 2025 · The neural activity (ERD/ERS) generated during motor imagery closely resembles that during actual movement, allowing the translation of motor imagery information in EEG into computer commands for controlling external devices. However, the limitation in decoding MI-EEG signals has restricted the further development of the related Consumer Electronics (CE) industry Apr 7, 2023 · Using MATLAB 2021a and an AMD Ryzen 5-5600X CPU @ 3. Hybrid deep neural network using transfer learning for eeg motor imagery decoding. EEG is a brain signal that can be acquired in a non-invasive way and has a high temporal resolution. [9]. However, the problem is ill-posed as these signals are non-stationary and noisy. Feb 11, 2021 · [27] Cheng L, Li D, Yu G, Zhang Z, Li X and Yu S 2020 A motor imagery eeg feature extraction method based on energy principal component analysis and deep belief networks IEEE Access 8 21453–72. Kim Y J, Kwak N S, Lee S W. Feb 1, 2022 · Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer interaction. , trials in session 1 for training, and trials in session 2 for testing. Jan 1, 2024 · Motor imagery-based brain computer interface (MI-BCI) offers an effective approach for the neuro-rehabilitation of stroke patients, it establishes communication between users and external devices by utilizing motor imagery electroencephalogram (MI-EEG) as a drive signal [1]. dai et al. In this approach, we used the same training and testing data as the original BCI-IV-2a competition division, i. In order to solve the above Sep 7, 2023 · For example, Amin et al. Mar 1, 2025 · In this paper, a new model called MCMTNet is proposed for the classification of motor imagery in EEG signals. Nov 1, 2023 · EEG-based motor imagery (MI) signal classification is a popular area of research due to its applications in robotics, gaming, and medical fields. . Although EEG research has made progress, motor imagery (MI) EEG decoding remains a challenge due to a lack of sample data, a lower signal noise ratio (SNR), and individual differences. Jan-2018: 2018 6th International Conference on Brain-Computer Interface (BCI) URL: BCIC IV 2a: CNN, RNN: Classification of motor imagery for Ear-EEG based brain-computer interface. This model consists of causal convolutions with different dilation rates. Jan 1, 2023 · Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications These applications span from motor imagery decoding, emotion recognition Oct 14, 2020 · The proposed EEG representations and network architectures have allowed to achieve interesting IRs, larger than 96% for identification sessions lasting about 20s, and taken at a temporal distance greater than one year from the enrolment, when performing EEG-based biometric recognition irrespective of the performed mental task, therefore Aug 1, 2024 · The composition of the paper is as follows: In Section 2, we discuss the state-of-the-art techniques for EEG-based MI and QML techniques; in Section 3, we describe the first method based on a quantum genetic algorithm for feature selection; in Section 4, we describe the second proposed method based on a hybrid classical–quantum neural network Aug 1, 2021 · Motor imagery electroencephalography (MI-EEG) signals are generated when a person imagines a task without actually performing it. The high-performance decoding capability of MI-EEG signals is a key issue that Deep Spatial-Temporal Neural Network for Classification of EEG-Based Motor Imagery AICS 2019: Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science As a challenging topic in brain-computer interface (BCI) research, motor imagery classification based on electroencephalogram (EEG) received more and more In this work we proposed Sinc-EEGNet, a lightweight convolutional neural network for EEG-BCI-based motor imagery classification that learns optimal band decomposition and spatial filtering, mimicking the behavior of the well-known FBCSP but learning the filters directly from the raw EEG data. Additionally, EEG Oct 7, 2021 · Finally, the experiments shows that the final intention recognition accuracy reach 97. Furthermore, Jan 1, 2021 · Researchers used traditional neural networks (NNs) for a long time for automating different tasks. & Bi, X. Rehabil. Mar 1, 2024 · His current research interests include Brain-Computer Interfaces (BCIs), particularly the field of Motor Imagery EEG classification. However, the problem is ill-posed Mar 7, 2024 · 论文阅读: Deep temporal networks for EEG-based motor imagery recognition. , et al. Feb 25, 2021 · Electroencephalography (EEG) based motor imagery (MI) is one of the promising Brain–computer interface (BCI) paradigms enable humans to communicate with the outside world based solely on brain intentions. Dec 1, 2019 · Electroencephalography (EEG) motor imagery (MI) signals have recently gained a lot of attention as these signals encode a person’s intent of performing an action. The preprocess. Nov 20, 2020 · On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Although a number Jul 19, 2024 · Given that the EEG signals encompass motor imagery information across temporal, spatial, and frequency domains, several endeavors have focused on improving the decoding accuracy of motor imagery intention by extracting features from spatiotemporal domains, as demonstrated by EEGNet and EEG-TCNet [15, 21, 22]. Although prospective studies have demonstrated promising performance, most of these studies have been affected by the lack of research between groups and individual subjects, and the accuracy of MI classification still has room for improvement. Attention temporal convolutional network for EEG-based motor imagery classification Network for EEG-Based Motor Imagery Classification with Deep CNN for EEG Mar 15, 2024 · A novel decoding framework for motor imagery EEG signals, known as the Spatial Filter Temporal Graph Convolutional Network (SF-TGCN), is proposed in this study. Deep Learning has grasped great attention for recognition of Electroencephalography. The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. Jul 1, 2018 · Deep temporal networks for EEG-based motor imagery recognition. Although convolutional neural networks have been Jul 1, 2021 · Motor imagery (MI) classification based on Electroencephalography (EEG) signal analysis has received a lot of attention for the purpose of movement intent recognition. Jul 12, 2019 · Download Citation | Deep Spatial-Temporal Neural Network for Classification of EEG-Based Motor Imagery | As a challenging topic in brain-computer interface (BCI) research, motor imagery Motor imagery (MI) electroencephalography (EEG) has been used in consumer products supported by brain-computer interfaces (BCI), with existing electronics covering a wide range of domains from artificial intelligence (AI) to the Internet of Things (IoT). The spatial resolution of motor imagery EEG signals is enhanced by constructing the spatial filtering module using the Laplacian graph operator. Among these BCI studies, MI, which classifies EEG signals based on the imagination of Feb 1, 2025 · Spiking neural networks (SNNs) are gaining attention across various fields, including EEG-based brain–computer interfaces. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Bi, Deep spatial-temporal neural network for classification of EEG-based motor imagery, in: Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science, 2019, pp. Jun 24, 2024 · Recognition of eeg signal motor imagery intention based on deep multi-view feature learning. Crossref; Google Scholar [28] Subasi A and Gursoy M I 2010 EEG signal classification using PCA, ICA, LDA and support vector machines Expert Syst. Despite these advancements, these Aug 1, 2023 · Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain–computer interfaces (BCIs). Such capabilities hold significant potential for advancing rehabilitation and the development of According to [24], CNN outperforms other network architectures such as RNN, AE and DBN in performing MI classification. The EEG signals from several people who were engaged in motor imagery tasks are collected in the EEG Motor Imagery BCICIV_2a dataset. Amin et al. One of the most popular BCI paradigms, motor imagery (MI) based on electroencephalograms (EEGs), is applied in healthcare, including rehabilitation. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to The electroencephalogram (EEG) based motor imagery (MI) signal classication, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and Proposed a deep temporal network-based model for motor recognition on unprocessed (raw) MI EEG signal. Until recent years, numerous models had been proposed, ranging from classical algorithms like Common Spatial Pattern (CSP) to deep learning models such as convolutional neural networks Aug 1, 2021 · Motor imagery electroencephalography (MI-EEG) signals are generated when a person imagines a task without actually performing it. For the analysis of brain dynamics, non-stationary motor imagery signals are used. Sharma N , Upadhyay A , Sharma M , Singhal A Sci Rep , 13(1):18813, 01 Nov 2023 Sep 20, 2023 · Motor imagery (MI) electroencephalogram (EEG) signal is recognized as a promising paradigm for brain-computer interface (BCI) systems and has been extensively employed in various BCI applications, including assisting disabled individuals, controlling devices and environments, and enhancing human capabilities. 265 - 272 , 10. 02% classification accuracy on the BCI Competition IV dataset 2a dataset, the experimental results show that the model can extract Sep 1, 2023 · Qiao W, Xiaojun B (2019) Deep spatial-temporal neural network for classification of EEG-based motor imagery. For end-to-end deep learning methods, researchers encode spatial information Oct 31, 2023 · Electroencephalogram (EEG)-based human-computer interaction (HCI) has become a major research direction in the field of brain-computer interface (BCI). Though many achievements have been made in EEG research recently, the lack of sample data and individual differences, effective motor imagery (MI Sep 21, 2023 · There is a correlation between adjacent channels of electroencephalogram (EEG), and how to represent this correlation is an issue that is currently being explored. In recent studies, MI-EEG has been used in the rehabilitation process of paralyzed patients, therefore, decoding MI-EEG signals accurately is an important task, and it is difficult task due to the low signal-to-noise ratio and the variation of brain waves between Jun 1, 2024 · A temporal-spectral-based squeeze-and-excitation feature fusion network for motor imagery EEG decoding IEEE Trans. Jan 1, 2022 · Deep learning (DL) method has emerged as a powerful tool in studying the behavior of Electroencephalogram (EEG)-based motor imagery (MI). Qiao, X. Dec 28, 2024 · W. In this study, we selected the MI-EEG samples for classification research. 3 The Proposed Method In this section, we first describe the notations as well as the definitions that are used later in this work. Researchers have used MI signals to help disabled persons, control devices such as wheelchairs and even for autonomous May 11, 2022 · Recently, various deep neural networks have been applied to classify electroencephalogram (EEG) signal. Nov 1, 2023 · Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. This inherent spontaneity makes MI-EEG particularly well-suited for active BCIs, offering a more flexible interface. (2022) employed five categories of Transformer-based models for EEG-based motor imagery. 3349414 View in Scopus Google Scholar Jul 1, 2024 · OpenBMI motor imagery dataset: The OpenBMI dataset includes EEG samples of motor imagery, event-related potentials, and steady-state visual evoked potentials. HCANN extracts temporal dimensional relevant information between EEG and tasks using depthwise separable convolution by designing multi-scale factor convolutional layers. IEEE Trans. The WMB technique performs better when applied to six DL models (EEGNet, Shallow ConvNet, Deep ConvNet, ResNet, Multiscale filter bank convolutional neural network (MSFBCNN), and EEG-Temporal Convolutional Network (EEG-TCNet)) and is verified on several EEG datasets. Sep 1, 2024 · Sun et al. Traditional neural networks often use serial structure to extract spatial features when dealing with motor imagery EEG signal classification, ignoring temporal information and a large amount of available information in the middle layer, resulting in poor classification performance of MI-BCI. Moreover, they introduced two methods for automatically selecting the optimal number of channels. 85 , Elsevier BV ( 2023 ) , 10. Jan-2018 Nov 1, 2023 · The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming Sep 27, 2022 · However, the recorded EEG signal is easily interfered by other signals, which leads to its low signal-to-noise ratio. Jul 12, 2019 · Ma X, Qiu S, Du C, et al. Oct 1, 2024 · Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. Additionally, PHNN exhibits lower standard deviation on both datasets compared to GL, RL, and SOA models. Facing the accuracy and precision requirements of emotion recognition, this paper combines neural network and proposes a motor imagery EEG signal recognition method based on deep convolutional network. hyclkk nnzf zxmvp snampoy uktp dypvizqm yxkox ajeccz mfkt kmv wbt aauqnzl tur ygbro kpojpw