LGFormer: integrating local and global representations for EEG decoding

In recent years, deep-learning-based motor imagery (MI) electroencephalography (EEG) decoding methods have shown great potential in the field of the brain–computer interface (BCI). The existing literature is relatively mature in decoding methods for two classes of MI tasks. However, with the increase in MI task classes, decoding studies for four classes of MI tasks need to be further explored. In addition, it is difficult to obtain large-scale EEG datasets. When the training data are limited, deep-learning-based decoding models are prone to problems such as overfitting and poor robustness. In this study, we design a data augmentation method for MI-EEG. The original EEG is slid along the time axis and reconstructed to expand the size of the dataset. Second, we combine the gated recurrent unit (GRU) and convolutional neural network (CNN) to construct a parallel-structured feature fusion network to decode four classes of MI tasks. The parallel structure can avoid temporal, frequency and spatial features interfering with each other. Experimenting on the well-known four-class MI dataset BCI Competition IV 2a shows a global average classification accuracy of 80.7% and a kappa value of 0.74. The proposed method improves the robustness of deep learning to decode small-scale EEG datasets and alleviates the overfitting phenomenon caused by insufficient data. The method can be applied to BCI systems with a small amount of daily recorded data.

Recently, Brain-Computer Interface (BCI) technology has been applied more and more in the field of clinical rehabilitation, which provides an effective way of communication for patients with brain disability and stroke. Because electroencephalogram (EEG) signals are extremely complex and contain many redundant signals, the recognition effect of BCI system based on EEG is not very well. The purpose of this paper is to solve the problem of brain intention classification in brain-computer interface system, which combines Deep Separation Convolutional Neural Network (DSCNN) and Gate Recurrent Unit (GRU) network to classify the motor imagination task of EEG. Firstly, one-dimensional timing EEG signals are transformed into two-dimensional array, and the temporal and spatial features of EEG signals are extracted by separate convolution. Then, these EEG signals containing spatio-temporal features are convolved once to extract spatial features, and the temporal features is extracted by GRU. Finally, the experiments shows that the final intention recognition accuracy reach 97.76% 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.

In the field of Brain-Computer Interface (BCI), usage of Electroencephalogram (EEG) signals provides various applications in many fields. EEG signals investigation is necessary due to an effective way of determining neurological brain disorders. These disorders include Epileptic and autism in the brain structures which are identified by the abnormal synchronous electrical discharge of the neuronal activities. The multichannel EEG signals are recorded these abnormal electrical events which can be positioned on the scalp of the brain. The aim of this research work is to classify various patterns of EEG recording samples namely autistic, epilepsy and normal using the deep learning algorithm. In order to remove the artifacts from EEG dataset, the Independent Components Analysis (ICA) technique is used to minimization the noise which is segmented and filtered using an elliptic band-pass filter. Next, EEG signal transforms to Power Spectrum Density Energy Diagrams (PSDEDs). Therefore, the techniques of feature extraction can transform the signals into various feature vectors which it automatically extracts features from the PSDED by applying Deep Convolutional Neural Networks (DCNN) and by making use of the Fully Connected (EC) layer the signals can be classified as autistic, epilepsy and normal. The classification accuracy created by the suggested DCNN model is achieved 80% for training data set validation.

This paper introduces a methodology based on deep convolutional neural networks (DCNN) for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More specifically, the DCNN is used for classification of the right hand and right foot MI-tasks based electroencephalogram (EEG) signals. The proposed method first transforms the input EEG signals into images by applying the time-frequency (T-F) approaches. The used T-F approaches are short-time-Fourier-transform (STFT) and continuous-wavelet-transform (CWT). After T-F transformation the images of MI-tasks EEG signals are applied to the DCNN stage. The pre-trained DCNN model, AlexNet is explored for classification. The efficiency of the proposed method is evaluated on IVa dataset of BCI competition-III. The evaluation metrics such as accuracy, sensitivity, specificity, F1-score, and kappa value are used for measuring the proposed method results quantitatively. The obtained results show that the CWT approach yields better results than the STFT approach. In addition, the proposed method obtained 99.35% accuracy score is the best one among the existing methods accuracy scores.

Understanding the relationship between rosemary odor and mental workload through deep learning.

Evaluating cognitive load using electroencephalogram (EEG) signals is a crucial research area in the field of Brain-Computer Interfaces (BCI). However, achieving high accuracy and generalization in feature extraction and classification for cognitive load assessment remains a challenge, primarily due to the low signal-to-noise ratio of EEG signals and the inter-individual variability in EEG data. In this study, we propose a novel deep learning architecture that integrates temporal information features and functional connectivity features to enhance EEG signal analysis. Functional connectivity features capture inter-channel information, while temporal features are extracted from continuous signal segments using a Long Short-Term Memory (LSTM) network enhanced with an attention mechanism. The fusion strategy combines these two information streams to leverage their complementary strengths, resulting in improved classification performance. We evaluated our architecture on two publicly available datasets, and the results demonstrate its robustness in cognitive load recognition. Achieving performance comparable to the best existing methods on two public datasets. Ablation studies further substantiate the contributions of each module, demonstrating the importance of combining temporal and functional connectivity features for optimal results. These findings underscore the robustness and versatility of the proposed approach, paving the way for more effective EEG-based BCI applications.

Existing research methods for recognizing EEG (Electroencephalogram) signals in motor imagery (MI) often overlook the dynamic changes of brain networks over time, resulting in insufficient classification accuracy for MI tasks. This article addresses the recognition problem of dynamic changes in brain networks during MI tasks and applies an EEG signal classification method based on multi-layer time-varying functional brain networks. This article uses the BCI (Brain-Computer Interface) Competition IV 2a dataset to preprocess the raw EEG signals through bandpass filtering and CSP (Common Spatial Pattern) algorithm. The EEG signals of the MI task are divided into 7 1-second time windows with a step size of 0.5 seconds. Within each time window, Pearson correlation coefficients between EEG channels can be calculated to generate corresponding brain networks, and multiple time-varying functional brain networks can be constructed by stacking the brain networks from multiple time windows. The network topology features, node degree, clustering coefficient, network efficiency, and multi-layer network features of each window can be extracted, including Multiplex Clustering Coefficient (MCC), Multiplex Participation Coefficient (MPC), and inter layer correlation coefficient. By dividing the dataset through 10 fold cross validation, the random forest algorithm can be used to classify and recognize four types of motion imagination tasks. The experimental results show that the average recognition rate of the article’s method in four types of MI tasks reached 89.19%. This method can improve the classification accuracy of MI tasks and enhance a comprehensive understanding of the dynamic changes in brain networks during the process of MI.

IntroductionIn the field of brain-computer interfaces (BCI), motor imagery (MI) classification is a critically important task, with the primary objective of decoding an individual's MI intentions from electroencephalogram (EEG) signals. However, MI decoding faces significant challenges, primarily due to the inherent complex temporal dependencies of EEG signals.MethodsThis paper proposes a temporal fusion attention network (TFANet), which aims to improve the decoding performance of MI tasks by accurately modeling the temporal dependencies in EEG signals. TFANet introduces a multi-scale temporal self-attention (MSTSA) mechanism that captures temporal variation in EEG signals across different time scales, enabling the model to capture both local and global features. Moreover, the model adaptively adjusts the channel weights through a channel attention module, allowing it to focus on key signals related to motor imagery. This further enhances the utilization of temporal features. Moreover, by integrating the temporal depthwise separable convolution fusion network (TDSCFN) module, TFANet reduces computational burden while enhancing the ability to capture temporal patterns.ResultsThe proposed method achieves a within-subject classification accuracy of 84.92% and 88.41% on the BCIC-IV-2a and BCIC-IV-2b datasets, respectively. Furthermore, using a transfer learning approach on the BCIC-IV-2a dataset, a cross-subject classification accuracy of 77.2% is attained.ConclusionThese results demonstrate that TFANet is an effective approach for decoding MI tasks with complex temporal dependencies.

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer's disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature representations of FCNs for automated brain disease classification. Even though convolution operations in CNNs are good at extracting local properties of FCNs, they generally cannot well capture global temporal representations of FCNs. Recently, the transformer technique has demonstrated remarkable performance in various tasks, which is attributed to its effective self-attention mechanism in capturing the global temporal feature representations. However, it cannot effectively model the local network characteristics of FCNs. To this end, in this paper, we propose a novel network structure for Local sequential feature Coupling Global representation learning (LCGNet) to take advantage of convolutional operations and self-attention mechanisms for enhanced FCN representation learning. Specifically, we first build a dynamic FCN for each subject using an overlapped sliding window approach. We then construct three sequential components (i.e., edge-to-vertex layer, vertex-to-network layer, and network-to-temporality layer) with a dual backbone branch of CNN and transformer to extract and couple from local to global topological information of brain networks. Experimental results on two real datasets (i.e., ADNI and ADHD-200) with rs-fMRI data show the superiority of our LCGNet.

EEG-based motor imagery classification using convolutional neural networks with local reparameterization trick

Motor imagery (MI) is an important brain-computer interface (BCI) paradigm. The traditional MI paradigm (imagining different limbs) limits the intuitive control of the outer devices, while fine MI paradigm (imagining different joint movements from the same limb) can control the mechanical arm without cognitive disconnection. However, the decoding performance of fine MI limits its application. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are widely used in BCI systems because of their portability and easy operation. In this study, a fine MI paradigm including four classes (hand, wrist, shoulder and rest) was designed, and the data of EEG-fNIRS bimodal brain activity was collected from 12 subjects. Event-related desynchronization (ERD) from EEG signals shows a contralateral dominant phenomenon, and there is difference between the ERD of the four classes. For fNIRS signal in the time dimension, the time periods with significant difference can be observed in the activation patterns of four MI tasks. Spatially, the signal peak based brain topographic map also shows difference of these four MI tasks. The EEG signal and fNIRS signal of these four classes are distinguishable. In this study, a bimodal fusion network is proposed to improve the fine MI tasks decoding performance. The features of these two modalities are extracted separately by two feature extractors based on convolutional neural networks (CNN). The recognition performance was significantly improved by the bimodal method proposed in this study, compared with the performance of the single-modal network. The proposed method outperformed all comparison methods, and achieved a four-class accuracy of 58.96%. This paper demonstrates the feasibility of EEG and fNIRS bimodal BCI systems for fine MI, and shows the effectiveness of the proposed bimodal fusion method. This research is supposed to support fine MI-based BCI systems with theories and techniques.

Brain Computer Interfaces (BCIs) have several applications. One of the most crucial domains that BCIs cater to is prosthetics, orthotics and rehabilitation devices. Such devices aim to better the lives of specially-abled people. Motor Imagery or Movement Intention (MI) neural signals are collected to obtain a better insight of the underlying neural activities. EEG is the most popular modality for obtaining neural signals due to its advantages like non-invasiveness, portability, and the ease of use. The utilization of MI EEG signals to design BCIs has been heavily exploited. Spectral estimation methods have been exploited to delineate different MI tasks. This article focusses on exploring channel-wise temporal and spectral features of MI EEG signals. A rich MI EEG dataset involving five different motor imageries has been utilized in this study. The analysis based upon channel-wise temporal and spectral decomposition involves two major biomarkers, Event Related Spectral Perturbation (ERSP) and Inter Trial Coherence (ITC). ERSP can be considered as a generalization of power increase and decrease within a frequency range of interest. ITC reflects consistency of rhythms across the trials. This article aims to exploit ERSP and ITC methods to explore relevant time segment, frequency range and informative channels associated to MI signatures.

Convolutional neural networks (CNNs), which can recognize structural/configuration patterns in data with different architectures, have been studied for feature extraction. However, challenges remain regarding leveraging advanced deep learning methods in BCIs. We focus on problems of small-sized training samples and interpretability of the learned parameters and leverages a semi-supervised generative and discriminative learning framework that effectively utilizes synthesized samples with real samples to discover class-discriminative features. Our framework learns the distributional characteristics of EEG signals in an embedding space using a generative model. By using artificially generated and real EEG signals, our framework finds class-discriminative spatio-temporal feature representations that help to correctly discriminate input EEG signals. It is noteworthy that the framework facilitates the exploitation of real, unlabeled samples to better uncover the underlying patterns inherent in a user’s EEG signals. To validate our framework, we conducted experiments comparing our method with conventional linear models by utilizing variants of three existing CNN architectures as generator networks and measuring the performance on three public datasets. Our framework exhibited statistically significant improvements over the competing methods. We investigated the learned network via activation pattern maps and visualized generated artificial samples to empirically justify the stability and neurophysiological plausibility of our model.

Acoustic scene classification and related tasks have been dominated by Convolutional Neural Networks (CNNs). Top-performing CNNs use mainly audio spectograms as input and borrow their architectural design primarily from computer vision. A recent study has shown that restricting the receptive field (RF) of CNNs in appropriate ways is crucial for their performance, robustness and generalization in audio tasks. One side effect of restricting the RF of CNNs is that more frequency information is lost. In this paper, we perform a systematic investigation of different RF configuration for various CNN architectures on the DCASE 2019 Task 1.A dataset. Second, we introduce Frequency Aware CNNs to compensate for the lack of frequency information caused by the restricted RF, and experimentally determine if and in what RF ranges they yield additional improvement. The result of these investigations are several well-performing submissions to different tasks in the DCASE 2019 Challenge.

Alzheimer’s diseases diagnosis using fusion of high informative BiLSTM and CNN features of EEG signal