EEG-based image classification using an efficient geometric deep network based on functional connectivity

To ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method's efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data. The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users' visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently availabl

Graph Neural Network-based EEG Classification:A Survey

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.</p

Face Emotion Recognition Based on Machine Learning: A Review

Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions

CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis

Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a signal spectrogram image based CNN-XGBoost fusion method for recognising three dimensions of emotion, namely arousal (calm or excitement), valence (positive or negative feeling) and dominance (without control or empowered). We used a benchmark dataset called DREAMER where the EEG signals were collected from multiple stimulus along with self-evaluation ratings. In our proposed method, we first calculate the Short-Time Fourier Transform (STFT) of the EEG signals and convert them into RGB images to obtain the spectrograms. Then we use a two dimensional Convolutional Neural Network (CNN) in order to train the model on the spectrogram images and retrieve the features from the trained layer of the CNN using a dense layer of the neural network. We apply Extreme Gradient Boosting (XGBoost) classifier on extracted CNN features to classify the signals into arousal, valence and dominance of human emotion. We compare our results with the feature fusion-based state-of-the-art approaches of emotion recognition. To do this, we applied various feature extraction techniques on the signals which include Fast Fourier Transformation, Discrete Cosine Transformation, Poincare, Power Spectral Density, Hjorth parameters and some statistical features. Additionally, we use Chi-square and Recursive Feature Elimination techniques to select the discriminative features. We form the feature vectors by applying feature level fusion, and apply Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) classifiers on the fused features to classify different emotion levels. The performance study shows that the proposed spectrogram image based CNN-XGBoost fusion method outperforms the feature fusion-based SVM and XGBoost methods. The proposed method obtained the accuracy of 99.712% for arousal, 99.770% for valence and 99.770% for dominance in human emotion detection.publishedVersio

DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection

Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0\% and 79.6\% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times

EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG With an Application to Emotion Recognition

How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG studies work in a task driven manner and explore the valid EEG features with a supervised model, which would be limited by the given labels to a great extent. In this paper, we propose a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature characterization and fusion model, which is termed as EEGFuseNet. EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering both spatial and temporal dynamics are automatically characterized. Comparing to the existing features, the characterized deep EEG features could be considered to be more generic and independent of any specific EEG task. The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases. The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and performs efficiently in the representation and fusion of dynamic EEG features. In particular, EEGFuseNet is established as an optimal unsupervised fusion model with promising cross-subject emotion recognition performance. It proves EEGFuseNet is capable of characterizing and fusing deep features that imply comparative cortical dynamic significance corresponding to the changing of different emotion states, and also demonstrates the possibility of realizing EEG based cross-subject emotion recognition in a pure unsupervised manner