EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition

Learning from M/EEG data with variable brain activation delays

International audienceMagneto- and electroencephalography (M/EEG) measure the electromagnetic signals produced by brain activity. In order to address the issue of limited signal-to-noise ratio (SNR) with raw data, acquisitions consist of multiple repetitions of the same experiment. An important challenge arising from such data is the variability of brain activations over the repetitions. It hinders statistical analysis such as prediction performance in a supervised learning setup. One such confounding variability is the time offset of the peak of the activation, which varies across repetitions. We propose to address this misalignment issue by explicitly modeling time shifts of different brain responses in a classification setup. To this end, we use the latent support vector machine (LSVM) formulation, where the latent shifts are inferred while learning the classifier parameters. The inferred shifts are further used to improve the SNR of the M/EEG data, and to infer the chronometry and the sequence of activations across the brain regions that are involved in the experimental task. Results are validated on a long term memory retrieval task, showing significant improvement using the proposed latent discriminative method

Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods

Lightweight Machine Learning with Brain Signals

Electroencephalography(EEG) signals are gaining popularity in Brain-Computer Interface(BCI) systems and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks(CNNs) or graph neural networks(GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account; 3) navigating and training deep learning model with the most critical EEG channels; 4) detecting most similar EEG segments with target subject to reduce the cost of computation as well as inter-subject variability. Specifically, we construct a task-adaptive graph representation of brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. Lastly, contributory EEG segments are detected by several similarity estimation metrics, we then evaluate and train our proposed framework upon detected EEG segments to compare the performance of different metrics in EEG BCI tasks. We empirically show that our proposed approach, SIFT-EEG, outperforms state-of-the-art, with around 4% and 7% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data. Moreover, the best-performed metric can achieve a high level of accuracy with less than 9% training data, suggesting a possible shift in direction for future works other than simply scaling up the model

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging