BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation

Recent advances in decoding language from brain signals (EEG and MEG) have been significantly driven by pre-trained language models, leading to remarkable progress on publicly available non-invasive EEG/MEG datasets. However, previous works predominantly utilize teacher forcing during text generation, leading to significant performance drops without its use. A fundamental issue is the inability to establish a unified feature space correlating textual data with the corresponding evoked brain signals. Although some recent studies attempt to mitigate this gap using an audio-text pre-trained model, Whisper, which is favored for its signal input modality, they still largely overlook the inherent differences between audio signals and brain signals in directly applying Whisper to decode brain signals. To address these limitations, we propose a new multi-stage strategy for semantic brain signal decoding via vEctor-quantized speCtrogram reconstruction for WHisper-enhanced text generatiOn, termed BrainECHO. Specifically, BrainECHO successively conducts: 1) Discrete autoencoding of the audio spectrogram; 2) Brain-audio latent space alignment; and 3) Semantic text generation via Whisper finetuning. Through this autoencoding--alignment--finetuning process, BrainECHO outperforms state-of-the-art methods under the same data split settings on two widely accepted resources: the EEG dataset (Brennan) and the MEG dataset (GWilliams). The innovation of BrainECHO, coupled with its robustness and superiority at the sentence, session, and subject-independent levels across public datasets, underscores its significance for language-based brain-computer interfaces.

Semantic-Guided Multimodal Sentiment Decoding with Adversarial Temporal-Invariant Learning

Multimodal sentiment analysis aims to learn representations from different modalities to identify human emotions. However, existing works often neglect the frame-level redundancy inherent in continuous time series, resulting in incomplete modality representations with noise. To address this issue, we propose temporal-invariant learning for the first time, which constrains the distributional variations over time steps to effectively capture long-term temporal dynamics, thus enhancing the quality of the representations and the robustness of the model. To fully exploit the rich semantic information in textual knowledge, we propose a semantic-guided fusion module. By evaluating the correlations between different modalities, this module facilitates cross-modal interactions gated by modality-invariant representations. Furthermore, we introduce a modality discriminator to disentangle modality-invariant and modality-specific subspaces. Experimental results on two public datasets demonstrate the superiority of our model. Our code is available at https://github.com/X-G-Y/SATI.

Robust Temporal-Invariant Learning in Multimodal Disentanglement

Multimodal sentiment recognition aims to learn representations from different modalities to identify human emotions. However, previous works does not suppresses the frame-level redundancy inherent in continuous time series, resulting in incomplete modality representations with noise. To address this issue, we propose the Temporal-invariant learning, which minimizes the distributional differences between time steps to effectively capture smoother time series patterns, thereby enhancing the quality of the representations and robustness of the model. To fully exploit the rich semantic information in textual knowledge, we propose a Text-Driven Fusion Module (TDFM). To guide cross-modal interactions, TDFM evaluates the correlations between different modality through modality-invariant representations. Furthermore, we introduce a modality discriminator to disentangle modality-invariant and modality-specific subspaces. Experimental results on two public datasets demonstrate the superiority of our model.

EEG-MACS: Manifold Attention and Confidence Stratification for EEG-based Cross-Center Brain Disease Diagnosis under Unreliable Annotations

Cross-center data heterogeneity and annotation unreliability significantly challenge the intelligent diagnosis of diseases using brain signals. A notable example is the EEG-based diagnosis of neurodegenerative diseases, which features subtler abnormal neural dynamics typically observed in small-group settings. To advance this area, in this work, we introduce a transferable framework employing Manifold Attention and Confidence Stratification (MACS) to diagnose neurodegenerative disorders based on EEG signals sourced from four centers with unreliable annotations. The MACS framework's effectiveness stems from these features: 1) The Augmentor generates various EEG-represented brain variants to enrich the data space; 2) The Switcher enhances the feature space for trusted samples and reduces overfitting on incorrectly labeled samples; 3) The Encoder uses the Riemannian manifold and Euclidean metrics to capture spatiotemporal variations and dynamic synchronization in EEG; 4) The Projector, equipped with dual heads, monitors consistency across multiple brain variants and ensures diagnostic accuracy; 5) The Stratifier adaptively stratifies learned samples by confidence levels throughout the training process; 6) Forward and backpropagation in MACS are constrained by confidence stratification to stabilize the learning system amid unreliable annotations. Our subject-independent experiments, conducted on both neurocognitive and movement disorders using cross-center corpora, have demonstrated superior performance compared to existing related algorithms. This work not only improves EEG-based diagnostics for cross-center and small-setting brain diseases but also offers insights into extending MACS techniques to other data analyses, tackling data heterogeneity and annotation unreliability in multimedia and multimodal content understanding.

AFBT GAN: enhanced explainability and diagnostic performance for cognitive decline by counterfactual generative adversarial network

Existing explanation results of functional connectivity (FC) are normally generated by using classification result labels and correlation analysis methods such as Pearson's correlation or gradient backward. However, the diagnostic model is still trained on the black box model and might lack the attention of FCs in important regions during the training. To enhance the explainability and improve diagnostic performance, providing prior knowledge on neurodegeneration-related regions when healthy subjects (HC) develop into subject cognitive decline (SCD) and mild cognitive impairment (MCI) for the diagnostic model is a key step. To better determine the neurodegeneration-related regions, we employ counterfactual reasoning to generate the target label FC matrices derived from source label FC and then subtract source label FC with target label FC. The counterfactual reasoning architecture is constructed by adaptive forward and backward transformer generative adversarial network (AFBT GAN), which is specifically designed by network property in FC and inverse patch embedding operation in the transformer. The specific design can make the model focus more on the current network correlation and employ the global insight of the transformer to reconstruct FC, which both help the generation of high-quality target label FC. The validation experiments are conducted on both clinical and public datasets, the generated attention map are both vital correlated to cognitive function and the diagnostic performance is also significant. The code is available at https://github.com/SXR3015/AFBT-GAN.

Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder

Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs). However, EEG-based language decoding is still in its nascent stages, facing several technical issues such as: 1) Absence of a hybrid strategy that can effectively integrate cross-modality (between EEG and text) self-learning with intra-modality self-reconstruction of EEG features or textual sequences; 2) Under-utilization of large language models (LLMs) to enhance EEG-based language decoding. To address above issues, we propose the Contrastive EEG-Text Masked Autoencoder (CET-MAE), a novel model that orchestrates compound self-supervised learning across and within EEG and text through a dedicated multi-stream encoder. Furthermore, we develop a framework called E2T-PTR (EEG-to-Text decoding using Pretrained Transferable Representations), which leverages pre-trained modules alongside the EEG stream from CET-MAE and further enables an LLM (specifically BART) to decode text from EEG sequences. Comprehensive experiments conducted on the popular text-evoked EEG database, ZuCo, demonstrate the superiority of E2T-PTR, which outperforms the state-of-the-art in ROUGE-1 F1 and BLEU-4 scores by 8.34% and 32.21%, respectively. These results indicate significant advancements in the field and underscores the proposed framework's potential to enable more powerful and widespread BCI applications.

HA-HI: Synergising fMRI and DTI through Hierarchical Alignments and Hierarchical Interactions for Mild Cognitive Impairment Diagnosis

Early diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) utilizing multi-modal magnetic resonance imaging (MRI) is a pivotal area of research. While various regional and connectivity features from functional MRI (fMRI) and diffusion tensor imaging (DTI) have been employed to develop diagnosis models, most studies integrate these features without adequately addressing their alignment and interactions. This limits the potential to fully exploit the synergistic contributions of combined features and modalities. To solve this gap, our study introduces a novel Hierarchical Alignments and Hierarchical Interactions (HA-HI) method for MCI and SCD classification, leveraging the combined strengths of fMRI and DTI. HA-HI efficiently learns significant MCI- or SCD- related regional and connectivity features by aligning various feature types and hierarchically maximizing their interactions. Furthermore, to enhance the interpretability of our approach, we have developed the Synergistic Activation Map (SAM) technique, revealing the critical brain regions and connections that are indicative of MCI/SCD. Comprehensive evaluations on the ADNI dataset and our self-collected data demonstrate that HA-HI outperforms other existing methods in diagnosing MCI and SCD, making it a potentially vital and interpretable tool for early detection. The implementation of this method is publicly accessible at https://github.com/ICI-BCI/Dual-MRI-HA-HI.git.

Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition

Electroencephalography (EEG) is an objective tool for emotion recognition with promising applications. However, the scarcity of labeled data remains a major challenge in this field, limiting the widespread use of EEG-based emotion recognition. In this paper, a semi-supervised Dual-stream Self-Attentive Adversarial Graph Contrastive learning framework (termed as DS-AGC) is proposed to tackle the challenge of limited labeled data in cross-subject EEG-based emotion recognition. The DS-AGC framework includes two parallel streams for extracting non-structural and structural EEG features. The non-structural stream incorporates a semi-supervised multi-domain adaptation method to alleviate distribution discrepancy among labeled source domain, unlabeled source domain, and unknown target domain. The structural stream develops a graph contrastive learning method to extract effective graph-based feature representation from multiple EEG channels in a semi-supervised manner. Further, a self-attentive fusion module is developed for feature fusion, sample selection, and emotion recognition, which highlights EEG features more relevant to emotions and data samples in the labeled source domain that are closer to the target domain. Extensive experiments conducted on two benchmark databases (SEED and SEED-IV) using a semi-supervised cross-subject leave-one-subject-out cross-validation evaluation scheme show that the proposed model outperforms existing methods under different incomplete label conditions (with an average improvement of 5.83% on SEED and 6.99% on SEED-IV), demonstrating its effectiveness in addressing the label scarcity problem in cross-subject EEG-based emotion recognition.

EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognition

Electroencephalography (EEG) is an objective tool for emotion recognition and shows promising performance. However, the label scarcity problem is a main challenge in this field, which limits the wide application of EEG-based emotion recognition. In this paper, we propose a novel semi-supervised learning framework (EEGMatch) to leverage both labeled and unlabeled EEG data. First, an EEG-Mixup based data augmentation method is developed to generate more valid samples for model learning. Second, a semi-supervised two-step pairwise learning method is proposed to bridge prototype-wise and instance-wise pairwise learning, where the prototype-wise pairwise learning measures the global relationship between EEG data and the prototypical representation of each emotion class and the instance-wise pairwise learning captures the local intrinsic relationship among EEG data. Third, a semi-supervised multi-domain adaptation is introduced to align the data representation among multiple domains (labeled source domain, unlabeled source domain, and target domain), where the distribution mismatch is alleviated. Extensive experiments are conducted on two benchmark databases (SEED and SEED-IV) under a cross-subject leave-one-subject-out cross-validation evaluation protocol. The results show the proposed EEGmatch performs better than the state-of-the-art methods under different incomplete label conditions (with 6.89% improvement on SEED and 1.44% improvement on SEED-IV), which demonstrates the effectiveness of the proposed EEGMatch in dealing with the label scarcity problem in emotion recognition using EEG signals. The source code is available at https://github.com/KAZABANA/EEGMatch.

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 are working on handcrafted features with a supervised modeling, which would be limited by experience and human feedbacks to a great extent. In this paper, we propose a practical hybrid unsupervised deep CNN-RNN-GAN based EEG feature characterization and fusion model, which is termed as EEGFuseNet. EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering spatial and temporal dynamics are automatically characterized. Comparing to the handcrafted features, the 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 a famous public emotion database. The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and manage and perform efficiently in the representation and fusion of dynamic EEG features. In particular, EEGFuseNet is established as an optimal unsupervised fusion model with promising subject-based leave-one-out results in the recognition of four emotion dimensions (valence, arousal, dominance and liking), which demonstrates the possibility of realizing EEG based cross-subject emotion recognition in a pure unsupervised manner.