19 accepted papers
Depression is a prevalent mental health disorder characterized by persistent sadness and a diminished interest in daily activities, early detection of depression facilitates timely intervention, mitigating its adverse effects. Electroencephalography (EEG) signals and eye movements are emerging as pr
Interpreting clinical electroencephalography (EEG) is a laborious, subjective process, and existing computational models are limited to narrow classification tasks rather than holistic interpretation. A key bottleneck for applying powerful Large Vision-Language Models (LVLMs) to this domain is the s…
Large Audio Language Models (LALMs) represent an important frontier in multimodal AI, addressing diverse audio tasks. Recently, post-training of LALMs has received increasing attention due to significant performance improvements over foundation models. While single-stage post-training such as reinfo…
Brain decoding aims to reconstruct video from brain signals. Existing brain decoding frameworks are primarily built on a subject-dependent paradigm, which requires large amounts of brain data for each subject. However, the expensive cost of collecting brain-video data causes severe data scarcity for
When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test outputs, e.g. using a neural network? We study this question on ARC by training neural models for \emph{induction} (inf…
Emotion recognition (ER) plays an important role in the field of modern technology and human-computer interaction. Traditional emotion recognition approaches usually utilize videos as stimuli. However, watching video lacks interaction. Recently, more and more game-related stimuli have been used. To…
Drawing insights from Large Language Models, researchers have developed several large-scale Electroencephalogram (EEG) models (LEMs) to learn a generalized representation adaptable to various tasks. However, such LEMs are scarce and neglecting the potential in reconstruction tasks. Meanwhile, how to…
Affective Brain-Computer Interfaces (aBCIs) have attracted growing attention due to their potential for decoding human emotional states through electroencephalogram (EEG) signals. However, existing deep learning models often struggle to fully capture both the spatial and temporal dependencies in EEG…
The significant variations in emotional patterns across different cultures pose a major challenge for cross-cultural electroencephalogram (EEG) emotion recognition. Moreover, this task must address not only differences in feature distributions among different cultures but also among individuals with…
Multimodal emotion recognition is a crucial research area in the field of affective brain-computer interfaces. However, in practical applications, it is often challenging to obtain all modalities simultaneously. To deal with this problem, researchers focus on using cross-modal methods to learn multi…
Research in emotion recognition using electroencephalography (EEG) has advanced rapidly, and affective EEG-based Brain-computer Interface (aBCI) technology is increasingly moving from lab research to real-world application. Nevertheless, EEG signals are inherently delicate and prone to noise and art…
Emotion recognition through electroencephalography (EEG) has been an area of active research, but the inherent sensitivity of EEG signals to noise and artifacts poses significant challenges, especially in real-world settings. These complications often necessitate the removal of corrupted channels, m…
Iteratively improving and repairing source code with large language models (LLMs), known as refinement, has emerged as a popular way of generating programs that would be too complex to construct in one shot. Given a bank of test cases, together with a candidate program, an LLM can improve that progr…
Our visual experience in daily life are dominated by dynamic change. Decoding such dynamic information from brain activity can enhance the understanding of the brain’s visual processing system. However, previous studies predominately focus on reconstructing static visual stimuli. In this paper, we e…
Multimodal emotion recognition based on electroencephalography (EEG) and eye movements has attracted increasing attention due to their high performance and complementary properties. However, there are two challenges that hinder its practical applications: the inconvenient EEG data collection and hig…
Facial parts swapping aims to selectively transfer regions of interest from the source image onto the target image while maintaining the rest of the target image unchanged. Most studies on face swapping designed specifically for full-face swapping, are either unable or significantly limited when it…
Emotion recognition is a primary and complex task in emotional intelligence. Due to the complexity of human emotions, utilizing multimodal fusion methods can enhance the performance by leveraging the complementary properties of different modalities. In this paper, we propose a Multimodal Multi-view…
Electroencephalogram (EEG) classification tasks have received increasing attention because its high application value. Meanwhile, the great success of general pre-training models in language processing areas inspires us to excavate the potential of an EEG pre-trained model. This model is expected to…
Electroencephalogram (EEG) has been applied in emotion recognition due to excellent temporal resolution with less competitive spatial resolution. This leads to the consequence that the majority of EEG-based emotion recognition models emphasize on exploiting temporal features while ignoring the effic…