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Found 6 papers, 5 papers with code
Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models
Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues.
Hierarchical Hypercomplex Network for Multimodal Emotion Recognition
Then, a hypercomplex fusion module learns inter-modal relations among the embeddings of the different modalities.
Towards Explaining Hypercomplex Neural Networks
To achieve this, we define a type of cosine-similarity transform within the parameterized hypercomplex domain.
Attention-Map Augmentation for Hypercomplex Breast Cancer Classification
In this step, a parameterized hypercomplex neural network (PHNN) is employed to perform breast cancer classification.
Breast Cancer Histology Image Classification
Breast Tumour Classification
+4
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals
Multimodal emotion recognition from physiological signals is receiving an increasing amount of attention due to the impossibility to control them at will unlike behavioral reactions, thus providing more reliable information.
Multi-View Hypercomplex Learning for Breast Cancer Screening
To overcome such limitations, in this paper, we propose a methodological approach for multi-view breast cancer classification based on parameterized hypercomplex neural networks.
Ranked #1 on
Cancer-no cancer per breast classification
on InBreast
(using extra training data)
