Automated seizure detection is of great importance to epilepsy diagnosis and treatment.

The low-resource label analysis and representation visualization further illustrate the effectiveness of our pre-training strategy.

In this paper, we reconstruct naturalistic images directly from macaque brain signals using a convolutional neural network (CNN) based decoder. We investigate the ability of this CNN-based decoding technique to differentiate among neuronal populations from areas V1, V4, and IT, revealing distinct readout characteristics for each. This research marks a progression from low-level to high-level brain signals, thereby enriching the existing framework for utilizing CNN-based decoders to decode brain activity. Our results demonstrate high-precision reconstructions of naturalistic images, highlighting the efficiency of CNN-based decoders in advancing our knowledge of how the brain's representations translate into pixels. Additionally, we present a novel space-time-resolved decoding technique, demonstrating how temporal resolution in decoding can advance our understanding of neural representations. Moreover, we introduce a learned receptive field layer that sheds light on the CNN-based model's data processing during training, enhancing understanding of its structure and interpretive capacity.

Understanding how the brain encodes external stimuli and how these stimuli can be decoded from the measured brain activities are long-standing and challenging questions in neuroscience. In this paper, we focus on reconstructing the complex image stimuli from fMRI (functional magnetic resonance imaging) signals. Unlike previous works that reconstruct images with single objects or simple shapes, our work aims to reconstruct image stimuli that are rich in semantics, closer to everyday scenes, and can reveal more perspectives. However, data scarcity of fMRI datasets is the main obstacle to applying state-of-the-art deep learning models to this problem. We find that incorporating an additional text modality is beneficial for the reconstruction problem compared to directly translating brain signals to images. Therefore, the modalities involved in our method are: (i) voxel-level fMRI signals, (ii) observed images that trigger the brain signals, and (iii) textual description of the images.

Smartwatches are cool and all, but have you considered wearable neurotech? Ten years ago, a Fitbit was about as sophisticated a wearable as you could get. Then came the sleeker, more unassuming Oura ring . Now there's a new breed of wearables--built for your head. Instead of tracking your step count, heart rate, and skin temperature, these devices are designed to read your brain waves.

Neural decoding, a critical component of Brain-Computer Interface (BCI), has recently attracted increasing research interest. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, the in-depth exploration of model architectures remains underexplored, despite its proven effectiveness in other tasks such as energy forecasting and image classification. In this study, we propose NeuroSketch, an effective framework for neural decoding via systematic architecture optimization. Starting with the basic architecture study, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this, we optimize the architecture from macro- to micro-level, achieving improvements in performance at each step. The exploration process and model validations take over 5,000 experiments spanning three distinct modalities (visual, auditory, and speech), three types of brain signals (EEG, SEEG, and ECoG), and eight diverse decoding tasks. Experimental results indicate that NeuroSketch achieves state-of-the-art (SOTA) performance across all evaluated datasets, positioning it as a powerful tool for neural decoding. Our code and scripts are available at https://github.com/Galaxy-Dawn/NeuroSketch.

We develop a novel characterization of extremal dependence between two cortical regions of the brain when its signals display extremely large amplitudes. We show that connectivity in the tails of the distribution reveals unique features of extreme events (e.g., seizures) that can help to identify their occurrence. Numerous studies have established that connectivity-based features are effective for discriminating brain states. Here, we demonstrate the advantage of the proposed approach: that tail connectivity provides additional discriminatory power, enabling more accurate identification of extreme-related events and improved seizure risk management. Common approaches in tail dependence modeling use pairwise summary measures or parametric models. However, these approaches do not identify channels that drive the maximal tail dependence between two groups of signals -- an information that is useful when analyzing electroencephalography of epileptic patients where specific channels are responsible for seizure occurrences. A familiar approach in traditional signal processing is canonical correlation, which we extend to the tails to develop a visualization of extremal channel-contributions. Through the tail pairwise dependence matrix (TPDM), we develop a computationally-efficient estimator for our canonical tail dependence measure. Our method is then used for accurate frequency-based soft clustering of neonates, distinguishing those with seizures from those without.

This setup enables efficient, high-quality visual reconstructions across subjects from one experimental trial.

Neural Information Processing Systems http://nips.cc/

We focus on stimuli reconstruction in this study.