Tootfinder

MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
Chengyao Wang, Zhisheng Zhong, Bohao Peng, Senqiao Yang, Yuqi Liu, Haokun Gui, Bin Xia, Jingyao Li, Bei Yu, Jiaya Jia
https://arxiv.org/abs/2509.25131

MGM-Omni: Scaling Omni LLMs to Personalized Long-Horizon Speech
We present MGM-Omni, a unified Omni LLM for omni-modal understanding and expressive, long-horizon speech generation. Unlike cascaded pipelines that isolate speech synthesis, MGM-Omni adopts a "brain-mouth" design with a dual-track, token-based architecture that cleanly decouples multimodal reasoning from real-time speech generation. This design enables efficient cross-modal interaction and low-latency, streaming speech generation. For understanding, a unified training strategy coupled with a du…

A Computational Perspective on NeuroAI and Synthetic Biological Intelligence
Dhruvik Patel, Md Sayed Tanveer, Jesus Gonzalez-Ferrer, Alon Loeffler, Brett J. Kagan, Mohammed A. Mostajo-Radji, Ge Wang
https://arxiv.org/abs/2509.23896

A Computational Perspective on NeuroAI and Synthetic Biological Intelligence
NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied …

#Blakes7 Series B, Episode 03 - Weapon
VILA: I think my brain must be furring up.
GAN: IMIPAK might be something they can use against us, Vila.
VILA: Only if it can run faster than we can.
BLAKE: Jenna, set a course for that planet.
https…

Interactive Semantic Segmentation for Phosphene Vision Neuroprosthetics
Eleftherios Papadopoulos (Donders Institute of Brain, Cognition and Behaviour), Yagmur G\"u\c{c}l\"ut\"urk (Donders Institute of Brain, Cognition and Behaviour)
https://arxiv.org/abs/2509.19957

Interactive Semantic Segmentation for Phosphene Vision Neuroprosthetics
Visual impairments present significant challenges to individuals worldwide, impacting daily activities and quality of life. Visual neuroprosthetics offer a promising solution, leveraging advancements in technology to provide a simplified visual sense through devices comprising cameras, computers, and implanted electrodes. This study investigates user-centered design principles for a phosphene vision algorithm, utilizing feedback from visually impaired individuals to guide the development of a g…

Low-Cost and Detunable Wireless Resonator Glasses for Enhanced Eye MRI with Concurrent High-Quality Whole Brain MRI
Ming Lu, Xiaoyue Yang, Jason Moore, Pingping Li, Adam W. Anderson, John C. Gore, Seth A. Smith, Xinqiang Yan
https://arxiv.org/abs/2509.08797

Low-Cost and Detunable Wireless Resonator Glasses for Enhanced Eye MRI with Concurrent High-Quality Whole Brain MRI
Purpose: To develop and evaluate a wearable wireless resonator glasses design that enhances eye MRI signal-to-noise ratio (SNR) without compromising whole-brain image quality at 7 T. Methods: The device integrates two detunable LC loop resonators into a lightweight, 3D-printed frame positioned near the eyes. The resonators passively couple to a standard 2Tx/32Rx head coil without hardware modifications. Bench tests assessed tuning, isolation, and detuning performance. B1$^+$ maps were measure…

Design and Validation of Metasurface Transmitarrays for Time-Reversal Microwave Hyperthermia in Deep Brain Tumors
Alireza Rahmani, Mohammad Javad Hajiahmadi, Reza Faraji-Dana
https://arxiv.org/abs/2509.14429

Design and Validation of Metasurface Transmitarrays for Time-Reversal Microwave Hyperthermia in Deep Brain Tumors
This work presents a systematic design of a metasurface-based microwave hyperthermia system, leveraging the time-reversal technique to achieve precise focusing of electromagnetic energy at deep-seated brain tumors. The manuscript includes detailed numerical simulations on realistic and spherical head models, as well as the experimental realization of a quad-layer transmissive metasurface and an electrothermal head phantom. This represents the first experimental demonstration of metasurface-base…

Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification
Kaouther Mouheb, Marawan Elbatel, Janne Papma, Geert Jan Biessels, Jurgen Claassen, Huub Middelkoop, Barbara van Munster, Wiesje van der Flier, Inez Ramakers, Stefan Klein, Esther E. Bron
https://arxiv.org/abs/2508.21458

Federated Fine-tuning of SAM-Med3D for MRI-based Dementia Classification
While foundation models (FMs) offer strong potential for AI-based dementia diagnosis, their integration into federated learning (FL) systems remains underexplored. In this benchmarking study, we systematically evaluate the impact of key design choices: classification head architecture, fine-tuning strategy, and aggregation method, on the performance and efficiency of federated FM tuning using brain MRI data. Using a large multi-cohort dataset, we find that the architecture of the classification…

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset
Surej Mouli, Ramaswamy Palaniappan
https://arxiv.org/abs/2508.01510 https://arx…

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset
A fully customisable chip-on board (COB) LED design to evoke two brain responses simultaneously (steady state visual evoked potential (SSVEP) and transient evoked potential, P300) is discussed in this paper. Considering different possible modalities in braincomputer interfacing (BCI), SSVEP is widely accepted as it requires a lesser number of electroencephalogram (EEG) electrodes and minimal training time. The aim of this work was to produce a hybrid BCI hardware platform to evoke SSVEP and P30…

Optimizing BCI Rehabilitation Protocols for Stroke: Exploring Task Design and Training Duration
Aniana Cruz, Marko Kuzmanoski, Gabriel Pires
https://arxiv.org/abs/2510.08082 htt…

Optimizing BCI Rehabilitation Protocols for Stroke: Exploring Task Design and Training Duration
Stroke is a leading cause of long-term disability and the second most common cause of death worldwide. Although acute treatments have advanced, recovery remains challenging and limited. Brain-computer interfaces (BCIs) have emerged as a promising tool for post-stroke rehabilitation by promoting neuroplasticity. However, clinical outcomes remain variable, and optimal protocols have yet to be established. This study explores strategies to optimize BCI-based rehabilitation by comparing motor image…