arXiv AI July 21, 2026 By Kartikey Ahlawat

Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks

arXiv:2607. 16296v1 Announce Type: cross Abstract: Continuous EEG monitoring for epilepsy is constrained by the limited power and memory budgets of wearable and implantable devices.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

efficiency

arXiv AI

Jun 12

Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices

arXiv:2606. 12742v1 Announce Type: new Abstract: Wearable healthcare devices are the fastest-growing Internet of Things (IoT) sector.

By Farough Shayeste Roodi, Parham Zilouchian Moghaddam, Mahdi Mohammadi-nasab, Mehdi Modarressi, Mostafa Ersali Salehi Nasab, Masoud Daneshtalab

efficiency benchmarks

arXiv AI

6d ago

Quantization-Aware Neuromorphic Architecture for Skin Lesion Classification on Resource-Constrained Devices

arXiv:2507. 15958v5 Announce Type: replace-cross Abstract: On-device skin lesion analysis is constrained by the compute and energy cost of conventional CNN inference and by the need for lightweight calibration under clinical data shift.

By Haitian Wang, Xia Cheng, Xinyu Wang, Fiona Wei, Zichen Geng

efficiency

arXiv AI

Aug 10

PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks

arXiv:2608. 07066v1 Announce Type: new Abstract: Spiking neural networks (SNNs) enable sparse and event-driven computation, but their low-bit deployment remains incomplete because recurrent membrane states are commonly retained in floating point even after weight quantization.

By Hui Xie, Tong Shi, Haotong Qin, Aishan Liu, Xiaode Liu, Jinyang Guo

llms computer-vision efficiency

arXiv AI

Jun 18

SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface

arXiv:2606. 18816v1 Announce Type: cross Abstract: Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically exceed the computational limits of embedded hardware.

By Gourav Siddhad, Yogesh Kumar Meena

arXiv AI

Jul 29

Representation Capacity-Matched QNN-SNN Twin Construction for Rate-Encoded SNNs

arXiv:2409. 08290v5 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) promise higher energy efficiency over conventional Quantized Artificial Neural Networks (QNNs) due to their event-driven, spike-based computation.

By Zhanglu Yan, Zhenyu Bai, Kaiwen Tang, Weng-Fai Wong

efficiency

arXiv Machine Learning

Jun 2

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

arXiv:2606. 02166v1 Announce Type: new Abstract: Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life.

By Vigneshwar Hariharan (National University of Singapore), Chithra Reghuvaran (University College Dublin), Arlene John (University of Twente), Nhat Pham (Cardiff University), Omer Rana (Cardiff University), Deepu John (University College Dublin), Ganesh Neelakanta Iyer (National University of Singapore)

llms fine-tuning benchmarks

Related stories

Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices

Quantization-Aware Neuromorphic Architecture for Skin Lesion Classification on Resource-Constrained Devices

PTQ4SNN: Membrane-Aware Post-Training Quantization for Spiking Neural Networks

SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface

Representation Capacity-Matched QNN-SNN Twin Construction for Rate-Encoded SNNs

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction