most citedHearables: Ear EEG Based Driver Fatigue Detection

3 citations · 5 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Targeted Angular Reversal of Weights (TARS) for Knowledge Removal in Large Language Models

Harry J. Davies, Giorgos Iacovides, Danilo P. Mandic

The sheer scale of data required to train modern large language models (LLMs) poses significant risks, as models are likely to gain knowledge of sensitive topics such as bio-securi…

eess.SP2024

In-ear ECG Signal Enhancement with Denoising Convolutional Autoencoders

Edoardo Occhipinti, Marek Zylinski, Harry J. Davies +5

The cardiac dipole has been shown to propagate to the ears, now a common site for consumer wearable electronics, enabling the recording of electrocardiogram (ECG) signals. However,…

cs.LG20242 cited

Interpretable Pre-Trained Transformers for Heart Time-Series Data

Harry J. Davies, James Monsen, Danilo P. Mandic

Decoder-only transformers are the backbone of the popular generative pre-trained transformer (GPT) series of large language models. In this work, we employ this framework to the an…

eess.SP20232 cited

A Deep Matched Filter For R-Peak Detection in Ear-ECG

Harry J. Davies, Ghena Hammour, Marek Zylinski +2

The Ear-ECG provides a continuous Lead I electrocardiogram (ECG) by measuring the potential difference related to heart activity using electrodes that can be embedded within earpho…

eess.SP20233 cited

Hearables: Ear EEG Based Driver Fatigue Detection

Metin C. Yarici, Pierluigi Amadori, Harry Davies +4

Ear EEG based driver fatigue monitoring systems have the potential to provide a seamless, efficient, and feasibly deployable alternative to existing scalp EEG based systems, which…