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001-es BibID:	BIBFORM126074
035-os BibID:	(WOS)001323342700001 (Scopus)85205239003
Első szerző:	Pap, Iuliu Alexandru
Cím:	eHealth Assistant AI Chatbot Using a Large Language Model to Provide Personalized Answers through Secure Decentralized Communication / Iuliu Alexandru Pap, Stefan Oniga
Dátum:	2024
ISSN:	1424-8220
Megjegyzések:	In this paper, we present the implementation of an artificial intelligence health assistant designed to complement a previously built eHealth data acquisition system for helping both patients and medical staff. The assistant allows users to query medical information in a smarter, more natural way, respecting patient privacy and using secure communications through a chat style interface based on the Matrix decentralized open protocol. Assistant responses are constructed locally by an interchangeable large language model (LLM) that can form rich and complete answers like most humanmedical staff would. Restricted access to patient information and other related resources is provided to the LLM through various methods for it to be able to respond correctly based on specific patient data. The Matrix protocol allows deployments to be run in an open federation; hence, the system can be easily scaled.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok magyar nyelvű folyóiratközlemény külföldi lapban folyóiratcikk eHealth mHealth telehealth telemedicine remote patient monitoring Internet of Things artificial intelligence large language model Matrix open communication protocol
Megjelenés:	Sensors. - 24 : 18 (2024), p. 1-16. -
További szerzők:	Oniga István László (1960-) (villamosmérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM126073
035-os BibID:	(WOS)001311667300001
Első szerző:	Xie, Yu
Cím:	A Comprehensive Review of Hardware Acceleration Techniques and Convolutional Neural Networks for EEG Signals / Yu Xie, Stefan Oniga
Dátum:	2024
ISSN:	1424-8220
Megjegyzések:	This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor imagery, epilepsy detection, and sleep monitoring. Previous reviews on EEG have mainly focused on software solutions. However, these reviews often overlook key challenges associated with hardware implementation, such as scenarios that require a small size, low power, high security, and high accuracy. This paper discusses the challenges and opportunities of hardware acceleration for wearable EEG devices by focusing on these aspects. Specifically, this review classifies EEG signal features into five groups and discusses hardware implementation solutions for each category in detail, providing insights into the most suitable hardware acceleration strategies for various application scenarios. In addition, it explores the complexity of efficient CNN architectures for EEG signals, including techniques such as pruning, quantization, tensor decomposition, knowledge distillation, and neural architecture search. To the best of our knowledge, this is the first systematic review that combines CNN hardware solutions with EEG signal processing. By providing a comprehensive analysis of current challenges and a roadmap for future research, this paper provides a new perspective on the ongoing development of hardware-accelerated EEG systems.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk EEG signal analysis convolutional neural networks feature extraction hardware acceleration
Megjelenés:	Sensors. - 24 : 17 (2024), p. 1-28. -
További szerzők:	Oniga István László (1960-) (villamosmérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM108683
035-os BibID:	(WoS)000941838200001 (Scopus)85148970625
Első szerző:	Xie, Yu
Cím:	Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks / Yu Xie, Stefan Oniga
Dátum:	2023
ISSN:	1424-8220
Megjegyzések:	In brain-computer interface (BCI) systems, motor imagery electroencephalography (MIEEG) signals are commonly used to detect participant intent. Many factors, including low signal-tonoise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time-frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.
Tárgyszavak:	Műszaki tudományok Villamosmérnöki tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk motor imagery (MI) electroencephalogram (EEG) data augmentation (DA) convolutional neural network (CNN)
Megjelenés:	Sensors. - 23 : 4 (2023), p. 1-16. -
További szerzők:	Oniga István László (1960-) (villamosmérnök)
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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