S
Spiros Nikolopoulos
Researcher at Information Technology Institute
Publications - 133
Citations - 1608
Spiros Nikolopoulos is an academic researcher from Information Technology Institute. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 14, co-authored 119 publications receiving 1055 citations. Previous affiliations of Spiros Nikolopoulos include University of London & Queen Mary University of London.
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Journal ArticleDOI
Deep Learning Advances in Computer Vision with 3D Data: A Survey
TL;DR: It is concluded that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation, therefore, larger-scale datasets and increased resolutions are required.
Journal ArticleDOI
EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century.
Ioulietta Lazarou,Spiros Nikolopoulos,Panagiotis C. Petrantonakis,Ioannis Kompatsiaris,Magda Tsolaki +4 more
TL;DR: This work reviews the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation and focuses on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials.
Journal ArticleDOI
IoT Wearable Sensors and Devices in Elderly Care: A Literature Review.
Thanos G. Stavropoulos,Asterios Papastergiou,Lampros Mpaltadoros,Spiros Nikolopoulos,Ioannis Kompatsiaris +4 more
TL;DR: The current state-of-the-art, as well as trends and effective practices for the future of effective, accessible, and acceptable eldercare with technology are outlined.
Proceedings ArticleDOI
A Comparison Study on EEG Signal Processing Techniques Using Motor Imagery EEG Data
Vangelis P. Oikonomou,Kostas Georgiadis,George Liaros,Spiros Nikolopoulos,Ioannis Kompatsiaris +4 more
TL;DR: A comparison between Common Spatial Patterns (CSP) related features and features based on Power Spectral Density (PSD) techniques confirms that PSD features demonstrate the most consistent robustness and effectiveness in extracting patterns for accurately discriminating between left and right MI tasks.
Posted ContentDOI
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
Vangelis P. Oikonomou,Georgios Liaros,Kostantinos Georgiadis,Elisavet Chatzilari,Katerina Adam,Spiros Nikolopoulos,Ioannis Kompatsiaris +6 more
TL;DR: A state-of-the-art baseline for SSVEP-based BCIs is made available for the community that can be used as a basis for introducing novel methods and approaches for brain-computer interfaces.