P
Panagiotis C. Petrantonakis
Researcher at Foundation for Research & Technology – Hellas
Publications - 39
Citations - 1666
Panagiotis C. Petrantonakis is an academic researcher from Foundation for Research & Technology – Hellas. The author has contributed to research in topics: Feature extraction & Computer science. The author has an hindex of 12, co-authored 35 publications receiving 1355 citations. Previous affiliations of Panagiotis C. Petrantonakis include Information Technology Institute & Aristotle University of Thessaloniki.
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Journal ArticleDOI
Emotion Recognition From EEG Using Higher Order Crossings
TL;DR: A novel emotion evocation and EEG-based feature extraction technique is presented, in which the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation, justifying the efficiency of the proposed approach.
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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.
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Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis
TL;DR: In this article, a novel feature extraction method for a user-independent emotion recognition system, namely, HAF-HOC, from electroencephalograms (EEGs), was provided by applying Genetic Algorithms to the empirical mode decomposition based representation of EEG signals.
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A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEG-Based Emotion Recognition
TL;DR: An extensive classification process was conducted using two feature-vector extraction techniques and a SVM classifier for six different classification scenarios in the valence/arousal space, confirming the efficacy of AsI as an index for the emotion elicitation evaluation.
Journal ArticleDOI
In Vivo Imaging of Dentate Gyrus Mossy Cells in Behaving Mice
Nathan Danielson,Gergely F. Turi,Max Ladow,Spyridon Chavlis,Panagiotis C. Petrantonakis,Panayiota Poirazi,Attila Losonczy +6 more
TL;DR: It is found that mossy cells are significantly more active than dentate granule cells in vivo, exhibit spatial tuning during head-fixed spatial navigation, and undergo robust remapping of their spatial representations in response to contextual manipulation.