dr. Luca Longo at Technological University Dublin - Research Interests, Publications & Academic Profile

dr. Luca Longo

lecturer

Technological University Dublin

Ireland

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Research Interests

Cognitive Neuroscience

80%

Computational Neuroscience

100%

Eeg

100%

Neuroscience

70%

Neuroimaging

40%

Psychophysics

30%

Neuroinformatics

30%

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Articles19

Interpreting the latent space of a Convolutional Variational Autoencoder for semi-automated eye blink artefact detection in EEG signals

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Year:

2025

Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios

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Year:

2024

A Novel Integration of Data-Driven Rule Generation and Computational Argumentation for Enhanced Explainable AI

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Year:

2024

Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

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Year:

2024

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer’s Disease with Electroencephalography in Resting-State Adults

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Year:

2024

A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners

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Year:

2024

Comparing and extending the use of defeasible argumentation with quantitative data in real-world contexts

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Year:

2023

Interpreting Disentangled Representations of Person-Specific Convolutional Variational Autoencoders of Spatially Preserving EEG Topographic Maps via Clustering and Visual Plausibility

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Year:

2023

How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review

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Year:

2023

Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices

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Year:

2023

Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanations

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Year:

2023

Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach

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Year:

2023

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning

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Year:

2022

Human Mental Workload: A Survey and a Novel Inclusive Definition.

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Year:

2022

An Evaluation of the EEG Alpha-to-Theta and Theta-to-Alpha Band Ratios as Indexes of Mental Workload.

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Year:

2022

On the Minimal Amount of EEG Data Required for Learning Distinctive Human Features for Task-Dependent Biometric Applications.

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Year:

2022

Evaluating instructional designs with mental workload assessments in university classrooms

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Year:

2022

Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

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Year:

2022

On the Dimensionality and Utility of Convolutional Autoencoder’s Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps

Article Link

Year:

2022

Collaborators5

M. Atif Qureshi

Technological University Dublin

IRELAND

Roberto Prevete

Assistant Professor

Università degli Studi di Napoli Federico II

ITALY

Damiano Verda

Head

ITALY

Bujar Raufi

Technological University Dublin

IRELAND

Lucas Rizzo

Assistant Lecturer

Technological University Dublin

IRELAND

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