3 citations · 4 across the 10 of their papers we have counts for
11 papers
Deep Convolutional Architectures for EEG Classification: A Comparative Study with Temporal Augmentation and Confidence-Based Voting
Aryan Patodiya, Hubert Cecotti
Electroencephalography (EEG) classification plays a key role in brain-computer interface (BCI) systems, yet it remains challenging due to the low signal-to-noise ratio, temporal va…
Non-Stationarity in Brain-Computer Interfaces: An Analytical Perspective
Hubert Cecotti, Rashmi Mrugank Shah, Raksha Jagadish +1
Non-invasive Brain-Computer Interface (BCI) systems based on electroencephalography (EEG) signals suffer from multiple obstacles to reach a wide adoption in clinical settings for c…
Deep Learning Architectures for Code-Modulated Visual Evoked Potentials Detection
Kiran Nair, Hubert Cecotti
Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (C-VEPs) require highly robust decoding methods to address temporal variability and s…
Towards Effective Deep Neural Network Approach for Multi-Trial P300-based Character Recognition in Brain-Computer Interfaces
Praveen Kumar Shukla, Hubert Cecotti, Yogesh Kumar Meena
Brain-computer interfaces (BCIs) enable direct interaction between users and computers by decoding brain signals. This study addresses the challenges of detecting P300 event-relate…
Post-Training Quantization in Brain-Computer Interfaces based on Event-Related Potential Detection
Hubert Cecotti, Dalvir Dhaliwal, Hardip Singh +1
Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primari…
Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance
Param Rajpura, Hubert Cecotti, Yogesh Kumar Meena
Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are…
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space
Param Rajpura, Hubert Cecotti, Yogesh Kumar Meena
This review paper provides an integrated perspective of Explainable Artificial Intelligence techniques applied to Brain-Computer Interfaces. BCIs use predictive models to interpret…
SymNet: Symmetrical Filters in Convolutional Neural Networks
Gregory Dzhezyan, Hubert Cecotti
Symmetry is present in nature and science. In image processing, kernels for spatial filtering possess some symmetry (e.g. Sobel operators, Gaussian, Laplacian). Convolutional layer…
Rotation Invariant Descriptors for Galaxy Morphological Classification
Hubert Cecotti
The detection of objects that are multi-oriented is a difficult pattern recognition problem. In this paper, we propose to evaluate the performance of different families of descript…
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface
Haider Raza, Dheeraj Rathee, ShangMing Zhou +2
The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challengin…
Toward improving the visual stimulus meaning for increasing the P300 detection
Hubert Cecotti, Bertrand Rivet
The P300 speller is a well known Brain-Computer Interface paradigm that has been used for over two decades. A new P300 speller paradigm (XP300) is proposed. It includes several cha…