3 citations · 4 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…