5 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…