Konstantinos BarmpasImperial College London | Imperial · Department of Computing
Konstantinos Barmpas
Master of Engineering
About
7
Publications
527
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Introduction
Currently, working as a Machine Learning Engineer at Cogitat, a London-based startup that uses AI to read brain waves and to convert them into digital commands.
Ph.D. Candidate in Machine Learning at Imperial College London under the supervision of Prof. Stefanos Zafeiriou. My research interest lies in the intersection of Machine Learning and Brain-Computer Interfaces (BCIs).
Education
October 2020 - October 2024
September 2019 - September 2020
October 2016 - September 2020
Publications
Publications (7)
Machine learning models have opened up enormous opportunities in the field of Brain-Computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting. Mixing causal reasoning, identifying causal relationships between variables of int...
Personalized electroencephalogram (EEG) decoders hold a distinct preference in healthcare applications, especially in the context of Motor-Imagery (MI) Brain-Computer Interfaces (BCIs), owing to their inherent capability to effectively tackle inter-subject variability. This study introduces a novel subject selection framework that blends ideas from...
Brain-computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one's neural activity, measured by electroencephalography (EEG) signals. In recent years, Convolutional Neural Networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However...
Deep Convolutional Neural Networks (CNNs) have recently demonstrated impressive results in electroencephalogram (EEG) decoding for several Brain-Computer Interface (BCI) paradigms, including Motor-Imagery (MI). However, neurophysiological processes underpinning EEG signals vary across subjects causing covariate shifts in data distributions and henc...
In this work, we employ causal reasoning to breakdown and analyze important challenges of the decoding of Motor-Imagery (MI) electroencephalography (EEG) signals. Furthermore, we present a framework consisting of dynamic convolu-tions, that address one of the issues that arises through this causal investigation, namely the subject distribution shif...
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because current methods cannot generalise well across human subjects' data and handle learning from different heterogeneously collected data sets, thus limiting the scale of...
Building subject-independent deep learning models for EEG decoding faces the challenge of strong covariate-shift across different datasets, subjects and recording sessions. Our approach to address this difficulty is to explicitly align feature distributions at various layers of the deep learning model, using both simple statistical techniques as we...