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Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure
Saravanakumar Duraisamy;Mateusz Dubiel;Maurice Rekrut;Luis A. Leiva
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Brain-Computer Interfaces (BCIs) can decode imagined speech from neural activity. However, these systems typically require extensive training sessions where participants imaginedly repeat words, leading to mental fatigue and difficulties identifying the onset of words, especially when imagining sequences of words. This paper addresses these challenges by transferring a classifier trained in overt ...Show More
Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure
Saravanakumar Duraisamy;Mateusz Dubiel;Maurice Rekrut;Luis A. Leiva
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Cross-Subject Startle Detection for Pilots on the Flight Deck Using Physiological Signals
Ganavi Basavaraju;Tobias Jungbluth;Maurice Rekrut;Florian Daiber;Antonio Krüger
In aviation, detecting startle responses in pilots is crucial for understanding their reactions in safety-critical situations. Startle events trigger sudden physiological and cognitive reactions, which affect decision-making and pose a potential risk in causing accidents. For the safety of passengers and crew, accurate startle detection models for pilots need to be developed. Due to the physiologi...Show More
Imagined Speech is a Brain Computer Interface (BCI) paradigm that aims at detecting imagined words or sentences from brain activity. This paradigm is particularly useful in contexts where a user may not be able to speak or there is environmental noise impairing the use of audio-based automated speech recognition. However, Imagined Speech BCIs are typically not very robust which may be caused by cu...Show More
Comparing the Effects of Dry, Water and Gel-Based Electrodes on EEG-Based Overt Speech Classification
2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Recent developments in EEG based overt speech recognition have shown that speech recorded with an EEG can be classified well, however there have yet to be actual applications developed for it. This is most likely due to the EEG setup being unintuitive to the layperson. The Gel-based electrodes used in most literature are both hard and time consuming to setup. To move towards a more user friendly a...Show More
Investigating Noticeable Hand Redirection in Virtual Reality using Physiological and Interaction Data
Martin Feick;Kora P. Regitz;Anthony Tang;Tobias Jungbluth;Maurice Rekrut;Antonio Krüger
2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Hand redirection is effective so long as the introduced offsets are not noticeably disruptive to users. In this work we investigate the use of physiological and interaction data to detect movement discrepancies between a user's real and virtual hand, pushing towards a novel approach to identify discrepancies which are too large and therefore can be noticed. We ran a study with 22 participants, col...Show More
Neurosurgery requires medical professionals to adjust operational microscopes according to constantly changing conditions during surgery. These adjustments are currently performed manually by the surgeon which comes with risks for the patient and a hands-free solution for this adjustment is desirable. Within this work we present the design, implementation and a first evaluation of a concept for ha...Show More
Classifying Words in Natural Reading Tasks Based on EEG Activity to Improve Silent Speech BCI Training in a Transfer Approach
Maurice Rekrut;Andreas Fey;Matthias Nadig;Johannes Ihl;Tobias Jungbluth;Antonio Krüger
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Silent speech Brain-Computer Interfaces (BCIs) classify imagined words from brain activity. Those BCIs require a tremendous amount of training data collected during tedious sessions in which the user continuously silently repeats certain words. In order to overcome those cumbersome training sessions, this work proposes a transfer learning approach in which we try to classify words silently read em...Show More
Improving Silent Speech BCI Training Procedures Through Transfer from Overt to Silent Speech
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Silent speech Brain-Computer Interfaces (BCIs) try to decode imagined speech from brain activity. Those BCIs require a tremendous amount of training data usually collected during mentally and physically exhausting sessions in which participants silently repeat words presented on a screen for several hours. Within this work we present an approach to overcome those exhausting sessions by training a ...Show More
Decoding Semantic Categories from EEG Activity in Silent Speech Imagination Tasks
Maurice Rekrut;Mansi Sharma;Matthias Schmitt;Jan Alexandersson;Antonio Krüger
2021 9th International Winter Conference on Brain-Computer Interface (BCI)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
Silent Speech Brain-Computer Interfaces try to decode imagined or silently spoken speech from brain activity. This technology holds big potential in various application domains, e.g. restoring communication abilities for handicapped people, or in settings where overtly spoken speech is not an option due to environmental conditions, e.g. noisy industrial or aerospace settings. However, one major dr...Show More
Decoding Semantic Categories from EEG Activity in Object-Based Decision Tasks
Maurice Rekrut;Mansi Sharma;Matthias Schmitt;Jan Alexandersson;Antonio Krüger
2020 8th International Winter Conference on Brain-Computer Interface (BCI)
Year: 2020 | Conference Paper |
Cited by: Papers (5)
This study explores the potential of decoding semantic categories from EEG-activity for the use in Silent Speech Brain-Computer Interfaces (BCI). We used object-based decision tasks to evoke conscious semantic processing for five different semantic categories in the participants cerebral cortical structures and implemented different feature extraction and classification methods to evaluate possibl...Show More
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