Silent speech interfaces for speech restoration: A review
JA Gonzalez-Lopez, A Gomez-Alanis… - IEEE …, 2020 - ieeexplore.ieee.org
This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-
acoustic biosignals generated by the human body during speech production to enable …
acoustic biosignals generated by the human body during speech production to enable …
Biosignal sensors and deep learning-based speech recognition: A review
Voice is one of the essential mechanisms for communicating and expressing one's
intentions as a human being. There are several causes of voice inability, including disease …
intentions as a human being. There are several causes of voice inability, including disease …
A neural speech decoding framework leveraging deep learning and speech synthesis
Decoding human speech from neural signals is essential for brain–computer interface (BCI)
technologies that aim to restore speech in populations with neurological deficits. However, it …
technologies that aim to restore speech in populations with neurological deficits. However, it …
Speech synthesis from ECoG using densely connected 3D convolutional neural networks
Objective. Direct synthesis of speech from neural signals could provide a fast and natural
way of communication to people with neurological diseases. Invasively-measured brain …
way of communication to people with neurological diseases. Invasively-measured brain …
Thinking out loud, an open-access EEG-based BCI dataset for inner speech recognition
Surface electroencephalography is a standard and noninvasive way to measure electrical
brain activity. Recent advances in artificial intelligence led to significant improvements in the …
brain activity. Recent advances in artificial intelligence led to significant improvements in the …
The potential of stereotactic-EEG for brain-computer interfaces: current progress and future directions
Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes
to measure electrophysiological brain activity. It is most commonly used in the identification …
to measure electrophysiological brain activity. It is most commonly used in the identification …
Bio-signals in medical applications and challenges using artificial intelligence
Artificial Intelligence (AI) has broadly connected the medical field at various levels of
diagnosis based on the congruous data generated. Different types of bio-signal can be used …
diagnosis based on the congruous data generated. Different types of bio-signal can be used …
EEG-transformer: Self-attention from transformer architecture for decoding EEG of imagined speech
YE Lee, SH Lee - 2022 10th International winter conference on …, 2022 - ieeexplore.ieee.org
Transformers are groundbreaking architectures that have changed a flow of deep learning,
and many high-performance models are developing based on transformer architectures …
and many high-performance models are developing based on transformer architectures …
EEG classification of covert speech using regularized neural networks
Communication using brain-computer interfaces (BCIs) can be non-intuitive, often requiring
the performance of a conversation-irrelevant task such as hand motor imagery. In this paper …
the performance of a conversation-irrelevant task such as hand motor imagery. In this paper …
Towards an EEG-based intuitive BCI communication system using imagined speech and visual imagery
Communication using brain-computer interface (BCI) has developed in attempts toward an
intuitive system by decoding the imagined speech or visual imagery. However …
intuitive system by decoding the imagined speech or visual imagery. However …