Decoding covert speech from EEG-a comprehensive review
JT Panachakel, AG Ramakrishnan - Frontiers in Neuroscience, 2021 - frontiersin.org
Over the past decade, many researchers have come up with different implementations of
systems for decoding covert or imagined speech from EEG (electroencephalogram). They …
systems for decoding covert or imagined speech from EEG (electroencephalogram). They …
A state-of-the-art review of EEG-based imagined speech decoding
Currently, the most used method to measure brain activity under a non-invasive procedure is
the electroencephalogram (EEG). This is because of its high temporal resolution, ease of …
the electroencephalogram (EEG). This is because of its high temporal resolution, ease of …
Data augmentation for motor imagery signal classification based on a hybrid neural network
As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery
(MI) has been widely used in the fields of neurological rehabilitation and robot control …
(MI) has been widely used in the fields of neurological rehabilitation and robot control …
Decoding imagined and spoken phrases from non-invasive neural (MEG) signals
Speech production is a hierarchical mechanism involving the synchronization of the brain
and the oral articulators, where the intention of linguistic concepts is transformed into …
and the oral articulators, where the intention of linguistic concepts is transformed into …
Evaluation of hyperparameter optimization in machine and deep learning methods for decoding imagined speech EEG
Classification of electroencephalography (EEG) signals corresponding to imagined speech
production is important for the development of a direct-speech brain–computer interface (DS …
production is important for the development of a direct-speech brain–computer interface (DS …
Data augmentation: Using channel-level recombination to improve classification performance for motor imagery EEG
Y Pei, Z Luo, Y Yan, H Yan, J Jiang, W Li… - Frontiers in Human …, 2021 - frontiersin.org
The quality and quantity of training data are crucial to the performance of a deep-learning-
based brain-computer interface (BCI) system. However, it is not practical to record EEG data …
based brain-computer interface (BCI) system. However, it is not practical to record EEG data …
Transfer learning with time series data: a systematic mapping study
Transfer Learning is a well-studied concept in machine learning, that relaxes the assumption
that training and testing data need to be drawn from the same distribution. Recent success in …
that training and testing data need to be drawn from the same distribution. Recent success in …
Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network
Objective. Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community
to find evidence of cortical involvement at walking initiation and during locomotion. However …
to find evidence of cortical involvement at walking initiation and during locomotion. However …
Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review
Introduction The use of machine learning (ML) techniques in healthcare encompasses an
emerging concept that envisages vast contributions to the tackling of rare diseases. In this …
emerging concept that envisages vast contributions to the tackling of rare diseases. In this …
Deep-learning-based BCI for automatic imagined speech recognition using SPWVD
The electroencephalogram (EEG)-based brain–computer interface (BCI) has potential
applications in neuroscience and rehabilitation. It benefits a person with neurological …
applications in neuroscience and rehabilitation. It benefits a person with neurological …