2,319 research outputs found
A large-scale evaluation framework for EEG deep learning architectures
EEG is the most common signal source for noninvasive BCI applications. For
such applications, the EEG signal needs to be decoded and translated into
appropriate actions. A recently emerging EEG decoding approach is deep learning
with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many
different architectures already published. Here we present a novel framework
for the large-scale evaluation of different deep-learning architectures on
different EEG datasets. This framework comprises (i) a collection of EEG
datasets currently including 100 examples (recording sessions) from six
different classification problems, (ii) a collection of different EEG decoding
algorithms, and (iii) a wrapper linking the decoders to the data as well as
handling structured documentation of all settings and (hyper-) parameters and
statistics, designed to ensure transparency and reproducibility. As an
applications example we used our framework by comparing three publicly
available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow
ConvNet, and two versions of EEGNet. We also show how our framework can be used
to study similarities and differences in the performance of different decoding
methods across tasks. We argue that the deep learning EEG framework as
described here could help to tap the full potential of deep learning for BCI
applications.Comment: 7 pages, 3 figures, final version accepted for presentation at IEEE
SMC 2018 conferenc
Speech Processes for Brain-Computer Interfaces
Speech interfaces have become widely used and are integrated in many applications and devices. However, speech interfaces require the user to produce intelligible speech, which might be hindered by loud environments, concern to bother bystanders or the general in- ability to produce speech due to disabilities. Decoding a usera s imagined speech instead of actual speech would solve this problem. Such a Brain-Computer Interface (BCI) based on imagined speech would enable fast and natural communication without the need to actually speak out loud. These interfaces could provide a voice to otherwise mute people. This dissertation investigates BCIs based on speech processes using functional Near In- frared Spectroscopy (fNIRS) and Electrocorticography (ECoG), two brain activity imaging modalities on opposing ends of an invasiveness scale. Brain activity data have low signal- to-noise ratio and complex spatio-temporal and spectral coherence. To analyze these data, techniques from the areas of machine learning, neuroscience and Automatic Speech Recog- nition are combined in this dissertation to facilitate robust classification of detailed speech processes while simultaneously illustrating the underlying neural processes. fNIRS is an imaging modality based on cerebral blood flow. It only requires affordable hardware and can be set up within minutes in a day-to-day environment. Therefore, it is ideally suited for convenient user interfaces. However, the hemodynamic processes measured by fNIRS are slow in nature and the technology therefore offers poor temporal resolution. We investigate speech in fNIRS and demonstrate classification of speech processes for BCIs based on fNIRS. ECoG provides ideal signal properties by invasively measuring electrical potentials artifact- free directly on the brain surface. High spatial resolution and temporal resolution down to millisecond sampling provide localized information with accurate enough timing to capture the fast process underlying speech production. This dissertation presents the Brain-to- Text system, which harnesses automatic speech recognition technology to decode a textual representation of continuous speech from ECoG. This could allow to compose messages or to issue commands through a BCI. While the decoding of a textual representation is unparalleled for device control and typing, direct communication is even more natural if the full expressive power of speech - including emphasis and prosody - could be provided. For this purpose, a second system is presented, which directly synthesizes neural signals into audible speech, which could enable conversation with friends and family through a BCI. Up to now, both systems, the Brain-to-Text and synthesis system are operating on audibly produced speech. To bridge the gap to the final frontier of neural prostheses based on imagined speech processes, we investigate the differences between audibly produced and imagined speech and present first results towards BCI from imagined speech processes. This dissertation demonstrates the usage of speech processes as a paradigm for BCI for the first time. Speech processes offer a fast and natural interaction paradigm which will help patients and healthy users alike to communicate with computers and with friends and family efficiently through BCIs
Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI
There are significant milestones in modern human's civilization in which
mankind stepped into a different level of life with a new spectrum of
possibilities and comfort. From fire-lighting technology and wheeled wagons to
writing, electricity and the Internet, each one changed our lives dramatically.
In this paper, we take a deep look into the invasive Brain Machine Interface
(BMI), an ambitious and cutting-edge technology which has the potential to be
another important milestone in human civilization. Not only beneficial for
patients with severe medical conditions, the invasive BMI technology can
significantly impact different technologies and almost every aspect of human's
life. We review the biological and engineering concepts that underpin the
implementation of BMI applications. There are various essential techniques that
are necessary for making invasive BMI applications a reality. We review these
through providing an analysis of (i) possible applications of invasive BMI
technology, (ii) the methods and devices for detecting and decoding brain
signals, as well as (iii) possible options for stimulating signals into human's
brain. Finally, we discuss the challenges and opportunities of invasive BMI for
further development in the area.Comment: 51 pages, 14 figures, review articl
Inner speech recognition through electroencephalographic signals
This work focuses on inner speech recognition starting from EEG signals.
Inner speech recognition is defined as the internalized process in which the
person thinks in pure meanings, generally associated with an auditory imagery
of own inner "voice". The decoding of the EEG into text should be understood as
the classification of a limited number of words (commands) or the presence of
phonemes (units of sound that make up words). Speech-related BCIs provide
effective vocal communication strategies for controlling devices through speech
commands interpreted from brain signals, improving the quality of life of
people who have lost the capability to speak, by restoring communication with
their environment. Two public inner speech datasets are analysed. Using this
data, some classification models are studied and implemented starting from
basic methods such as Support Vector Machines, to ensemble methods such as the
eXtreme Gradient Boosting classifier up to the use of neural networks such as
Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory
(BiLSTM). With the LSTM and BiLSTM models, generally not used in the literature
of inner speech recognition, results in line with or superior to those present
in the stateof-the-art are obtained.Comment: Submitted to the Italian Workshop on Artificial Intelligence for
Human Machine Interaction (AIxHMI 2022), December 02, 2022, Udine, Ital
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