2,463 research outputs found
Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges
In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices
EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs
A hybrid brain-computer interface based on motor intention and visual working memory
Non-invasive electroencephalography (EEG) based brain-computer interface (BCI) is able to provide alternative means for people with disabilities to communicate with and control over external assistive devices. A hybrid BCI is designed and developed for following two types of system (control and monitor).
Our first goal is to create a signal decoding strategy that allows people with limited motor control to have more command over potential prosthetic devices. Eight healthy subjects were recruited to perform visual cues directed reaching tasks. Eye and motion artifacts were identified and removed to ensure that the subjects\u27 visual fixation to the target locations would have little or no impact on the final result. We applied a Fisher Linear Discriminate (FLD) analysis for single-trial classification of the EEG to decode the intended arm movement in the left, right, and forward directions (before the onsets of actual movements). The mean EEG signal amplitude near the PPC region 271-310 ms after visual stimulation was found to be the dominant feature for best classification results. A signal scaling factor developed was found to improve the classification accuracy from 60.11% to 93.91% in the two-class (left versus right) scenario. This result demonstrated great promises for BCI neuroprosthetics applications, as motor intention decoding can be served as a prelude to the classification of imagined motor movement to assist in motor disable rehabilitation, such as prosthetic limb or wheelchair control.
The second goal is to develop the adaptive training for patients with low visual working memory (VWM) capacity to improve cognitive abilities and healthy individuals who seek to enhance their intellectual performance. VWM plays a critical role in preserving and processing information. It is associated with attention, perception and reasoning, and its capacity can be used as a predictor of cognitive abilities. Recent evidence has suggested that with training, one can enhance the VWM capacity and attention over time. Not only can these studies reveal the characteristics of VWM load and the influences of training, they may also provide effective rehabilitative means for patients with low VWM capacity. However, few studies have investigated VWM over a long period of time, beyond 5-weeks.
In this study, a combined behavioral approach and EEG was used to investigate VWM load, gain, and transfer. The results reveal that VWM capacity is directly correlated to the reaction time and contralateral delay amplitude (CDA). The approximate magic number 4 was observed through the event-related potentials (ERPs) waveforms, where the average capacity is 2.8-item from 15 participants. In addition, the findings indicate that VWM capacity can be improved through adaptive training. Furthermore, after training exercises, participants from the training group are able to improve their performance accuracies dramatically compared to the control group. Adaptive training gains on non-trained tasks can also be observed at 12 weeks after training.
Therefore, we conclude that all participants can benefit from training gains, and augmented VWM capacity can be sustained over a long period of time. Our results suggest that this form of training can significantly improve cognitive function and may be useful for enhancing the user performance on neuroprosthetics device
Data-driven multivariate and multiscale methods for brain computer interface
This thesis focuses on the development of data-driven multivariate and multiscale methods
for brain computer interface (BCI) systems. The electroencephalogram (EEG), the
most convenient means to measure neurophysiological activity due to its noninvasive nature,
is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its
multichannel recording nature require a new set of data-driven multivariate techniques to
estimate more accurately features for enhanced BCI operation. Also, a long term goal
is to enable an alternative EEG recording strategy for achieving long-term and portable
monitoring.
Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully
data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary
EEG signal into a set of components which are highly localised in time and frequency. It
is shown that the complex and multivariate extensions of EMD, which can exploit common
oscillatory modes within multivariate (multichannel) data, can be used to accurately
estimate and compare the amplitude and phase information among multiple sources, a
key for the feature extraction of BCI system. A complex extension of local mean decomposition
is also introduced and its operation is illustrated on two channel neuronal
spike streams. Common spatial pattern (CSP), a standard feature extraction technique
for BCI application, is also extended to complex domain using the augmented complex
statistics. Depending on the circularity/noncircularity of a complex signal, one of the
complex CSP algorithms can be chosen to produce the best classification performance
between two different EEG classes.
Using these complex and multivariate algorithms, two cognitive brain studies are
investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user
attention to a sound source among a mixture of sound stimuli, which is aimed at improving
the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments
elicited by taste and taste recall are examined to determine the pleasure and displeasure
of a food for the implementation of affective computing. The separation between two
emotional responses is examined using real and complex-valued common spatial pattern
methods.
Finally, we introduce a novel approach to brain monitoring based on EEG recordings
from within the ear canal, embedded on a custom made hearing aid earplug. The new
platform promises the possibility of both short- and long-term continuous use for standard
brain monitoring and interfacing applications
Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review
Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed
Network-based brain computer interfaces: principles and applications
Brain-computer interfaces (BCIs) make possible to interact with the external
environment by decoding the mental intention of individuals. BCIs can therefore
be used to address basic neuroscience questions but also to unlock a variety of
applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In
general, BCI usability critically depends on the ability to comprehensively
characterize brain functioning and correctly identify the user s mental state.
To this end, much of the efforts have focused on improving the classification
algorithms taking into account localized brain activities as input features.
Despite considerable improvement BCI performance is still unstable and, as a
matter of fact, current features represent oversimplified descriptors of brain
functioning. In the last decade, growing evidence has shown that the brain
works as a networked system composed of multiple specialized and spatially
distributed areas that dynamically integrate information. While more complex,
looking at how remote brain regions functionally interact represents a grounded
alternative to better describe brain functioning. Thanks to recent advances in
network science, i.e. a modern field that draws on graph theory, statistical
mechanics, data mining and inferential modelling, scientists have now powerful
means to characterize complex brain networks derived from neuroimaging data.
Notably, summary features can be extracted from these networks to
quantitatively measure specific organizational properties across a variety of
topological scales. In this topical review, we aim to provide the
state-of-the-art supporting the development of a network theoretic approach as
a promising tool for understanding BCIs and improve usability
Enhancement of Robot-Assisted Rehabilitation Outcomes of Post-Stroke Patients Using Movement-Related Cortical Potential
Post-stroke rehabilitation is essential for stroke survivors to help them regain independence and to improve their quality of life. Among various rehabilitation strategies, robot-assisted rehabilitation is an efficient method that is utilized more and more in clinical practice for motor recovery of post-stroke patients. However, excessive assistance from robotic devices during rehabilitation sessions can make patients perform motor training passively with minimal outcome. Towards the development of an efficient rehabilitation strategy, it is necessary to ensure the active participation of subjects during training sessions. This thesis uses the Electroencephalography (EEG) signal to extract the Movement-Related Cortical Potential (MRCP) pattern to be used as an indicator of the active engagement of stroke patients during rehabilitation training sessions. The MRCP pattern is also utilized in designing an adaptive rehabilitation training strategy that maximizes patients’ engagement.
This project focuses on the hand motor recovery of post-stroke patients using the AMADEO rehabilitation device (Tyromotion GmbH, Austria). AMADEO is specifically developed for patients with fingers and hand motor deficits.
The variations in brain activity are analyzed by extracting the MRCP pattern from the acquired EEG data during training sessions. Whereas, physical improvement in hand motor abilities is determined by two methods. One is clinical tests namely Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) which include FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements’ tests. The other method is the measurement of hand-kinematic parameters using the AMADEO assessment tool which contains hand strength measurements during flexion (force-flexion), and extension (force-extension), and Hand Range of Movement (HROM)
Using brain-computer interaction and multimodal virtual-reality for augmenting stroke neurorehabilitation
Every year millions of people suffer from stroke resulting to initial paralysis,
slow motor recovery and chronic conditions that require continuous reha
bilitation and therapy. The increasing socio-economical and psychological
impact of stroke makes it necessary to find new approaches to minimize its
sequels, as well as novel tools for effective, low cost and personalized reha
bilitation. The integration of current ICT approaches and Virtual Reality
(VR) training (based on exercise therapies) has shown significant improve
ments. Moreover, recent studies have shown that through mental practice
and neurofeedback the task performance is improved. To date, detailed in
formation on which neurofeedback strategies lead to successful functional
recovery is not available while very little is known about how to optimally
utilize neurofeedback paradigms in stroke rehabilitation. Based on the cur
rent limitations, the target of this project is to investigate and develop a
novel upper-limb rehabilitation system with the use of novel ICT technolo
gies including Brain-Computer Interfaces (BCI’s), and VR systems. Here,
through a set of studies, we illustrate the design of the RehabNet frame
work and its focus on integrative motor and cognitive therapy based on VR
scenarios. Moreover, we broadened the inclusion criteria for low mobility pa
tients, through the development of neurofeedback tools with the utilization
of Brain-Computer Interfaces while investigating the effects of a brain-to-VR
interaction.Todos os anos, milho˜es de pessoas sofrem de AVC, resultando em paral
isia inicial, recupera¸ca˜o motora lenta e condic¸˜oes cr´onicas que requerem re
abilita¸ca˜o e terapia cont´ınuas. O impacto socioecon´omico e psicol´ogico do
AVC torna premente encontrar novas abordagens para minimizar as seque
las decorrentes, bem como desenvolver ferramentas de reabilita¸ca˜o, efetivas,
de baixo custo e personalizadas. A integra¸c˜ao das atuais abordagens das
Tecnologias da Informa¸ca˜o e da Comunica¸ca˜o (TIC) e treino com Realidade
Virtual (RV), com base em terapias por exerc´ıcios, tem mostrado melhorias
significativas. Estudos recentes mostram, ainda, que a performance nas tare
fas ´e melhorada atrav´es da pra´tica mental e do neurofeedback. At´e a` data,
na˜o existem informac¸˜oes detalhadas sobre quais as estrat´egias de neurofeed
back que levam a uma recupera¸ca˜o funcional bem-sucedida. De igual modo,
pouco se sabe acerca de como utilizar, de forma otimizada, o paradigma de
neurofeedback na recupera¸c˜ao de AVC. Face a tal, o objetivo deste projeto ´e
investigar e desenvolver um novo sistema de reabilita¸ca˜o de membros supe
riores, recorrendo ao uso de novas TIC, incluindo sistemas como a Interface
C´erebro-Computador (ICC) e RV. Atrav´es de um conjunto de estudos, ilus
tramos o design do framework RehabNet e o seu foco numa terapia motora
e cognitiva, integrativa, baseada em cen´arios de RV. Adicionalmente, ampli
amos os crit´erios de inclus˜ao para pacientes com baixa mobilidade, atrav´es do
desenvolvimento de ferramentas de neurofeedback com a utilizac¸˜ao de ICC,
ao mesmo que investigando os efeitos de uma interac¸˜ao c´erebro-para-RV
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Developing robust movement decoders for local field potentials
textBrain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms.Electrical and Computer Engineerin
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