12,735 research outputs found
The Impact of Flow in an EEG-based Brain Computer Interface
Major issues in Brain Computer Interfaces (BCIs) include low usability and
poor user performance. This paper tackles them by ensuring the users to be in a
state of immersion, control and motivation, called state of flow. Indeed, in
various disciplines, being in the state of flow was shown to improve
performances and learning. Hence, we intended to draw BCI users in a flow state
to improve both their subjective experience and their performances. In a Motor
Imagery BCI game, we manipulated flow in two ways: 1) by adapting the task
difficulty and 2) by using background music. Results showed that the difficulty
adaptation induced a higher flow state, however music had no effect. There was
a positive correlation between subjective flow scores and offline performance,
although the flow factors had no effect (adaptation) or negative effect (music)
on online performance. Overall, favouring the flow state seems a promising
approach for enhancing users' satisfaction, although its complexity requires
more thorough investigations
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
Evaluation on flow discharge of grassed swale in lowland area
Grassed swale is an open vegetated channel designed specifically in attenuating stormwater runoff to decrease the velocity, to reduce the peak flows, and minimize the causes of flood. Therefore, the fundamental of this study is to evaluate the flow discharge of swale in Universiti Tun Hussein Onn Malaysia (UTHM), which has flat land surface area. There are two sites of study were involved to assess the performance of swale as stormwater quantity control, named as swale 1 and swale 2. Data collection was conducted on 100 meters of length for each swale. The velocity of swale was measured thrice by using a current meter according to the six-tenths depth method, after a rainfall event. The discharge of drainage area in UTHM was determined by the Rational Method (Qpeak), and the discharge of swales (Qswale) was evaluated by the Mean-Section Method. Manning’s roughness coefficient and the infiltration rate were also determined in order to describe the characteristics of swale, which contributing factors for the effectiveness of swale. The results shown that Qswale is greater than Qpeak at swale 1 and swale 2, which according to the Second Edition of MSMA, the swales are efficient as stormwater quantity control in preventing flash flood at the campus area of UTHM
Games and Brain-Computer Interfaces: The State of the Art
BCI gaming is a very young field; most games are proof-of-concepts. Work that compares BCIs in a game environments with traditional BCIs indicates no negative effects, or even a positive effect of the rich visual environments on the performance. The low transfer-rate of current games poses a problem for control of a game. This is often solved by changing the goal of the game. Multi-modal input with BCI forms an promising solution, as does assigning more meaningful functionality to BCI control
Magnetoencephalography in Stroke Recovery and Rehabilitation
Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables
people to communicate with the outside world by interpreting the EEG signals of
their brains to interact with devices such as wheelchairs and intelligent
robots. More specifically, motor imagery EEG (MI-EEG), which reflects a
subjects active intent, is attracting increasing attention for a variety of BCI
applications. Accurate classification of MI-EEG signals while essential for
effective operation of BCI systems, is challenging due to the significant noise
inherent in the signals and the lack of informative correlation between the
signals and brain activities. In this paper, we propose a novel deep neural
network based learning framework that affords perceptive insights into the
relationship between the MI-EEG data and brain activities. We design a joint
convolutional recurrent neural network that simultaneously learns robust
high-level feature presentations through low-dimensional dense embeddings from
raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various
artifacts such as background activities. The proposed approach has been
evaluated extensively on a large- scale public MI-EEG dataset and a limited but
easy-to-deploy dataset collected in our lab. The results show that our approach
outperforms a series of baselines and the competitive state-of-the- art
methods, yielding a classification accuracy of 95.53%. The applicability of our
proposed approach is further demonstrated with a practical BCI system for
typing.Comment: 10 page
Electroencephalography (EEG)-Derived Markers to Measure Components of Attention Processing
Although extensively studied for decades,
attention system remains an interesting challenge in
neuroscience field. The Attention Network Task (ANT)
has been developed to provide a measure of the
efficiency for the three attention components identified
in the Posner’s theoretical model: alerting, orienting and
executive control. Here we propose a study on 15 healthy
subjects who performed the ANT. We combined
advanced methods for connectivity estimation on
electroencephalographic (EEG) signals and graph theory
with the aim to identify neuro-physiological indices
describing the most important features of the three
networks correlated with behavioral performances. Our
results provided a set of band-specific connectivity
indices able to follow the behavioral task performances
among subjects for each attention component as defined
in the ANT paradigm. Extracted EEG-based indices
could be employed in future clinical applications to
support the behavioral assessment or to evaluate the
influence of specific attention deficits on Brain Computer
Interface (BCI) performance and/or the effects of BCI
training in cognitive rehabilitation applications
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