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Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction...
The utility to decode hand movement parameters is significant to the control of artificial limb in the BCI fields. Most previous studies have adopted amplitude features of the low-frequency EEG signals to decode hand movement parameters. In this study, we have investigated the instantaneous phase of the low-frequency EEG signals attained by Hilbert transform for such a task for the first time, and...
In this study, we investigate whether sparse coding helps explain the semantic representation in human cerebral cortex. We show this by using sparse coding to model semantic representation in the cerebral cortex. We propose three methods for estimating semantic representation from brain activity data. For estimating a new semantic representation, in the first method, we use only a semantic representation...
Independent mobility is important for the self-esteem and well-being of people with mobility impairments. For people with severe disabilities, there is a body of research investigating how best to share control of motion between a person with disabilities and a "smart wheelchair". Traditionally in "shared control", the control law is a linear combination of the human's intended...
We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Specifically, we show that BCI technology can be used to learn personalized decoding models that relate the global configuration of brain rhythms in individual subjects (as measured by EEG) to their motor performance...
The use of electroencephalogram (EEG) data is common to develop brain-computer interface (BCI) applications. Analysis of EEG data in the oddball paradigm has revealed that some electrodes experience clearer manifestations of the P300 wave, giving a particular relevance to their position. For this study, we arrange recorded EEG data as a single trial 3D representation in which spatial and temporal...
We present here deep covariance learning models for predicting drivers' drowsy and alert states from Electroencephalography (EEG). Three types of deep covariance learning models are proposed: SPDNet, CNN, and DNN on covariance matrices. Our test results show that all the deep covariance learning methods reported better performance than shallow learning methods including Riemannian methods and STCNN,...
In this paper, a mind controlled multi-task manipulator based on motor imagery electroencephalogram (EEG) is proposed. Describe the system function first: In the case of only two types of control signal, the implementation of multi-task Manipulator relies on a toggle-confirmation mode of operation: the task is switched when imagining the left-hand movement, and the task is confirmed when the right-hand...
The control performance and safety of current brain-controlled mobile robots are limited. To address this problem, in this paper, we design an assistive controller based on the model predictive control method. The proposed controller fuses tracking user intention and guaranteeing safety of brain-controlled mobile robots into an optimization problem. In this way, the proposed controller can make users...
In some of the EEG-based recognition tasks, for example, EEG-based emotion recognition (EEG-ER), enhancing feature extractors is difficult. In such cases, the use of deep neural networks which are capable of classification and recognition by the input of raw data is desirable. Therefore, effective components and models of neural networks for EEG-based recognition must be proposed. In addition, the...
Motor imagery based brain computer interface (BCI) has drawback of long subject dependent calibration session times. This can be a very exhausting and a time consuming process. In order to alleviate it, transfer learning and active learning approaches can be utilised. Informative instances are selected by applying active learning concept from other subjects under similar circumstances. Then, they...
Estimation of intracranial sources, using inverse solutions methods, has been proposed as a mean to improve performance in non-invasive brain-computer interfaces. These methods estimate the activity of a large number of neural sources from a smaller number of scalp electroencephalography (EEG) channels. This is a highly undetermined problem and regularisation constraints need to be applied. In this...
This paper applies a recurrent higher-order neural network for sliding-mode pinning control of complex networks for achieving trajectory tracking. This control strategy does not require having the same coupling strength for all node connections on the network. The tracking effectiveness and dynamical behavior of the controlled network is illustrated via simulations.
Brain-computer interfaces (BCIs) can provide an alternative means of communication for individuals with severe neuromuscular limitations. The P300-based BCI speller relies on eliciting and detecting transient event-related potentials (ERPs) in electroencephalography (EEG) data, in response to a user attending to rarely occurring target stimuli amongst a series of non-target stimuli. However, in most...
Key element protection of combat system is a challenging problem in modern combat. Effectively evaluating the key elements would be of great help in force deployment in crisis situations. This paper proposes a new evaluation method that combines expert evaluation, PCA (Principal Component Analysis) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). Specifically, we first...
Recently, noninvasive brain stimulation is gaining significant attention in stroke rehabilitation. In this paper, we investigate the effects of transcranial direct current stimulation (tDCS) on the motor-imagery brain-computer interface (MI-BCI) performance of stroke patients. To this end, we processed the EEG data collected from a randomized control trial (RCT) study of 19 stroke patients grouped...
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