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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...
Unmanned aerial vehicles (UAVs) have been applied for both civilian and military applications; scientific research involving UAVs has encompassed a wide range of scientific study. However, communication with unmanned vehicles are subject to attack and compromise. Such attacks have been reported as early as 2009, when a Predator UAV's video stream was compromised. Since UAVs extensively utilize autonomous...
Automated mental workload measurement is particularly important in safety-critical settings, such as in nuclear plants, aviation, air traffic control, shipping, and transportation, to name a few. As an example, recent statistics have suggested that 90% of the accidents in the transport industry are due to human factors. In this paper, we explore the potential of off-the-shelf wearable technologies...
Recent advances in brain-computer interface (BCI) technologies have shown the feasibility of neural decoding for both users' gait intent and continuous kinematics. However, the cortical adaptation and the dynamics of cortical involvement in human upright walking with a closed-loop BCI in virtual environment (VE) have yet to be demonstrated. To address explore this possibility, we designed a closed-loop...
With the growing volume and complexity of air traffic, air traffic controllers (ATCOs) encounter heavier burden nowadays. Therefore, human factors study in air traffic control (ATC) is increasingly essential, paving the way to a safer air transportation system. In this paper, we conducted an ATC experiment, where Electroencephalogram (EEG) data were collected throughout the experiment. Compared to...
Long monitoring tasks without regular actions, are becoming increasingly common from aircraft pilots to train conductors as these systems grow more automated. These task contexts are challenging for the human operator because they require inputs at irregular and highly interspaced moments even though these actions are often critical. It has been shown that such conditions lead to divided and distracted...
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...
Motor imagery (Ml)-based brain-computer interface (BCI) allows users to control external devices using the brain signal patterns induced by the imagination of movements. Since these patterns have high variability between subjects and sessions, the BCI system necessarily requires 20–30 minutes for the calibration process each time the system is used. This time-consuming process requires a high level...
Error-related electroencephalographic (EEG) potentials (ErrPs) have been explored to improve the reliability of modern Brain-Computer Interfaces (BCIs), thanks to the information they carry about user awareness of erroneous responses. ErrPs detection on a single-trial basis has been successfully demonstrated, and proved to effectively enhance human-computer interaction and BCI performance. Previous...
This study investigates the neural features of locomotion mode transitions (i.e., level-ground walking to stair ascent) from non-invasive electroencephalography (EEG) signals. A systematic EEG processing method was implemented to reduce artifacts. Source localization using independent component analysis and k-mean clustering algorithm revealed the involvement of four clusters in the brain (Left and...
A relevant issue of neuro-interfacing wearable robots in rehabilitation is the necessity to have training data, since the collection of sufficient data from patients within a reasonable recording time is not always possible. However, the use of historic data (e.g., session-to-session transfer, subject-to-subject transfer) can often lead to a reduction in classification performance which is affected...
Brain-Computer Interfaces (BCI), which are capable of operating external devices using human brain activity, have recently been actively studied. Previous studies have developed an auditory BCI using event-related potential (P300) for estimating the single target of a sound source direction to which the subjects pay attention. In this study, we proposed a new paradigm using two targets instead of...
This study present an intervention combining an electroencephalography-based brain computer interface with a hybrid robotic system for the modulation of the cortical excitability (plasticity). Plasticity is intended to be elicited through the association of the voluntary motor-related cortical processes with the hybrid assistance during the execution of reaching movement. The cortical excitability...
Lack of forearm muscles in transhumerai amputees is one of the major issues when it comes to control of upper-limb prosthetic arms using only EMG signals. Brain signal is one of the alternative input signals that can be explored to recognize the motion intention of the users and subsequently can be used to control the upper-limb prosthetic hands. This paper proposes a transhumeral prosthetic arm which...
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...
To identify the attended speaker from single-trial EEG recordings in an acoustic scenario with two competing speakers, an auditory attention decoding (AAD) method has recently been proposed. The AAD method requires the clean speech signals of both the attended and the unattended speaker as reference signals for decoding. However, in practice only the binaural signals, containing several undesired...
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,...
Phase synchronies are often used to study relationships between different parts of the brain and to identify regions that interact in a coordinated manner for a certain task. In this paper, we propose a wavelet reconstruction and phase-locking-based feature extraction method to visualize and classify the direction-specific phase synchronies between Electroencephalogram (EEG) channel-pairs for hand...
Single-trial classification of event-related potentials in electroencephalogram (EEG) signals is a very important paradigm of brain-computer interface (BCI). Because of individual differences, usually some subject-specific calibration data are required to tailor the classifier for each subject. Transfer learning has been extensively used to reduce such calibration data requirement, by making use of...
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