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Brain-computer interface (BCI) has been used as a communication tool to enable paralyzed people to interact with the world. Its application has been extended to other non-medical areas like self-regulation, marketing, games and entertainment. Conventionally, BCI largely relies on the visual perception channel to provide users with cues or stimuli for the generation of appropriate brain signals that...
Motor imagery (MI) based on brain computer interfaces (BCIs) have been widely applied for upper limb motor rehabilitation. Due to the fact that a large number of disabled people need to restore or improve walking ability, it is also important to investigate the use of MI-based BCIs for lower limb motor rehabilitation. The brain activity of lower limb MI is more difficult to detect because of low reliability...
Tripolar concentric ring electrode (TCRE) sensors have unique properties. These sensors have been used to acquire various bio-signals such as: electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG). Compared to conventional disc electrode signals TCRE EEG (tEEG) has four times better signal-to-noise ratio, eleven times better mutual information and spatial resolution....
Brain-Computer Interface (BCI) applications are gaining popularity as Electroencephalography (EEG) hardware becomes more accessible. BCI technology is used for various purposes such as neurophysiological evaluation, device control, user-state monitoring, and cognitive improvement. Although BCI software platforms exist, there are few systems designed to assist novice programmers with creating BCI applications...
In this paper, we present an efficient approach to investigate data of EEG-based Brain-Machine Interface (BMI) using a bagging Support Vector Machines (SVMs) for collected data classification from a P3-speller paradigm. The combination of SVMs allows to handle the problem of EEG data variability between the different sessions of the acquisition process. This variability is caused by temporal non-stationarity...
Cognition involves dynamic reconfiguration of functional brain networks at sub-second time scale. A precise tracking of these reconfigurations to categorize visual objects remains elusive. Here, we use dense electroencephalography (EEG) data recorded during naming meaningful (tools, animals…) and scrambled objects from 20 healthy subjects. We combine technique for identifying functional brain networks...
In this study, the effect of the luminance level on the emotional evaluation was investigated using P300 waves observed in electroencephalography (EEG) records. EEG records of 31 helathy individuals were used in this study. The power graphs of records were examined and statistical analysis of the features obtained these power values was performed. With the findings, it was tried to find out how the...
This paper presents a new method for video preference estimation using functional near-infrared spectroscopy signals (fNIRS signals). The proposed method first computes fNIRS features from fNIRS signals recorded while users are watching videos and multiple visual features from these videos. Next, by applying Locality Preserving Canonical Correlation Analysis to fNIRS features and each visual feature,...
In this preliminary study, we explore the brain activity of healthy subjects during visual sustained attention task using a low-cost electroencephalography headset. The main objective is to study the ability of subjects to detect and respond to stimulus changes which occur infrequently during the time using Emotiv EPOC system. 14-Channels EEG data were recorded while subjects participated in 10 minutes...
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 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...
Patients with disorders of consciousness (DOC) cannot reply to questions or clinical assessments using voluntary motor control, and therefore it is very difficult to assess their cognitive capabilities and conscious awareness. Patients who are locked-in (LIS) are instead fully conscious, and they can communicate with their preserved eye movements. However, when the residual oculomotor activity is...
The present work proposes a neurofeedback training system for the induction of an attention state aided by audiovisual stimuli on an experimental group of nine junior high school individuals between twelve and fifteen years old. A control group of 10 individuals with the same characteristics as the experimental group is defined as well to validate the training's efficiency. The auditory stimulation...
This paper makes the subjects' sight locked in a certain area using an eye tracker, getting Steady-state visual evoked potential (SSVEP) from flickering stimuli with a fixed frequency but at random positions, in order to observe the impact of stimulus at different positions and their distances on the electroencephalogram (EEG). The result suggests that if human have to select the positions of stimuli...
Biometrie recognition of persons are widely explored nowadays to develop robust and trustworthy security systems. On account of the unique neural signature of each person, the brain activity recorded by Electroencephalogram (EEG) has recently been identified as a potential biometric trait. In this paper, we propose an online EEG-based biometric system which utilizes the activations of brain towards...
The process through which children learn about the world and develop perceptual, cognitive and motor skills relies heavily on object exploration in their physical world. New types of assistive technology that enable children with impairments to interact with their environment have emerged in recent years, and they could be beneficial for children's cognitive and perceptual skills development. Many...
Electroencephalography (EEG) is the most frequently used technique to monitor functional activity of the brain. It has been widely employed in brain-computer interfaces based on the detection of P300 potentials. However, the P300 waves often contain physiological and non-physiological artifacts such as steady state visually evoked potential, power line or environment noise. The aim of this work is...
Recent advancements in generative adversarial networks (GANs), using deep convolutional models, have supported the development of image generation techniques able to reach satisfactory levels of realism. Further improvements have been proposed to condition GANs to generate images matching a specific object category or a short text description. In this work, we build on the latter class of approaches...
Afternoon sleepiness is a routine physiological phenomenon and it reduces arousal level, performance, and so on. It has been cleared and is often said that taking a short nap is effective to cancel the sleepiness. Sleep stage 2 is one of important factors on sleeping especially in short naps. Also, sleep spindles are important hallmarks of EEG in sleep stage 2. Therefore, it is necessary to find a...
In this paper we propose a biometric recognition system based on steady-state visual evoked potentials (SSVEPs), exploiting brain signals elicited by repetitive stimuli having a constant frequency as identifiers. EEG responses to SSVEP stimuli flickering at different frequencies are recorded, and both mel-frequency cepstral coefficients (MFCCs) and autoregressive (AR) reflection coefficients are used...
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