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The ability to acquire Electroencephalogram (EEG) signals from the brain has led to the development of Brain Computer Interfaces (BCI), which capture signals generated by the physical processes in the brain and use them to control external devices. In this paper, we establish an application to control a robot on the Arduino platform by the use of a BCI system, which does not require training for individual...
Brain machine interfaces (BMIs) transform modulation of electroencephalogram (EEG) elicited by cognitive and mental events users voluntarily perform into words and commands in accordance with their intents to communicate with somebody else or machines the users want to control. One of the leading paradigms in BMIs includes a method which utilizes the modulation of a steady state visual evoked potentials...
In this paper, a brain-computer interface (BCI) system based on steady-state visually evoked EEG potentials (SSVEP) has been presented to steer a NAO humanoid robot, in which a novel robot control paradigm with two-layer-interface was designed and implemented. The scene captured from robot camera was divided into different target regions and displayed to the human subjects with extra visual stimulus...
This paper presents the Brain computer interface (BCI) and its current potential for application in devices and process control. BCI is one of emerging options for Human Computer Interface (HCI) allowing more comfortable interaction with devices and processes in the Information and Communication Technology (ICT) area. Current state on the art is described and various available BCI devices are listed...
The aim of this paper was to separate the EEG recordings into cerebral and noncerebral waves and compare a statistical properties of chosen components using the coefficient of excess kurtosis. Noncerebral waves, particularly the ocular artifacts, should be properly identified, because some of them imitate the cerebral potentials. The eye opening and closure, blinks or eye flutter are similar to the...
Mental tasks classification such as motor imagery based on EEG signals is a challenging issue in brain-computer interface (BCI) systems. Automatic classifier tuning seems to be an essential component in real-time BCI systems which makes the interface more reliable and easy to use and may offer the optimum configuration of classifier. This paper investigates the robustness of Least-Square Support Vector...
Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG)...
Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions...
Rapid serial visual presentation (RSVP) tasks, in which participants are presented with a continuous sequence of images in one location, have been used in combination with electroencephalography (EEG) in a variety of Brain-Machine Interface (BMI) applications. The RSVP task is advantageous because it can be performed at a high temporal rate. The rate of the RSVP sequence is controlled by the stimulus...
An electroencephalography (EEG)-based Motor Imagery Brain-Computer Interface (MI-BCI) requires a long setup time if a large number of channels is used, and EEG from noisy or irrelevant channels may adversely affect the classification performance. To address this issue, this paper proposed 2 approaches to systematically select discriminative channels for EEG-based MI-BCI. The proposed Discriminative...
Brain-computer interface (BCI) users can control very complex applications such as multimedia players or even web browsers. Therefore, different biosignal acquisition systems are available to noninvasively measure the electrical activity of the brain, the electroencephalogram (EEG). To make BCIs more practical, hardware and software are nowadays designed more user centered and user friendly. In this...
This paper presents the comparison of sleep-wake classification using electroencephalogram (EEG) and multi-modal data from a wrist wearable sensor. We collected physiological data while participants were in bed: EEG, skin conductance (SC), skin temperature (ST), and acceleration (ACC) data, from 15 college students, computed the features and compared the intra-/inter-subject classification results...
Empirical studies of programming language learnability and usability have thus far depended on indirect measures of human cognitive performance, attempting to capture what is at its essence a purely cognitive exercise through various indicators of comprehension, such as the correctness of coding tasks or the time spent working out the meaning of code and producing acceptable solutions. Understanding...
Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patients experience sudden difficulties in starting or continuing locomotion. It is described by patients as the sensation that their feet are suddenly glued to the ground. This, disturbs their balance, and hence often leads to falls. In this study, directed transfer function (DTF) and partial directed coherence...
Physiological sensor based workload estimation technology provides a real-time means for assessing cognitive workload and has a broad range of applications in cognitive ergonomics, mental health monitoring, etc. In this paper we report a study on detecting changes in workload using multi-modality physiological sensors and a novel feature extraction and classification algorithm. We conducted a cognitive...
The Electroencephalogram (EEG) is a non-invasive technique used in the medical field to record and analyze brain activity. In particular, Brain Machine Interfaces (BMI) create this bridge between brain signals and the external world through prosthesis, visual interfaces and other physical devices. This paper investigates the relation between particular hand movement directions while using a BMI and...
Sleep has been shown to be imperative for the health and well-being of an individual. To design intelligent sleep management tools, such as the music-induce sleep-aid device, automatic detection of sleep onset is critical. In this work, we propose a simple yet accurate method for sleep onset prediction, which merely relies on Electroencephalogram (EEG) signal acquired from a single frontal electrode...
A statistical analysis of the separability of EEG A-phases, with respect to basal activity, is presented in this study. A-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around...
Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification...
Visually stimulated brain-computer interfacing detects which target on a screen a user is gazing at; however, this is also accomplished by tracking gaze points with a camera. These two approaches have been independently investigated and sometimes doubts about BCI with visual stimuli are raised in terms of usability compared to eye tracking interfaces (ETI). This paper answers this question by investigating...
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