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A visual Brain-Computer Interface (BCI) speller is a system which assists disabled persons with severe neu-romuscular diseases to communicate with the external world. It acquires brain signals in response to visual stimuli shown to the person on a screen, and then analyzes in real-time to predict the desired symbol on a single trial basis. To date most BCI design paradigms have been focused on the...
For a practical intracranial brain computer interface (BCI), minimizing the invasiveness of the electrode implantation is crucial. In this study, we used only one intracranial electrode to implement an online BCI for fast typing. When the subject attended the virtual button containing visual motion stimuli, prominent responses were elicited at the stereo-EEG (SEEG) electrodes within the fMRI defined...
Artifacts such as voluntarily and involuntarily muscle movements are usually seen as a source of noise in EEG signals. In this paper, we see artifacts as a source of information in a signal. For example, eye movements can generate a traceable change in the EEG signals. We use eye movements as an effective marker for direction of movement. We propose two experiments for classification of four eye movement...
Motor imagery (MI) based Brain-Computer Interfaces (BCIs) controlled Functional Electrical Stimulation (FES) can help people with severe neuromuscular impairments to control their limbs by bypassing peripheral nerves and muscle pathways. However, there are still four major limitations with current MI-based BCIs for FES control: 1) They require relatively longer training and the training procedures...
One of the objectives of the control using the human thought is to make useful robotic systems for persons with high dependency (quadriplegics, paraplegics, etc.). When the human subject is not able to move his limbs, upper or lower, he is no longer able to perform basic and necessary tasks in his daily life. Recently, robotic systems have reached a very advanced level. For example, humanoid robots...
Brain-machine interface (BMI) systems collect and classify electroencephalogram (EEG) data to predict the desired command of the user. The P300 EEG signal is passively produced when a user observes or hears a desired stimulus. The P300 can be used with a visual display to allow a BMI user to select commands from an array of selections. The visual stimuli are often repeated and averaged to increase...
We report on a P300 based spatial visual brain-computer interface (BCI) application improvement based on an inter-stimulus-interval (ISI) optimization. The proposed system allows for nine commands' application using a non-invasive electroencephalography (EEG) brainwave monitoring. This paper presents the experiments results obtained by relying entirely on the visual oddball paradigm-based interaction...
Detection of frequency for steady-state visual evoked potentials (SSVEP) is addressed. We propose to use the combination of CCA and training data-based template matching between two level of data adaptive reference signals that can deal with the dominant frequency. On the basis of magnitude of stimulus frequency components, the dominant channels are selected. The recognition accuracy as well as the...
The diagnosis of patients with Disorders Of Consciousness represents a challenge in the clinical routine. Recently, Brain Computer Interfaces based in Electroencephalography (EEG-BCI) have been used to detect signs of consciousness in these patients. This approach allows to discover brain responses to command following. Nevertheless, a reliable BCI strategy must to be able to determine the commands...
We present a Brain Computer Interface (BCI) system in an asynchronous setting that allows classifying objects in their semantic categories (e.g. a hammer is a tool). For training, we use visual cues that are representative of the concepts (e.g. a hammer image for the concept of hammer). We evaluate the system in an offline synchronous setting and in an online asynchronous setting. We consider two...
A novel approach to steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) is presented in the paper. To minimize possible side-effects of the monochromatic light SSVEP-based BCI we propose to utilize chromatic green-blue flicker stimuli in higher, comparing to the traditionally used, frequencies. The developed safer SSVEP responses are processed an classified with features...
Dyslexia is a brain-based reading disability characterized by deficit in phonological processing. In this paper the effect of computer working memory (WM) training on EEG signals of 15 dyslexic children has been investigated. For this purpose, three sets of EEG were recorded from the subjects before and after the computer treatment. Each set of EEG was recorded while the subjects were doing a visual...
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...
This paper presents a novel brain-computer interface (BCI) system aiming at the rehabilitation of attention-deficit/hyperactive disorder in children. It uses the P300 potential in a series of feedback games to improve the subjects' attention. We applied a support vector machine (SVM) using temporal and template-based features to detect these P300 responses. In an experimental setup using five subjects,...
In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based,...
We present an automated solution for the acquisition, processing and classification of electroencephalography (EEG) signals in order to remotely control a remotely located robotic hand executing communicative gestures. The Brain-Computer Interface (BCI) was implemented using the Steady State Visual Evoked Potential (SSVEP) approach, a low-latency and low-noise method for reading multiple non-time-locked...
Users of a brain-computer interface (BCI) learn to co-adapt with the system through the feedback they receive. Particularly in case of motor imagery BCIs, feedback design can play an important role in the course of motor imagery training. In this paper we investigated the effect of biased visual feedback on performance and motor imagery skills of users during BCI control of a pair of humanlike robotic...
Visual search tasks can take long amounts of time and the more complicated the image is the longer it takes to find a target. Therefore, it is of interest to come up with a system that can augment a searcher's vision, in relation to speed, enabling the searcher to find the target faster than through normal means. Audition and vision are important for both communicative and informational purposes and...
This paper presents a brain-computer interface (BCI) that can help users to input phone numbers or select any command in the graphical user interface. The system is based on the steady-state visual evoked potential (SSVEP). To ensure universal applicability, a system with three fixed positioned electrodes for reducing user variation on system performance has been proposed. Sixteen buttons illuminated...
The field of brain computer interfaces has been emerged as a new way to simplify the lives of paralyzed, ‘Locked-in’ people and people with motor disabilities. Modern brain computer interfaces provide an alternative to the natural neuronal pathways that are normally used to convey the brain signals/commands to different body parts. Brain computer interfaces use the captured brain signals to automate...
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