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We investigate the neural correlates of visual working memory using electroencephalography (EEG). Our objective is to develop a cognitive Brain-Computer Interface (BCI) able to monitor visual working memory load in real-time. A system with these properties would eventually have different applications, such as training, rehabilitation, or safety while operating dangerous machinery. The BCI performances...
Emblematic gesture pictures were presented to subjects as probes in relation to semantically congruent and incongruent sentences to investigate if there is a similar cognitive processing network for congruity as there is with words. Subjects had to perform a simple discrimination task while undergoing EEG recordings. The ERPs elicited by semantically incongruent gestures produced larger N400 and possibly...
This paper demonstrates a high performance brain-computer interface (BCI) that allows users to dial phone numbers. The system is based on Canonical Correlation Analysis (CCA) and Steady-State Visual Evoked Potential (SSVEP). Through six buttons (9Hz, 10Hz, 11Hz, 12Hz, 13 Hz, 14Hz) displayed on the screen, subjects can choose the number by gazing at the computer interface. This proposed EEG (Electroencephalography)...
In this paper, a novel method for detecting steadystate visual evoked potentials (SSVEP) using multiple channel electroencephalogram (EEG) data is presented. Accurate asynchronous detection, high speed and high information transfer rate can be achieved after a short calibration session. Spatial filtering based on the Canonical Corelation Analysis method proposed in [1] is used for identifying optimal...
This paper presents a novel image classification method based on integration of EEG and visual features. In the proposed method, we obtain classification results by separately using EEG and visual features. Furthermore, we merge the above classification results based on a kernelized version of Supervised learning from multiple experts and obtain the final classification result. In order to generate...
This paper presents a brain-computer interface (BCI) in which the face paradigm was optimized for the visual mismatch negativity (MMN). There were 12 cells in a LCD monitor. A single letter was at the bottom of each cell. In the new paradigm, a color face appeared above each of the 12 cells randomly while the gray faces appeared in others 11 cells. A traditional face paradigm with single character...
We aim to develop a brain-machine interface (BMI) system that estimates user's gaze or attention on an object to pick it up in the real world. In Experiment 1 and 2 we measured steady-state visual evoked potential (SSVEP) using luminance and/or contrast modulated flickers of photographic scenes presented on a head-mounted display (HMD). We applied multiclass SVM to estimate gaze locations for every...
EEG brainwaves have recently emerged as a promising biometric that can be used for individual identification, since those signals are confidential, sensitive, and hard to steal and replicate. In this study, we propose a new stimulidriven, non-volitional brain responses based framework towards individual identification. The non-volitional mechanism provides an even more secure way in which the subjects...
Although human cognition often occurs while moving, most studies of the dynamics of the human brain examine subjects while static and seated in a highly controlled laboratory. EEG signals have been considered to be too noisy to record brain dynamics during human locomotion. Here, we present a real-time ambulatory brain computer interface which allows us to detect gait phases and remove motion-related...
This paper aims to investigate the effects of steady-state visual evoked potential (SSVEP) in the aspects of viewing distance variation for a smart TV control system. We designed an experimental environments with different viewing distance. Four healthy people (age 27.5±1, male) participated in the experiment. Four visual stimuli with a round shape were designed and presented on LED monitor flickering...
An electroencephalographic (EEG) waveform could be denoted by a series of ordinal patterns called motifs which are based on the ranking values of subsequence time series. Permutation entropy (PE) has been developed to describe the relative occurrence of each of these motifs. However, PE has few limitations, mainly its inability to differentiate between distinct patterns of a certain motif, and its...
This paper aims to improve tactile and bone-conduction brain computer interface (tbaBCI) classification accuracy based on a new stimulus pattern search in order to trigger more separable P300 responses. We propose and investigate three approaches to stimulus spatial and frequency content modification. As result of the online tbaBCI classification accuracy tests with six subjects we conclude that frequency...
A brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is one of the most practical BCI, because of high recognition accuracies and short time training. Phase of SSVEPs can be potentially applicable for generating device commands. However, the effective method of estimating the phase of SSVEPs has not yet been established, especially, in the case of using multi-channel...
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...
This paper reports on a project aiming to confirm whether a tactile stimulator "touch-sense glove" is effective for a novel brain-computer interface (BCI) paradigm and whether the tactile stimulus delivered to the fingers could be utilized to evoke event related potential (ERP) responses with possible attentional modulation. The tactile ERPs are expected to improve the BCI accuracy. The...
Steady-state visual evoked potentials (SSVEPs) enable brain-computer interfaces to achieve efficient performance in command detection accuracy and information transfer rate (ITR). However, a limited bandwidth of SSVEPs causes a limited number of possible command in BCIs. Moreover since the amplitude of SSVEP at a particular frequency depends on users, some BCI commands could be executed easily (higher...
Brain-computer interface (BCI) is currently developed as an alternative technology with a potential to restore lost motor function in patients with neurological injuries. In this paper, we describe an integrated system of a non-invasive electroencephalogram (EEG)-based BCI with a non-invasive functional electrical stimulation (FES). This system enables the direct brain control of upper limbs to achieve...
Brain Computer Interface(BCI) systems provide an additional way for people to interact with external environment without using peripheral nerves or muscles[1]. In a variety of BCI systems, a BCI system based on the steady-state visual evoked potentials (SSVEP) is one most common system known for application, because of its ease of use and good performance with little user training. In this study,...
Non-invasive Brain-Computer Interface (BCI) has appeared as a new hope for a large population of disabled people, who were waiting for a new communication means that would translate some brain responses into actions. After several decades of research in fields such as neuroscience and machine learning, the performance remains too low due to the low signal to noise ratio of the EEG signal, and the...
This study emphases on the alpha oscillation of Electroencephalography (EEG) signal of normal children ability towards working memory performance and visual responsive. The assessments were conducted on 30 children aged between 7 to 9 years old who have no records of working memory disability. The raw EEG signals were decomposed using discrete wavelet transform with mother wavelet: Daubechies 4 (db4)...
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