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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...
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...
Along with its clinical applications, EEG signals can be used in the biometric authentication domain based on its inimitable characteristics and uniqueness. Analyzed EEG signals are applied in many research studies as a communication trail between the human brain and the computer using BCI technology. In this paper, brain responses were triggered with visual stimulation of ten normal subjects. Biometric...
The purpose of the study is to observe the effect of the level of luminance on the emotional evaluation (positive, negative, neutral) in electroencephalography (EEG) records. EEG records of 31 healthy volunteers were used in the study. Features were obtained from these records by using Principle Component Analysis (PCA) method. As a result of the analysis of the features, it was observed that the...
Error-Related Potentials (ErrPs) have been used lately in order to improve several existing Brain-Computer Interface (BCI) applications. In our study we investigate the contribution of ErrPs in a Steady State Visual Evoked Potential (SSVEP) based BCI. An extensive study is presented in order to discover the limitations of the proposed scheme. Using Common Spatial Patterns and Random Forests we manage...
Dual tasking refers to the simultaneous execution of two tasks with different demands. In this study, we aimed to investigate the effect of a second task on a main task of motor execution and on the ability to detect the cortical potential related to the main task from non-invasive electroencephalographic (EEG). Participants were asked to perform a series of cue-based ankle dorsiflexions as the primary...
Working memory processing is central for higher-order cognitive functions. Although the ability to access and extract working memory load has been proven feasible, the temporal resolution is low and cross-task generalization is poor. In this study, EEG oscillatory activity was recorded from sixteen healthy subjects while they performed two versions of the visual n-back task. Observed effects in the...
In this paper we present a P300-hased Brain Computer Interface (BCI) for the remote control of a mechatronic actuator, such as wheelchair, or even a car, driven by EEG signals to be used hy tetraplegic and paralytic users or just for safe drive in case of car. The P300 signal, an Evoked Related Potential (ERP) devoted to the cognitive brain activity, is induced for purpose by visual stimulation. The...
It remains unclear whether brain networks are altered during conversion blindness in electroencephalogram (EEG) representation, which is of significance both on improving clinical management of conversion blindness, and providing objective evidence for judicial disputes. Functional brain network was constructed on coherence extracted from scalp EEGs, and conventional network metrics were analyzed...
Authentication is a crucial consideration when securing data or any kind of information system. Though existing approaches for authentication are user-friendly, they have vulnerabilities such as the possibility & criminally threatening a user. We propose a novel approach which uses Electroencephalogram (EEG) brain signals for an authentication process. Unique features of EEG data for distinguishing...
Canonical correlation analysis (CCA) has been successfully used for extracting frequency components of steady-state visual evoked potential (SSVEP) in electroencephalography (EEG). Recently, a few efforts on CCA-based SSVEP methods have been made to demonstrate the benefits for brain computer interface (BCI). Most of these methods are limited to linear CCA. In this paper consider a deep extension...
Motor imagery, one of the first investigated neural process for Brain-Computer Interfaces (BCIs) still provides a great challenge nowadays. Aiming a better and more accurate control, multiple researches have been conducted by the scientific community. Nevertheless, there is still no robust and confident application developed. In order to augment the potential referring to motor imagery, and to attract...
Brain-computer interfaces (BCI) provide means of communications and control, in assistive technology, which do not require motor activity from the user. The goal of this study is to promote classification of two types of imaginary movements, left and right hands, in an EMOTIV cap based system, using the Naïve Bayes classifier. A preliminary analysis with respect to results obtained by other experiments...
Attention is the primary cognitive process to induce a response to a stimulus. Maintaining the attentive state continuously for a prolonged period of time is known as sustained attention which is vital for performing any task. The present study aims at evaluating the activation of different brain regions while performing an attention requiring task. A standard attention task called the Visual Continuous...
The canonical correlation analysis (CCA), double-partial least-squares (DPLS) methods and least absolute shrinkage and selection operator (LASSO) have been proven effectively in detecting the steady-state visual evoked potential (SSVEP) in SSVEP-based brain-computer interface systems. However, the accuracy of SSVEP classification can be affected by phase shifts of the electroencephalography data,...
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...
The Electroencephalography (EEG) signal contains information about a person's brain activity including the Event-Related Potential (ERP) — an evoked response to a task-related stimulus. EEG is contaminated by artefacts that degrade ERP classification performance. Independent Component Analysis (ICA) is normally employed to decompose EEG into independent components (ICs) associated to artefact and...
Electroencephalography (EEG) has long been used for Brain computer interface (BCI). Recent researches have proved that EEG can be also used to classify data generated in speech imagery. This classification can further be utilized to develop speech prosthesis and synthetic telepathy systems. In this paper we wanted to check whether features extracted from beta, delta and theta rhythms of EEG can be...
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 offers a brief overview of the tutorial presentation to be offered at the International Conference on Computer, Communication, Control and Information Technology (C3IT'2015) by the first author. It covers foundations to applications of the Brain-Computer Interfacing research undertaken by a research group at Artificial Intelligence and Brain Imaging Laboratory of Jadavpur University, located...
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