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A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear...
The tangent space mapping (TSM) becomes an effective method to implement brain computer interface (BCI) with motor imagery. In this paper, TSM is employed with multiband approach to extract discriminative features from electroencephalogram (EEG) to enhance classification accuracy. The EEG is decomposed into multiple subbands and the sample covariance matrices (SCMs) are then estimated on each of the...
High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
Hybrid Brain-Computer Interfaces (BCI) has shown great promise for neuro-prosthetics and assistive devices in the field of rehabilitation. However, the complexity involved with the system design and time cost for classification of motor tasks is a core problem when we step into clinical applications. To help address this problem, simultaneous measurements of Electroencephalography (EEG) and functional...
Steady State Visual Evoked Potential (SSVEP) has been commonly adopted in Brain Computer Interface (BCI) applications. For wearable BCI applications, several aspects of SSVEP-based BCI systems, such as speed, subject variability, and accurate target detection, are under ongoing research investigations. Up to date, Canonical Correlation Analysis (CCA) has been considered the state-of-the-art feature...
The P300 signal is widely used in brain computer interfaces (BCIs) because of its high recognition accuracy, flexible number of commands and short training time. Mapping P300 signals into control commands, namely, P300 signal processing is the research core of BCIs. Focusing on variability of raw data collected from different electrodes, a multi-sensor weighted support vector machine (msw-SVM) algorithm...
In recent past, Brain Computer Interface (BCI) has emerged as one of the fastest growing technology in the field of science and technology. With the continuous and dedicated efforts by many researchers, application of BCI technology has not only proved significant for disabled but also for healthy individuals. Here we have discussed about one such well known BCI paradigm i.e. P300 speller. The conventional...
The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain–computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize...
Brain-machine interface (BMI) is a system that allows a person to control a device such as a robot arm using only his or her brain activity. This work is aimed at discriminating between left and right imagined hand movements using a Support Vector Machine (SVM) classifier. The main focus here is to search for the best features that efficiently describe the electroencephalogram (EEG) data during such...
Brain Computer Interface (BCI) systems enable subjects affected by neuromuscular disorders to interact with the outside world. A P300 speller uses Event Related Potential (ERP) components, generated in the brain in the presence of a target stimulus, to extract information about the user's intent. Several methods have been proposed for spatial filtering and classification of the P300 components. In...
We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various...
EEG based vowel classification is currently gaining importance for its increasing applications in the next generation mind-driven type-writing. This paper addresses a novel approach to classify the mentally uttered alphabets in a specific three lettered format, where the first and the last letter represent two vowel sounds and the middle is a space, where no character is imagined. Such formatting...
We conduct an emotion recognition study to understand and classify the emotional states of human since the social problems caused by impulsive rage explosions such as the revenge driving, noise floor, and violent crimes. Recently, the emotion recognition technology using electroencephalogram (EEG) has become the foremost consideration of researchers compared with others using voice, facial image,...
This paper presents the result of research epilepsy signals preictal and ictal. Some express type of marks are to probably to know as early possible some special symptoms that a ictal is in improve. Nineteen Electrodes were applied, namely the FP1, FP2, F7, F3, Fz, F4, F8, C3, Cz, C4, T3, T4, T5, T6, P3, P4, Pz, O1 and O2. The acquisition of the values of the statistical quantity datas, namely, the...
An Epilepsy signals classification system is expected to reveal the specific characteristics of the patient's EEG signals. Some representative models of the signals are to open the possibility to detect as early as possible some specific symptoms that a seizure is in progress. The standard Principle Component Analysis followed by the acquisition of the values of the statistical quantities, namely,...
Learning and memory are two related mental processes. EEG is a brain mapping technique, which can record brain states directly and can be used to assess learning and memory recall. In this paper, we will assess the effects of 2D and 3D educational contents on learning and memory recall by analyzing the brain states during recall tasks using EEG signals. 34 subjects learn same 2D and 3D educational...
Automation of Electroencephalogram (EEG) analysis can significantly help the neurologist during the diagnosis of epilepsy. During last few years lot of work has been done in the field of computer assisted analysis to detect an epileptic activity in an EEG. Still there is a significant amount of need to make these computer assisted EEG analysis systems more convenient and informative for a neurologist...
Several researches and methods have been developed in the aim of efficiently detecting abnormalities in Electroencephalogram (EEG) time series. The aim of this work is to detect a real-time Epileptic seizure. We designed an algorithm which decomposes EEG signals of a database, normal and epileptics, by the lifted wavelet transform (LWT), in order to extract the features. To reduce the time allocated...
Identifying artifacts in EEG data produced by the neurons in brain is an important task in EEG signal processing research. These artifacts are corrected before further analyzing. In this work, fast fixed point algorithm for Independent Component Analysis (ICA) is used for removing artifacts in EEG signals and principal component analysis (PCA) tool is used for reducing high dimensional data and spatial...
In this work, two dimensional motions of a robot are controlled using brain computer interface. Motor Imagery signals for different mental activities are recorded using Electroencephalography technique. Recorded Electroencephalogram signals are filtered out for noise reduction and processed. Processed signals are further used to prepare the feature vector to train classifier algorithm. Appropriately...
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