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Experiment design has a key role in the functional magnetic resonance imaging (fMRI) data analyses. Block designs are suitable to localize functional areas but are not able to measure the transient changes in the brain activity. Event related design is a better approach and saves time and resources like single trial analyses. In this study, we explored the event related design with single, and multi...
The work addresses classification of EEG signals into seizure and non-seizure by applying EMD and SVM with proposal of new feature Root Mean Square (RMS) frequency and feature using Hilbert marginal spectrum which overcomes the drawback of feature instantaneous bandwidth. We have success in achieving the consistency with the new features which shows classification average accuracy of 97.72% and highest...
The advantage of using collaborative brain-computer interfaces in improving human response in visual target recognition tests was investigated. We used a public EEG dataset created from recordings made using a 32-channel EEG system by Delorme et al. (2004) to compare the classification accuracy using one, two, and three EEG signal sets from different subjects. Fourteen participants performed a go/no-go...
Emotion recognition is an integral part of affective computing. An affective brain-computer-interface (BCI) can benefit the user in a number of applications. In most existing studies, EEG (electroencephalograph)-based emotion recognition is explored in a classificatory manner. In this manner, human emotions are discretized by a set of emotion labels. However, human emotions are more of a continuous...
Our cognitive abilities can help us communicate without any visible action or words. Brain Computer Interfaces (BCI) achieves this communication using the brain waves. But for practical applications, system using BCI must be fast and accurate. In this paper, we present a method that uses SVM classifiers over ensemble of averaged data. Averaging data over number of trails removes the random noise and...
Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off between accuracy and computational time. This paper presents a very fast and accurate method to classify asynchronous brain signals from a multi-class mental tasks dataset using time-domain features. Five different statistical time-domain features were extracted to characterize various properties of three...
Stress is a mental condition that can effects the brain electrical activity to be different from the normal state. This brain cognitive change can be measured using EEG. The objective of this paper is to classify stress subjects based on EEG signal using SVM. The data which are used to represent stress subjects were taken from the residents of Pusat Darul Wardah; a shelter centre for troubled women...
Classification of finger movement related to (electrocorticography) ECoG records is the main purpose of this study. Data set IV presented in BCI Competition IV was used in this study. This data set contains brain signals from three epileptic subjects and the data records consist of both ECoG and electronic glove data. ECoG segments related finger movements were extracted by means of finger movement...
Motor imagery based brain-computer interface (BCI) translates subject's motor intention into a control signal through electroencephalogram (EEG) pattern classification. In this paper, a large margin nearest neighbor (LMNN) method is applied for the classification of multi-class BCI based on motor imagery. The main idea of LMNN is to learn a Mahalanobis distance that tries to collapse examples in the...
In this study, a Common Spatial Pattern (CSP) driven Artificial Neural Network (ANN) Classification strategy is presented to classify the mental tasks, namely, left-hand movement imagination, right-hand movement imagination, and word generation in EEG data. According to this strategy, first, electrode re-referencing and band-pass filtering are used to enhance the EEG signal. Then a multi-class extension...
The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Here we employ machine learning techniques to detect driver drowsiness. The system obtained 98% performance in predicting driver drowsiness. This is the highest prediction rate reported to date for detecting...
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