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Brain Computer Interface is a reliable communication interface between human brain and external world. It translates human brain electrical activity to useful command by extracting meaningful features from Electroencephalogram signals. In present work, feature extraction techniques and classification methods are proposed for implementation of Brain Computer Interface system. Proposed methodology is...
Epileptic seizure is the abnormal synchronous neuronal activity that occurs in human brain. The early detection of epileptic seizure helps in improving patient's mental health. In this work, an Electroencephalogram based methodology of automated epileptic seizure detection using Flexible Analytical Wavelet Transform is presented. In the proposed methodology, Electroencephalogram signals are decomposed...
Virtual Reality (VR) research is accelerating the development of inexpensive real-time Brain Computer Interface (BCI). Hardware improvements that increase the capability of Virtual Reality displays and Brain Computer wearable sensors have made possible several new software frameworks for developers to use and create applications combining BCI and VR. It also enables multiple sensory pathways for communications...
Electroencephalogram (EEG) contains five rhythms, which provide details about various activities of brain. These rhythms are separated using Hilbert–Huang transform for classification of focal and non-focal EEG signals. For this, the EEG signal is disintegrated into narrow bands intrinsic mode functions (IMFs) using empirical mode decomposition, and analytic representation of IMFs is computed by Hilbert...
Emotion classification and recognition from electroencephalogram (EEG) signals have been studied extensively due to its potential benefits such as entertainment and health care. Concerning classification, various techniques have been developed and applied. Support Vector Machines (SVMs) has been reported as the most used because of its accuracy. Nevertheless, although SVMs has satisfactory performance,...
Human being faces numerous types of neurological disorders. Among them epilepsy is the most frequent after stroke. Several techniques have been developed to identify seizure using EEG signals. The basic contribution of those works can be broadly categorized in three different areas: pre-processing, feature extraction and classification. In this work, we systematically compare different features and...
This paper presents patient-specific epileptic seizure detection approach based on Common Spatial Pattern (CSP) and its variants; Diagonal Loading Common Spatial Pattern (DLCSP), and Tikhonov Regularization Common Spatial Pattern (TRCSP). In this proposed approach, multi-channel scalp Electroencephalogram (sEEG) signals are traced and segmented into overlapping segments for both normal and epileptic...
This paper presents a comparison of Electroencephalogram (EEG) signals classification for Brain Computer-Interfaces (BCI). At present, it is a challenging task to extract the meaningful EEG signal patterns from a large volume of poor quality data and simultaneously with the presence of artifacts noises. Selection of the effective classification technique of the EEG signals at classification stage...
EEG contains immense information about the brain activity which cannot be understood completely by visual inspection. Powerful signal processing algorithms in EEG analysis can greatly assist the physicians and neurologists to extract such hidden information. It has been found that EEG being a time-varying and non-stationary signal, can be analyzed by non-linear methods. In this paper we tried to evaluate...
This paper studies the supervised classification of electroencephalogram (EEG) brain signals to identify persons and their activities. The brain signals are obtained from a commercially available and modestly priced wearable headband. Such wearable devices generate a large amount of data and due to their attractive pricing structure are becoming increasingly commonplace. As a result, the data generated...
It has been established that it is possible to reveal human emotions using electroencephalogram (EEG) signals. Most studies used a wide variety of data sets and methods, therefore a comparison between the performances of their approaches is difficult. This paper reports a study on the effects of the number of electrode channels and frequency bands for emotion classification based on a database for...
In electroencephalography (EEG)-based brain computer interfaces (BCI) one way to extract discriminative command-related patterns is to use common spatial pattern (CSP). Speech-imagery BCIs on the other hand, are a more recent type of BCIs in which imagination of a vowel, sound or word is detected from EEG measurements. Since the performance of CSP highly depends on the spectral filters which are previously...
Brain-Computer Interface (BCI) is a very essential and useful communication tool between the human brain and external devices. Effective and accurate classification of Electroencephalography (EEG) signals is important in performance of BCI systems. In this paper, a mental task classification approach based on sparse representation is proposed. A dictionary is used for classification, which is the...
Performance of any brain computer interface system depends upon features of electroencephalogram signals. Electroencephalogram signals undergo for unpredictable changes when vigilance state of human brain alters widely. This may cause adverse changes in extracted features and affect classification performance of brain computer interface system. To avoid miss-classification, brain computer interface...
Epilepsy is a common neurological disorder which is difficult to treat because of its unpredictable and recurrent nature. The electroencephalogram (EEG) is a valuable tool for detecting epileptic seizures. With the aim of reducing the input feature dimensionality, a single median based feature called interquartile range (IQR) was used in this paper for the classification of normal and seizure EEG...
The ability to acquire Electroencephalogram (EEG) signals from the brain has led to the development of Brain Computer Interfaces (BCI), which capture signals generated by the physical processes in the brain and use them to control external devices. In this paper, we establish an application to control a robot on the Arduino platform by the use of a BCI system, which does not require training for individual...
Brain activities are often investigated through Electroencephalographic (EEG) data analysis using timedomain Independent Component Analysis (ICA). Nevertheless, the instantaneous mixing model of ICA cannot properly describe spatio-temporal dynamics, such as those related to traveling waves of neural activity. In this work, we exploit the application of the Complex ICA (cICA) to describe the underlying...
EEG (Electroencephalograph) is a technique for identifying neurological disorders. Epilepsy is a disorder of the central nervous system characterized by loss of consciousness and convulsions. EEG is the recording of electrical activity of the brain signals that can be used to diagnose the epilepsy condition. Epilepsy occurs irregularly and unpredictably manner due to temporary electrical disturbance...
This paper described a research project conducted to recognize to finding the relationship between EEG signals and Human emotions. EEG signals are used to classify three kinds of emotions, positive, neuter and negative. Firstly, literature research has been performed to establish a suitable approach for emotion recognition. Secondly, we extracted features from original EEG data using 4-order wavelet...
Many studies have reported the usefulness of motor imagery (MI) electroencephalogram (EEG) signals for Brain Computer Interface (BCI) systems. MI has been broadly characterized by the average of event-related changes of brain activity at specific frequency bands; but, temporal features of EEG have rarely been considered to identify different mental states of BCIs' users. Additionally, complex classification...
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