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Support vector machines (SVM) have become a gold standard method for the classification of brain signals. However, for highly nonlinear and non-stationary signals like Electroencephalography (EEG), conventional SVM is not sufficient to classify the different brain states associated with different cognitive activity. Brain state classification is a challenging task when using standard SVM. Thus, a...
Recent advancements in brain computer-interfacing (BCI) and neuro-robotics have played an indispensable role for people suffering from neural injuries to expect better quality of life by restoring sensory functions and replacement of neuro-muscular pathways as BCI systems work on imagination of movements to control prosthetic limbs. In this research, multiple combinations of features and classifiers...
The paper describes the application of different wavelet analysis together with machine learning algorithm for the recognition of English alphabet from EEG signal. Decision making was executed in two stages. At first important features such as maximum, minimum, delta value, moment, kurtosis, skew, median, mean and standard deviation at different sub-bands are computed using the wavelet functions —...
Brain Computer Interface (BCI) system converts thoughts into commands for driving external device with Electroencephalography (EEG). This paper presents the use of decimation filters for filtering the EEG signal. Common Spatial Pattern (CSP) technique is used to transform the filtered signal to a new time series in order to have optimal variance for the discrimination of different tasks. Fishers Discriminant...
P300, which is usually evoked by visual stimulus, is widely used in electroencephalography (EEG) based brain computer interface (BCI) studies. As an application-oriented BCI study, the P300 speller would inevitably be used in outdoor environments. However, the visual stimulation effect might be interfered by the reflections in outdoor environment. This paper attempted to improve the outdoor P300 speller...
Classification of EEG under wearable environment faces many challenges including motion artifact, electrode DC offset, noise and limited available energy source. This paper describes the design consideration of a multi-channel machine-learning based EEG classification and recording processors for wearable form-factor sensors. The goal is to optimize the detection performance while balancing the analog...
The main principle behind EEG-based brain computer interfaces (BCI) is the recording and accurate classification of EEG signals during imagination of different types of motor movements. The changes in the neural activity effected by motor imagery are a lot similar to those induced by actual movement. Common features, e.g., band power values, present in the single EEG trials are extracted by suitable...
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...
This paper introduces a new method based on the Singular Values of EEG signals for the detection of epileptic seizures. Singular Value Decomposition was performed on an EEG signal in epochs of 8 seconds and Singular Values were extracted from each epoch. These singular values were fed into Support Vector Machine (SVM) for a binary classification between epileptic seizure and non- seizure events. Singular...
Many brain disorders are diagnosed by analysing the EEG signals. EEG refers to the recording of the brain's spontaneous electrical activity over a short period of time. In this paper an efficient approach for detecting the presence of epileptic seizures in EEG signals is presented. Epilepsy is a disease due to temporary alternation in brain functions due to abnormal electrical activity of a group...
Single trial ERP detection is critical for stimulus-synchronous brain computer interfaces. This paper presents a comparison of three different algorithmic schemes for single-trial ERP detection: SVM (baseline), hierarchical SVM-(naive) Bayes, selected temporal windows-based SVM-(naive) Bayes. An ERP-based image search system, including experimental setup, data collection, pre-processing, and three...
Brain-computer interface provides a new communication paradigm between the human and machine, thus allowing physically impaired and paralyzed patients to control devices with the aid of brain activity alone, instead of using normal brain output pathways. In this paper, we present an algorithm to classify single-trial electroencephalogram (EEG) during the preparation of self-paced key tapping based...
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