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A simple method is developed for selecting effective channels and feature dimensions automatically at the training stage of BCI systems. The method is applied on feature vectors constructed with all the EEG channels used for recording. Performance was evaluated with EEG data which was preprocessed by band pass filtering and feature vectors constructed by band powers. The classification method used...
Brain computer interface is one of the most recent and latest hot field in Computer Science which emerged in order to help some handicapped people. This paper investigates different classification algorithms that deal with the BCI P300 speller diagram. The system used is composed of an ensemble of Support vector machines. Three different methods are used, namely weighted ensemble of SVM, channel selection...
Brain Computer Interface Systems (BCIs) allow the identification of volitive brain activity patterns. This allows their use as input channels for alternative communication and computer access systems by patients suffering from severe motor disabilities. This paper presents preliminary results obtained after extracting four different features from EEG signals in order to recognize the activity patterns...
In this paper, we propose a tensorial approach to single trial recognition in a EEG-based BCI system related to movement related potentials. In this approach input data are considered as tensors instead of more conventional vector or matrix representations. Feature extraction for multiway EEG spectral tensors is solved by using tensor (multi-array) decompositions. For the same EEG motor imagery dataset,...
The non-stationary nature of the electroencephalogram (EEG) poses a major challenge for the successful operation of a brain-computer interface (BCI) when deployed over multiple sessions. The changes between the early training measurements and the proceeding multiple sessions can originate as a result of alterations in the subject's brain process, new cortical activities, change of recording conditions...
Electroencephalography (EEG) is the reaction of the overall activities of the brain neurons. In the researches of Brain Computer Interface (BCI), the pattern recognition of EEG which is associated with mental tasks is the most important part of the BCI system. In this paper, data of ?? wave and ?? wave of C3, C4, P3 and P4 channels are certificated to be the proper sources for feature extraction,...
Automatic classification of electroencephalography (EEG) signals, for different type of mental activities, is an active area of research and has many applications such as brain computer interface (BCI) and medical diagnoses. We introduce a simple yet effective way to use Kullback-Leibler (KL) divergence in the classification of raw EEG signals. We show that k-nearest neighbor (k-NN) algorithm with...
The common spatial patterns (CSP) algorithm has been widely used in EEG classification and brain computer interface (BCI). In this paper, we propose a multilinear formulation of the CSP, termed as TensorCSP or common tensor discriminant analysis (CTDA) for high-order tensor data. As a natural extension of CSP, the proposed algorithm uses the analogous optimization criteria in CSP and a new framework...
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