Abstract
In this paper, Fisher linear discriminant analysis (FLDA) is used to classify the EEGP-300 signals which are extracted from brain activities. In this case, at first the preprocessing algorithms such as filtering and referencing are applied to the raw EEG signal. Then, in order to create a model out of the signal, a linear predictive coding model with 6 order is used. So that the signal is reconstructed by extracting linear predictive coding (LPC) model parameters of each single trial, and then every signal trial is passed through the Hamming window by length 9. At last Fisher Linear Discriminant Analysis is used for classifying. In this paper, classification accuracy, the maximum bit rate and the convergence time to achieve stability in maximum accuracy of classification are computed to compare performance of the proposed method, Fisher Linear Discriminant Analysis with Linear Predictive Coding Model and Hamming Window (LPC+HAMMING+FLDA), to FLDA and LPC+FLDA. The implementation results show that the efficiency of the proposed method in terms of classification accuracy and convergence time to achieve stability in maximum accuracy is better than the other two mentioned algorithms. As example, at the proposed algorithm with 8 electrode configuration the S2 converges to the maximum accuracy after eleventh Block while this happens for two other algorithms after fourteenth Block and the total classification accuracy for this person at proposed algorithm is improved as 2.2% and 4% than respectively LPC+FLDA and FLDA algorithms.
Highlights
The EEG signal is related to the signals which are measured during synaptic excitation of many pyramidal neurons of brain's crust, which can be measured using electromyogram machine
In a BCI system, by using brain signals that can be recorded in different ways, we could analyze mental intentions of a person
As shown in Fig. (2), a Hamming window with length 9 is used as a filter on each set of sequences that each set includes 32 samples
Summary
The EEG signal is related to the signals which are measured during synaptic excitation of many pyramidal neurons of brain's crust, which can be measured using electromyogram machine. In a BCI system, human brain activities are converted to computer usable commands and the purpose is to improve and develop the systems which are able to communicate with the outside universe and control different organs of disabled people [3]. Especially important due to noninvasive property and easy implementation These signals reflect electrical activities in large group of nervous signals in brain. In designing a BCI system, many types of mental activities may be used These methods can be divided into two main groups based on their production [7]: A) Using of stimulation input such as Visual Evoked Potentials (VEPs). The main application is for disabled people who suffer from severe muscle inability [9] They can communicate with the external world and control the different organs of their body. The most commonly used classifiers in this case include the linear discriminant classifiers [17], neural networks, K-means classifier and combination of classifiers [18], nonlinear Bayesian classifiers [19]
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