Optimisation of a BCI system using the GA tehnique

Dobrea, Dan-Marius; Dobrea, Monica-Claudia

doi:10.1109/isabel.2009.5373673

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Since in each iteration the achieved informedness result on the validation set throughout the population is used as a reference for the selection of a sub-set of population for the next generation (except in random search), it can be concluded that the learning inside the used evolutionary methods is influenced by the validation set. Despite the fact that such results are reported in several studies [17], [18], [19], and [20] it is possible to consider this as a case of contamination between the training and evaluation sets. To avoid such scenario, a final evaluation step is conducted in which the training and testing sets are used to evaluate the results using Sigmoid ELM (the classifier that the EA approaches outcomes are customized on) and two alternative classifiers (Polynomial SVM and Perceptron).…”

Section: Resultsmentioning

confidence: 91%

Evolutionary feature selection and electrode reduction for EEG classification

Atyabi

Luerssen

Fitzgibbon

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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EEG signals usually have a high dimensionality which makes it difficult for classifiers to learn the difference of various classes in the underlying pattern in the signal. This paper investigates several evolutionary algorithms used to reduce the dimensionality of the data. The study presents electrode and feature reduction methods based on Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Evolution-based methods are used to generate a set of indexes presenting either electrode seats or feature points that maximizes the output of a weak classifier. The results are interpreted based on the dimensionality reduction achieved, the significance of the lost accuracy, and the possibility of improving the accuracy by passing the chosen electrode/feature sets to alternative classifiers.

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Section: Resultsmentioning

confidence: 91%

Evolutionary feature selection and electrode reduction for EEG classification

Atyabi

Luerssen

Fitzgibbon

et al. 2012

2012 IEEE Congress on Evolutionary Computation

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“…It also focuses on its importance on the rehabilitation of neurological disorders such as attention deficit/hyperactivity disorder. The classification algorithm used in Dobrea and Dobrea (2009) includes genetic algorithm-based ideology resulting in a better classification result and a faster BCI system. Khorshidtalab and Salami (2011) is an abstract review of the various classifications and feature extraction methods used in real-time EEG signal processing.…”

Section: Prior Workmentioning

confidence: 99%

A Review on Algorithms for EEG-Based BCIs

Das

Tripathy

Raheja

2018

SpringerBriefs in Applied Sciences and Technology

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Many classes of electrophysiological activities of the brain are used in designing various types of brain-computer interfaces (BCIs). Those are discussed briefly in this section. Sensorimotor activity generally corresponds to the behavior of the brain rhythms (mu, beta, and gamma), movement-related potentials (MRPs), etc. Next, the classification of BCI based on various parameters has also been discussed. These parameters are the mode of signal acquisition, timing, and placement of sensors. Later in this section, algorithms that have been and with chances of being, used in BCI applications have been discussed in a detailed manner. The algorithms chosen for each stage of the signal processing have an equal role to play in resulting a better outcome. Therefore, the section emphasizes the algorithms for each such stage, separately. Choosing a perfect algorithm is very important to design an efficient classifier. This section provides important information about the algorithms concerned with BCI. Sensorimotor ActivitySensorimotor cortex is the source of mu rhythms (ranging from 8 to 12 Hz) and beta rhythms (ranging from 13 to 30 Hz). These are generated when a person is not involved in sensory or motor activities. They are most distinct in the frontal and parietal lobes of the brain and make changes in the mu and lower beta bands. Any voluntary movement in the subject increases the power in the brain rhythms or frequencies. This phenomenon is also known as event-related synchronization (ERS). The peak of an ERS occurs at a delay of 600 ms following the movement offset. Finally, gamma rhythm is a high-frequency rhythm in the electroencephalography (EEG). The amplitude of gamma rhythm increases upon the occurrence of a movement.Movement-related potentials (MRPs) usually have a maximum amplitude at the vertex with low-frequency potentials, which become more significant close to the movement. Sensorimotor activities other than the previously mentioned ones are not restricted to any particular band of frequencies or location in the brain. For example,

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“…This cost had to be minimized by the GA. Two of` the main parameters of the GA, with an important impact on both -the convergence time to a solution and the quality of that solution -, are the population size and the crossover operator. In our case, related with this particular problem and based on a previous experience [7], a population of 15 chromosomes was used. The second parameter was managed in this analysis based on three different trials, with three different types of crossover operators.…”

Section: B Classification Of Eeg Features With Individual Spectral Bmentioning

confidence: 99%

GA methods for selecting the proper EEG individual spectral bands limits

Dobrea

Sîrbu

2010

2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010)

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In a previous set of analyses and researches we have proved the strong relationship that exists between each particular subject and its corresponding particular set of imagery cognitive tasks (determined out of several proposed mental tasks); these individual sets of tasks were the ones on which the obtained classification performances were significantly superior than on the other possible combinations of tasks. Also, a remarkable aspect is that all these improvements in classification were achieved for the same EEG features (namely, AR parameters) and the same processing and classification methods, that, during the entire study, were kept unmodified. In consequence, the act of finding, for each individual subject, the appropriate specific set of cognitive tasks should be considered of great importance in any brain computer interface (BCI) implementation. The present paper continues these researches and focuses on the necessity to find (as it has been already suggested in the literature), for each subject, that set of custom band power coefficients for which superior classification rates on the subject optimal set of cognitive tasks -previously determined -will be the highest. Based on some specific GA methods, implemented in order to find the subject appropriate frequency band parameters, and using a neural network structure for the classification, the final new obtained classification performances considerably improved with 4 to 6 percents.

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Optimisation of a BCI system using the GA tehnique

Cited by 4 publications

References 14 publications

Evolutionary feature selection and electrode reduction for EEG classification

Evolutionary feature selection and electrode reduction for EEG classification

A Review on Algorithms for EEG-Based BCIs

GA methods for selecting the proper EEG individual spectral bands limits

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