Group based Swarm evolution algorithm (GSEA) driven mental task classifier

Agarwal, Saurabh; Shah, Saatvik; Kumar, Rajesh

doi:10.1007/s12293-015-0155-0

Cited by 4 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of these algorithms use an elitism-based strategy [163]. The application of evolutionary algorithms in BMI-based systems can be categorized into four major groups of filter design [57,58,[164][165][166], feature selection [61,[167][168][169][170][171][172][173][174], channel selection and optimal electrodes [52, 58-60, 175, 176], parameter selection and optimal classifier structure [9,68,69,[177][178][179][180][181][182][183][184], and finally improving the performance of control tools [185].…”

Section: Evolutionary Algorithms (Eas)mentioning

confidence: 99%

“…In the application of evolutionary algorithms to improve the performance of BMI-based systems, the three evolutionary algorithms of GA, PSO and DE are most commonly used. Based on the conducted studies, the application of the PSO algorithm in BMI can be categorizes in three groups of filter design [164], feature selection, frequency and optimal electrodes [59,167,168,171,175,176,190], and classification [9,69,177,178,[180][181][182][183][184]191]. [164] uses five generalized versions of PSO (constant weighted inertia-PSO, linear decay inertia-PSO, constriction factor inertia-PSO, nonlinear inertia-PSO, and dynamic inertia-PSO) in order to design the optimal coefficients of the adaptive noise canceller (ANC) [192] for filtering of EEG signals.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…In this paper, the preprocessed features are used. Many papers in the field of BMI in recent years have used the pre-processed data [177,181,[235][236][237] and the raw data [238] from this database. References [177,181] present and compare the group-based Swarm evolution algorithm-NN (GSEA-NN) and improved PSO-NN (IPSO-NN) classifiers with other classifiers for improving the accuracy of the classification.…”

Section: Database 1 This Database Includes Three-class Motor Imagery-...mentioning

confidence: 99%

“…Many papers in the field of BMI in recent years have used the pre-processed data [177,181,[235][236][237] and the raw data [238] from this database. References [177,181] present and compare the group-based Swarm evolution algorithm-NN (GSEA-NN) and improved PSO-NN (IPSO-NN) classifiers with other classifiers for improving the accuracy of the classification. GDES is an evolutionary algorithm based on swarm intelligence and the differential evolution IPSO is the improved version of the PSO algorithm which is used to improve the neural network classifier.…”

Section: Database 1 This Database Includes Three-class Motor Imagery-...mentioning

confidence: 99%

See 3 more Smart Citations

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

2018

View full text Add to dashboard Cite

show abstract

Section: Evolutionary Algorithms (Eas)mentioning

confidence: 99%

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Section: Database 1 This Database Includes Three-class Motor Imagery-...mentioning

confidence: 99%

Section: Database 1 This Database Includes Three-class Motor Imagery-...mentioning

confidence: 99%

See 2 more Smart Citations

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

2018

View full text Add to dashboard Cite

show abstract

A robust framework for optimum feature extraction and recognition of P300 from raw EEG

Shah¹,

Kumar

et al. 2019

U-Healthcare Monitoring Systems

View full text Add to dashboard Cite

Research on Fuzzy Scheduling of Cloud Computing Tasks Based on Hybrid Search Algorithms and Differential Evolution

Jin¹,

Liu²,

Malaikah³

et al. 2022

Fractals

View full text Add to dashboard Cite

In the process of task scheduling, due to the large search space of resources, it takes a long time to allocate appropriate resources for tasks, which results in the increase of execution time and execution cost of algorithms. For this reason, this paper proposes a cloud computing task fuzzy scheduling strategy based on hybrid search algorithm and differential evolution, which incorporates the classification based on normal distribution and a variety of mutation strategies on the basis of standard differential evolution algorithm. In mutation strategy, the individual difference vector assigns priority to each task and arranges tasks according to resource allocation rules. It improves the slow convergence speed and easy to fall into local optimum of standard difference algorithm, and can effectively solve the task scheduling problem of cloud computing. A cloud computing task scheduling algorithm with time and cost constraints is designed and tested in a simulation environment. The experimental results show that the algorithm can not only shorten the task processing time and reduce the execution cost, but also fully satisfy the users’ actual quality of service requirements by adjusting the weights of time and cost factors.

show abstract

Group based Swarm evolution algorithm (GSEA) driven mental task classifier

Cited by 4 publications

References 47 publications

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

A robust framework for optimum feature extraction and recognition of P300 from raw EEG

Research on Fuzzy Scheduling of Cloud Computing Tasks Based on Hybrid Search Algorithms and Differential Evolution

Contact Info

Product

Resources

About