ArticleComputational intelligence and neuroscience2022
EEG Feature Extraction Using Evolutionary Algorithms for Brain-Computer Interface Development.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Application and Research Progress of BCI in Post-Stroke Psychiatric Disorders: A Narrative Review.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Toward practical BCIs: a BMNABC-based feature selection and sensor optimization framework for implicit learning detection from multimodal EEG-fNIRS data.Frontiers in human neuroscience · 2026Article
- Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs.Frontiers in human neuroscience · 2024Article
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Authors and funding
8 authors.
Funding
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Abstract
Brain-computer interfaces are systems capable of mapping brain activity to specific commands, which enables to remotely automate different types of processes in hardware devices or software applications. However, the development of brain-computer interfaces has been limited by several factors that affect their performance, such as the characterization of events in brain signals and the excessive processing load generated by the high volume of data. In this paper, we propose a method based on computational intelligence techniques to handle these problems, turning them into a single optimization problem. An artificial neural network is used as a classifier for event detection, along with an evolutionary algorithm to find the optimal subset of electrodes and data points that better represents the target event. The obtained results indicate our approach is a competitive and viable alternative for feature extraction in electroencephalograms, leading to high accuracy values and allowing the reduction of a significant amount of data.
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Registered trials
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