Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification

Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification

This work presents a method for classifying EEG (Electroencephalogram) signals generated when a person concentrates on specific words, defined as “Imagined Speech”. Imagined speech is essential to enhance problem-solving, memory, and language development. In addition, imagined speech is beneficial b...

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Bibliographic Details
Main Authors: Edgar Lara-Arellano, Andras Takacs, Saul Tovar-Arriaga, Juvenal Rodríguez-Reséndiz
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Eng
Subjects:
imagined speech
EEG
feature extraction
genetic algorithm
classification
optimization algorithms
Online Access:https://www.mdpi.com/2673-4117/6/4/75
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