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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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/6/4/75 |
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| author | Edgar Lara-Arellano Andras Takacs Saul Tovar-Arriaga Juvenal Rodríguez-Reséndiz |
| author_facet | Edgar Lara-Arellano Andras Takacs Saul Tovar-Arriaga Juvenal Rodríguez-Reséndiz |
| author_sort | Edgar Lara-Arellano |
| collection | DOAJ |
| description | 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 because of its applications in therapy fields like managing anxiety or improving communication skills. EEG measures the electrical activity of the brain. EEG signal classification is difficult as the machine learning (ML) algorithm has to learn how to categorize the signal linked to the imagined word. This work proposes a novel method to generate a specific feature vector to achieve classification with superior accuracy results to those found in the state of the art. The method leverages a genetic algorithm to create an optimal feature combination for the classification task and machine learning model. This algorithm can efficiently explore ample feature space and identify the most relevant features for the task. The proposed method achieved an accuracy of 96% using eight electrodes for EEG signal recordings. |
| format | Article |
| id | doaj-art-df8e5c503a254fa98e67142ba36bc96f |
| institution | DOAJ |
| issn | 2673-4117 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj-art-df8e5c503a254fa98e67142ba36bc96f2025-08-20T03:13:47ZengMDPI AGEng2673-41172025-04-01647510.3390/eng6040075Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal ClassificationEdgar Lara-Arellano0Andras Takacs1Saul Tovar-Arriaga2Juvenal Rodríguez-Reséndiz3Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoThis 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 because of its applications in therapy fields like managing anxiety or improving communication skills. EEG measures the electrical activity of the brain. EEG signal classification is difficult as the machine learning (ML) algorithm has to learn how to categorize the signal linked to the imagined word. This work proposes a novel method to generate a specific feature vector to achieve classification with superior accuracy results to those found in the state of the art. The method leverages a genetic algorithm to create an optimal feature combination for the classification task and machine learning model. This algorithm can efficiently explore ample feature space and identify the most relevant features for the task. The proposed method achieved an accuracy of 96% using eight electrodes for EEG signal recordings.https://www.mdpi.com/2673-4117/6/4/75imagined speechEEGfeature extractiongenetic algorithmclassificationoptimization algorithms |
| spellingShingle | Edgar Lara-Arellano Andras Takacs Saul Tovar-Arriaga Juvenal Rodríguez-Reséndiz Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification Eng imagined speech EEG feature extraction genetic algorithm classification optimization algorithms |
| title | Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification |
| title_full | Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification |
| title_fullStr | Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification |
| title_full_unstemmed | Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification |
| title_short | Feature Generation with Genetic Algorithms for Imagined Speech Electroencephalogram Signal Classification |
| title_sort | feature generation with genetic algorithms for imagined speech electroencephalogram signal classification |
| topic | imagined speech EEG feature extraction genetic algorithm classification optimization algorithms |
| url | https://www.mdpi.com/2673-4117/6/4/75 |
| work_keys_str_mv | AT edgarlaraarellano featuregenerationwithgeneticalgorithmsforimaginedspeechelectroencephalogramsignalclassification AT andrastakacs featuregenerationwithgeneticalgorithmsforimaginedspeechelectroencephalogramsignalclassification AT saultovararriaga featuregenerationwithgeneticalgorithmsforimaginedspeechelectroencephalogramsignalclassification AT juvenalrodriguezresendiz featuregenerationwithgeneticalgorithmsforimaginedspeechelectroencephalogramsignalclassification |