Brain Signals to Actions Using Machine Learning
This research presents a machine learning model that predicts left, right, or no action using electroencephalography (EEG) signals extracted from two different wearable EEG headsets. The research aims to develop an accurate and efficient model by following a rigorous and effective process divided in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/50/1/7 |
| _version_ | 1797241264382410752 |
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| author | Dimitris Angelakis Errikos Ventouras Pantelis Asvestas |
| author_facet | Dimitris Angelakis Errikos Ventouras Pantelis Asvestas |
| author_sort | Dimitris Angelakis |
| collection | DOAJ |
| description | This research presents a machine learning model that predicts left, right, or no action using electroencephalography (EEG) signals extracted from two different wearable EEG headsets. The research aims to develop an accurate and efficient model by following a rigorous and effective process divided into two parts. In Part I, the constant features approach is employed, which involves data loading, feature extraction, preprocessing, model selection, and tuning the best model for optimal performance. The performance of classification algorithms (support vector machine (SVM), decision tree classifier, and random forest classifier) is evaluated using root-mean-squared error metrics. In Part II, the multivariate time series approach is utilized to improve the accuracy and robustness of the model. The approach involves data loading, preprocessing (such as normalizing the data), modeling, results analysis, and deployment preparation. A neural network architecture consisting of convolutional filters followed by a long short-term memory neural network (LSTM) is used in the proposed approach. The convolutional layer performs a convolution of an input series of feature maps with a filter matrix to extract high-level features. The LSTM network is specifically designed to capture long-term dependencies and overcome the issue of vanishing gradients. The proposed approach achieves an accuracy of 98% and can be used for real-time testing. The model can be utilized in various fields where accurate and real-time prediction of brain–computer interfaces (BCI) actions is crucial. Overall, the proposed approach provides a promising solution to the problem of action prediction using EEG signals, and further research can be conducted to explore its potential applications and optimize its performance. |
| first_indexed | 2024-04-24T18:20:34Z |
| format | Article |
| id | doaj.art-b116adc4dc554f95b56ad37a73c60219 |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-04-24T18:20:34Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-b116adc4dc554f95b56ad37a73c602192024-03-27T13:36:22ZengMDPI AGEngineering Proceedings2673-45912023-10-01501710.3390/engproc2023050007Brain Signals to Actions Using Machine LearningDimitris Angelakis0Errikos Ventouras1Pantelis Asvestas2Department of Biomedical Engineering, University of West Attica, 12243 Athens, GreeceDepartment of Biomedical Engineering, University of West Attica, 12243 Athens, GreeceDepartment of Biomedical Engineering, University of West Attica, 12243 Athens, GreeceThis research presents a machine learning model that predicts left, right, or no action using electroencephalography (EEG) signals extracted from two different wearable EEG headsets. The research aims to develop an accurate and efficient model by following a rigorous and effective process divided into two parts. In Part I, the constant features approach is employed, which involves data loading, feature extraction, preprocessing, model selection, and tuning the best model for optimal performance. The performance of classification algorithms (support vector machine (SVM), decision tree classifier, and random forest classifier) is evaluated using root-mean-squared error metrics. In Part II, the multivariate time series approach is utilized to improve the accuracy and robustness of the model. The approach involves data loading, preprocessing (such as normalizing the data), modeling, results analysis, and deployment preparation. A neural network architecture consisting of convolutional filters followed by a long short-term memory neural network (LSTM) is used in the proposed approach. The convolutional layer performs a convolution of an input series of feature maps with a filter matrix to extract high-level features. The LSTM network is specifically designed to capture long-term dependencies and overcome the issue of vanishing gradients. The proposed approach achieves an accuracy of 98% and can be used for real-time testing. The model can be utilized in various fields where accurate and real-time prediction of brain–computer interfaces (BCI) actions is crucial. Overall, the proposed approach provides a promising solution to the problem of action prediction using EEG signals, and further research can be conducted to explore its potential applications and optimize its performance.https://www.mdpi.com/2673-4591/50/1/7brain–computer interfaces (BCI)constant features approachfeature extractionlong short-term memory (LSTM)support vector machine (SVM) |
| spellingShingle | Dimitris Angelakis Errikos Ventouras Pantelis Asvestas Brain Signals to Actions Using Machine Learning Engineering Proceedings brain–computer interfaces (BCI) constant features approach feature extraction long short-term memory (LSTM) support vector machine (SVM) |
| title | Brain Signals to Actions Using Machine Learning |
| title_full | Brain Signals to Actions Using Machine Learning |
| title_fullStr | Brain Signals to Actions Using Machine Learning |
| title_full_unstemmed | Brain Signals to Actions Using Machine Learning |
| title_short | Brain Signals to Actions Using Machine Learning |
| title_sort | brain signals to actions using machine learning |
| topic | brain–computer interfaces (BCI) constant features approach feature extraction long short-term memory (LSTM) support vector machine (SVM) |
| url | https://www.mdpi.com/2673-4591/50/1/7 |
| work_keys_str_mv | AT dimitrisangelakis brainsignalstoactionsusingmachinelearning AT errikosventouras brainsignalstoactionsusingmachinelearning AT pantelisasvestas brainsignalstoactionsusingmachinelearning |