Classify four imagined objects with eeg signals
EEG signals contain information directly related to cognitive activity. This paper presents a method to classify the images a person imagines via the information provided by the EEG signals. The images relating to the objects `tree', `house', `plane' and `dog' have been reconstructed. We have used a...
| Authors: | ; ; ; |
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| Format: | Article |
| Status: | Accepted version |
| Publication date: | 2021 |
| Country: | España |
| Institution: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repository: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/19329 |
| Online Access: | http://hdl.handle.net/10256/19329 |
| Access Level: | Open access |
| Keyword: | Intel·ligència artificial Aprenentatge automàtic Artificial intelligence Machine learning |
| Summary: | EEG signals contain information directly related to cognitive activity. This paper presents a method to classify the images a person imagines via the information provided by the EEG signals. The images relating to the objects `tree', `house', `plane' and `dog' have been reconstructed. We have used a convolutional network to obtain the reconstruction of the images and a genetic algorithm to find the parameters of the network. The results obtained have been evaluated by means of a Chebychev metric of comparison of images, and this shows that the reconstruction is performed with a success of 57% over chance, with an accuracy in the classification of 60% and a kappa value of 0.40, demonstrating that the classification of five mental states where four of them come from the visual imagery is possible |
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