RT info:eu-repo/semantics/article
T1 Effect of time windows in LSTM networks for EEG-based BCIs
A1 Gómez González, José Francisco
A1 Martín Chinea, Kevin
A1 Ortega Rodríguez, Jordan
A1 Pereda de Pablo, Ernesto
A1 Toledo Carrillo, Jonay Tomás
A1 Acosta Sánchez, Leopoldo
A2 Ingeniería Industrial
K1 EEG
K1 LSTM
K1 Brain–computer interface
K1 Machine learning
K1 Deep learning
K1 Artificial neural network
AB People with impaired motor function could be helped by an effective brain–computer interface (BCI) based on a real-timeelectroencephalogram (EEG) and artificial intelligence algorithms. However, current methodologies for interpreting patientinstructions from an EEG are not accurate enough to be completely safe in a real-world situation , where a poor decisionwould place their physical integrity at risk, such as when traveling in an electric wheelchair in a city. For various reasons,such as the low signal-to-noise ratio of portable EEGs or the effects of signal contamination (disturbances due to usermovement, temporal variation of the features of EEG signals, etc.), a long short-term memory network (LSTM) (a type ofrecurrent neural network) that is able to learn data flow patterns from EEG signals could improve the classification of theactions taken by the user. In this paper, the effectiveness of using an LSTM with a low-cost wireless EEG device in realtime is tested, and the time window that maximizes its classification accuracy is studied. The goal is to be able toimplement it in the BCI of a smart wheelchair with a simple coded command protocol, such as opening or closing the eyes,which could be executed by patients with reduced mobility. Results show a higher resolution of the LSTM with anaccuracy range between 77.61 and 92.14% compared to traditional classifiers (59.71%), and an optimal time window ofaround 7 s for the task done by users in this work. In addition, tests in real-life contexts show that a trade-off betweenaccuracy and response times is necessary to ensure detection.
SN 1871-4099
YR 2022
FD 2022
LK http://riull.ull.es/xmlui/handle/915/39046
UL http://riull.ull.es/xmlui/handle/915/39046
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 27-dic-2024