Motor Imagery Task Classification in EEG Signals with Spiking Neural Network
| Autor: | Carlos D. Virgilio G, Javier M. Antelis, Humberto Sossa, Luis Eduardo Falcón |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Foot (prosody)
Spiking neural network Quantitative Biology::Neurons and Cognition medicine.diagnostic_test Artificial neural network Computer science business.industry Feature extraction Pattern recognition 02 engineering and technology Electroencephalography 03 medical and health sciences Task (computing) 0302 clinical medicine Motor imagery Multilayer perceptron 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030210762 MCPR |
| DOI: | 10.1007/978-3-030-21077-9_2 |
| Popis: | We report the development and evaluation of brain signal classifiers, specifically Spiking Neuron based classifiers. The proposal consists of two main stages: feature extraction and pattern classification. The EEG signals used represent four motor imagery tasks: Left Hand, Right Hand, Foot and Tongue movements. In addition, one more class was added: Rest. These EEG signals were obtained from a database provided by the Technological University of Graz. Feature extraction stage was carried out by applying two algorithms: Power Spectral Density and Wavelet Decomposition. The tested algorithms were: K-Nearest Neighbors, Multilayer Perceptron, Single Spiking Neuron and Spiking Neural Network. All of them were evaluated in the classification between two Motor Imagery tasks; all possible pairings were made with the 5 mental tasks (Rest, Left Hand, Right Hand, Tongue and Foot). In the end, a performance comparison was made between a Multilayer Perceptron and Spiking Neural Network. |
| Databáze: | OpenAIRE |
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