Exploring differences for motor imagery using Teager energy operator-based EEG microstate analyses
| Autor: | Yabing Li, Mo Chen, Shujun Sun, Zipeng Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Adult
Spatial correlation Support Vector Machine Computer science Neurosciences. Biological psychiatry. Neuropsychiatry Electroencephalography Motor Activity Motor imagery motor imagery Ministate Classifier (linguistics) medicine Humans eeg signals teager energy operator classifier medicine.diagnostic_test business.industry General Neuroscience Pattern recognition Signal Processing Computer-Assisted General Medicine Energy operator Support vector machine Duration (music) Imagination Artificial intelligence business microstate parameters Psychomotor Performance RC321-571 |
| Zdroj: | Journal of Integrative Neuroscience, Vol 20, Iss 2, Pp 411-417 (2021) |
| Popis: | In this paper, the differences between two motor imagery tasks are captured through microstate parameters (occurrence, duration and coverage, and mean spatial correlation (Mspatcorr)) derived from a novel method based on electroencephalogram microstate and Teager energy operator. The results show that the significance between microstate parameters for two tasks is different (P < 0.05) with paired t-test. Furthermore, these microstate parameters are utilized as features. Support vector machine is utilized to classify the two tasks with a mean accuracy of 93.93%, which yielded superior performance compared to the other methods. |
| Databáze: | OpenAIRE |
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