Rule-Based EEG Classifier Utilizing Local Entropy of Time–Frequency Distributions
Electroencephalogram (EEG) signals are known to contain signatures of stimuli that induce brain activities. However, detecting these signatures to classify captured EEG waveforms is one of the most challenging tasks of EEG analysis. This paper proposes a novel time–frequency-based method for EEG ana...
| 主要な著者: | , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
MDPI AG
2021-02-01
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| シリーズ: | Mathematics |
| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/2227-7390/9/4/451 |