EEG based drowsiness detection using relative band power and short time fourier transform

Krishnan, Pranesh; Yaacob, Sazali; Krishnan, Annapoorni Pranesh; Rizon, M.

doi:10.5954/icarob.2020.os10-2

Cited by 2 publications

(1 citation statement)

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“…In [10], augmenting STFT is used to extract EEG features by mapping the spatiotemporal implicit relation to the 2D image for classification. In [11], the relative band power and STFT are used for EEG based drowsiness detection. In [12], a robust sorting algorithm and Friedman test are used to search the best combination feature, and the effectiveness of PSD combination feature is verified by comparing with other methods.…”

Section: Introductionmentioning

confidence: 99%

The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface

et al. 2021

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The breakthrough of electroencephalogram (EEG) signal classification of brain computer interface (BCI) will set off another technological revolution of human computer interaction technology. Because the collected EEG is a type of nonstationary signal with strong randomness, effective feature extraction and data mining techniques are urgently required for EEG classification of BCI. In this paper, the new bionic whale optimization algorithms (WOA) are proposed to promote the improved extreme learning machine (ELM) algorithms for EEG classification of BCI. Two improved WOA-ELM algorithms are designed to compensate for the deficiency of random weight initialization for basic ELM. Firstly, the top several best individuals are selected and voted to make decisions to avoid misjudgment on the best individual. Secondly, the initial connection weights and bias between the input layer nodes and hidden layer nodes are optimized by WOA through bubble-net attacking strategy (BNAS) and shrinking encircling mechanism (SEM), and different regularization mechanisms are introduced in different layers to generate appropriate sparse weight matrix to promote the generalization performance of the algorithm.As shown in the contrast results, the average accuracy of the proposed method can reach 93.67%, which is better than other methods on BCI dataset.

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Section: Introductionmentioning

confidence: 99%