Abstract
The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.
Highlights
Electroencephalography (EEG) recordings from the scalp reflect the electrophysiological activity of the brain neurons
Rhythm extraction is very important for the research and application of EEG signal
The artifacts show considerably larger amplitude than the spontaneous EEG signal, which lead to an unsatisfied low signal-to-noise ratio (SNR) [8]
Summary
Electroencephalography (EEG) recordings from the scalp reflect the electrophysiological activity of the brain neurons. The features of the spontaneous EEG signal contain different physiological and pathological information [1]. Alpha rhythm (α-rhythm) reflects attentional demands and beta rhythm (β-rhythm) reflects emotional and cognitive processes [2]. Theta rhythm (θ-rhythm) is related to moral actions [3], while delta rhythm (δ-rhythm) is an indicator of attention to internal processing during performance of mental tasks [4]. Rhythm extraction is very important for the research and application of EEG signal. The recorded EEG signal usually contains large amounts of artifacts, such as electrooculogram (EOG), electromyography (EMG), electrocardiography (ECG), baseline drift and so on, in consistence with common interference and random noise originating from measurement system. The frequency spectrum of artifacts overlaps with that of the spontaneous EEG signal. Traditional methods based on frequency spectrum analysis, like Fourier Transform, are difficult to accomplish the artifacts removal and rhythms extraction
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