SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

Abstract

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification

“…In this study, 500 data segments correspond to each SNR value in the semi-simulated data. For each segment of data, the ocular artifact removal process was performed using EEMD-ICA [ 53 ], SSA-SOBI [ 23 ], CWT-KMEANS-SSA [ 54 ], VME-DWT [ 55 ], and the proposed SVM-IVMD-SOBI method. Each algorithm was run several times on different data for every SNR value, and the effect of the algorithms on ocular artifact removal for different SNR data was evaluated by calculating the mean and standard deviation of the four metrics, namely, RRMSE, CC, MSE, and PSNR, for these data.…”

Eye Blink Identification and Removal From Single-Channel Eeg Using Emd With Energy Threshold and Adaptive Filter

“…Subsequently, the appropriate workaround is introduced to ensure satisfactory eye blink component reconstruction. For example, Maddirala et al use Hjorth mobility as a threshold to choose the SSA subspaces for the eye blink reconstruction [13]. While in [14], the eigenvalue ratio is used as the threshold to select the subspaces that correspond to the eye blink component for eye blink reconstruction.…”

“…The threshold qResol is as an energy ratio of which when the energy of the e(t) is qResol times lower than the energy of x(t) or 𝑟 𝑛 (𝑡), the IMF is derived from the following: , IMF is derived. (13) Threshold qResol aims to avoid constructing envelopes that introduce new components during IMF derivation. As for the energy threshold for the stopping criterion, the ratio of x(t) or 𝑟 𝑛 (𝑡) to the new residual produced (𝑟 𝑛+1 (𝑡)), which is produced after an IMF is derived, called threshold qResid.…”

ICA With CWT and k-means for Eye-Blink Artifact Removal From Fewer Channel EEG

“…SSA is a subspace based technique and it can decompose the given uni-variate time-series signal into low-frequency trend, the oscillating and the noise components. SSA has been used widely to process the EEG signals [43], [44]. Here, we employ SSA as a low-pass (smoothing) filter to remove any EEG remnants that reside on the eye-blink activity portion and also to smooth the edges at the onset and offset of eyeblink activity.…”