Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks

Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications

Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods.

Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis.

Eye movements are critical signs of the neurological disorders and they can be acquired by EOG. The EOG signal is electrical signal generated due to eye ball movements and is contaminated with brain signals and power line while recording. As the EOG signal is a non-stationary signal, it can be denoised by wavelet transformation techniques. The present work covers denoising of noisy EOG signal using Stationary Wavelet Transform (SWT), which was done with all suitable wavelets that are morphologically similar to an EOG signal by applying both Soft and Hard Thresholding methods. An EOG signal was simulated and added with noise to obtain noisy EOG signal. The wavelet analysis of the simulated noisy EOG signal reveals that the Biorthogonal 3.3 wavelet is the best wavelet to denoise by using SWT technique, wherein the yield achieved was good with Signal to Noise Ratio of 36.5882 dB and minimum Mean Square Error of 0.383313 for quality diagnosis.

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Denoising EOG Signal using Stationary Wavelet Transform

A two-stage framework for denoising electrooculography signals

Eye ball movements are vital signs in some of the neurological disorders and it can be tracked by acquiring electrooculogram (EOG) signals. EOG is an obtrusive, inexpensive and non-invasive means of recording eye ball movements. The source for EOG signal is cornea-retinal potential (CRP) and is generated due to the movements of eye balls within the conductive environment of the skull. While recording the EOG signal, it will be contaminated by electromyography (EMG) signal. As the EOG is a non stationary signal, the multi resolution analysis using wavelet decomposition offers the best solution to denoise the EOG signal. In this paper, the author proposed a new wavelet based method to detect eye ball moments from signal conditioned EOG. Comparative wavelet analysis is performed by considering different statistical measures. Test results reveal that the Symlet based method provides better efficacy in eliminating noise from EOG signals.

Analysis of EOG signals using wavelet transform for detecting eye blinks

Eye ball movement is the most common way for paralysis patients to communicate. Eye movement can be used by the paralysis patients and armless persons to perform simple tasks such as Human Machine Interfacing and EOG signals are also vital signs in some of the neurological disorders and it can be tracked by acquiring electrooculogram (EOG) . EOG is an obtrusive, inexpensive and non-invasive means of recording eye ball movements. The source for EOG signal is cornea-retinal potential (CRP) and is generated due to the movements of eye balls within the conductive environment of the skull. While recording the EOG signal, it will be contaminated by electromyography (EMG) signal. As the EOG is a non stationary signal like other biomedical signals ECG, EEG & PPG. Generally they are suffers from different interferences like power line interference and with other biomedical signals. The concept of decomposing the signal into different IMF's will analyze the EOG signals to study different types of eye movements (SEMs & REMs). In this paper, the well established method is utilized for denoising and detection of eye blinks from ECG signals.

Analysis of EOG Signals Using Empirical Mode Decomposition for Eye Blink Detection

Human activities are recognized from the Electrooculogram (EOG) signal generated from the movement of eye. Hence early, accurate preprocessing of EOG signals is important. In recent years, this became an active area of research. The EOG signal captured using acquisition device is corrupted with the noise and device intrinsic, thus pre processing (noise reduction) is first and foremost step in any further analysis & activity recognition. In this paper a novel method of De-noising EOG signals using Dual Tree complex wavelet transform (DT-CWT) is proposed. The Denoising results obtained are compared with conventional wavelet (DWT) de-noising method. To demonstrate the efficacy of the proposed method, SNR calculations and the statistical analysis are evaluated. The proposed method is best suitable for real time EOG based applications like human-machine communication devices for disabled persons, eye movement analysis and gaming applications.

K. Subba Rao

Papers

Analysis of EOG signals using wavelet transform for detecting eye blinks

Analysis of EOG Signals Using Empirical Mode Decomposition for Eye Blink Detection

Complex wavelet transform driven de-noising method for an EOG signals

Diabetic retinopathy detection by optimized deep learning model