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

The aim of this study is to build a linear decoding model that reveals the relationship between the movement information and the EOG electrooculogram data to online control a cursor continuously with blinks and eye pursuit movements. First of all, a blink detection method is proposed to reject a voluntary single eye blink or double-blink information from EOG. Then, a linear decoding model of time series is developed to predict the position of gaze, and the model parameters are calibrated by the RLS Recursive Least Square algorithm; besides, the assessment of decoding accuracy is assessed through cross-validation procedure. Additionally, the subsection processing, increment control, and online calibration are presented to realize the online control. Finally, the technology is applied to the volitional and online control of a cursor to hit the multiple predefined targets. Experimental results show that the blink detection algorithm performs well with the voluntary blink detection rate over 95%. Through combining the merits of blinks and smooth pursuit movements, the movement information of eyes can be decoded in good conformity with the average Pearson correlation coefficient which is up to 0.9592, and all signal-to-noise ratios are greater than 0. The novel system allows people to successfully and economically control a cursor online with a hit rate of 98%.

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Volitional and Real-Time Control Cursor Based on Eye Movement Decoding Using a Linear Decoding Model

Human activity recognition (HAR) is a research hotspot in the field of artificial intelligence and pattern recognition The electrooculography (EOG)-based HAR system has attracted much attention due to its good realizability and great application potential Focusing on the signal processing method of the EOG-HAR system, we propose a robust EOG-based saccade recognition using the multi-channel convolutional independent component analysis (ICA) method To establish frequency-domain observation vectors, short-time Fourier transform (STFT) is used to process time-domain EOG signals by applying the sliding window technique Subsequently, we apply the joint approximative diagonalization of eigenmatrix (JADE) algorithm to separate the mixed signals and choose the "clean" saccadic source to extract features To address the problem of permutation ambiguity in a case with a six-channel condition, we developed a constraint direction of arrival (DOA) algorithm that can automatically adjust the order of eye movement sources according to the constraint angle Recognition experiments of four different saccadic EOG signals (ie up, down, left and right) were conducted in a laboratory environment The average recognition ratios over 13 subjects were 9566% and 9733% under the between-subjects test and the within-subjects test, respectively Compared with "bandpass filtering", "wavelet denoising", "extended infomax algorithm", "frequency-domain JADE algorithm" and "time-domain JADE algorithm, the recognition ratios obtained relative increments of 46%, 349%, 285%, 281% and 291% (within-subjects test) and 491%, 343%, 221%, 224% and 228% (between-subjects test), respectively The experimental results revealed that the proposed algorithm presents robust classification performance in saccadic EOG signal recognition

Robust EOG-based saccade recognition using multi-channel blind source deconvolution

Saccadic eye movement as a voluntary activity has become the basis of a promising approach for the implementation of human–computer interaction. Seeking blind source separation (BSS) model that can exactly describe eye movement generation and improve the quality of saccadic electrooculography (EOG) signals, we performed a comparative study between an instantaneous and a convolutional mixing model for multichannel EOG signals. Experiments involving seven subjects were carried out in a laboratory environment. The independent component analysis (ICA) method was adopted for BSS. The statistical indicators of the residual statistical dependence and the total square cross correlation for the convolutional model were 0.04 (second order)/0.08 (fourth order) and 1.25, respectively, which were lower than those of the instantaneous model by 0.3 (second order)/0.17 (fourth order) and 0.32, respectively. In addition, the average classification accuracies of the convolutional model were 95.76% (within subject) and 94.08% (between subject) in the case of upward, downward, left, and right saccade tasks, which were higher than those of the instantaneous model by 1.69% and 5.53%, respectively. Experiments revealed that the quality of the saccadic signals separated using the convolutional model was higher than that achieved using the instantaneous model. The outcomes of this research can serve as a valuable reference for multichannel EOG analysis and application.

A Comparative Experimental Study Between Instantaneous and Convolutional BSS Models for Saccadic EOG Signal Separation

A growing area of research addresses how physiological data can be used to understand the state of an operator across a variety of operational environments, including semi-autonomous driving. Use of multiple physiological sensors is highly practical; however, it introduces a number of challenges related to signal noise reduction. During the design and construction of the laboratory in HI:DAVe it became clear that there was not much documentation concerning how to undertake such a task. Paper is assimilating the literature related to this to provide a user’s guide for how someone can get to the stage of a fully functional Faraday Cage mid-fidelity driving simulator with an extensive physiological recording suite. It describes a process of psychophysiological laboratory construction and introduces a decision-tree to help with the choice of the most optimal noise-reduction strategies. The psychophysiological measures included are electroencephalography, electrooculography, electromyography, eye-tracking, voice analysis, electrocardiography, respiration, electrodermal response and oximetry.

Surviving in Frankenstein’s Lab- How to Build and Use a Laboratory that Applies Multiple Physiological Sensors

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. Punnam Chander

Papers

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