Vertical Electrooculogram Research Articles

Conservative method for vertical electrooculogram attenuation based on local suppression of ongoing EEG artifact templates.

Eye movement during blinking can be a significant artifact in Event-Related Potentials (ERP) analysis. Blinks produce a positive potential in the vertical electrooculogram (VEOG), spreading towards the posterior direction. Two methods are frequently used to suppress VEOGs: linear regression to subtract the VEOG signal from the electroencephalogram (EEG) and Independent Component Analysis (ICA). However, some information is lost in both. The present algorithm (1) statistically identifies the position of VEOGs in the frontopolar channels; (2) performs EEG averaging for each channel, which results in 'blink templates'; (3) subtracts each template from the respective EEG at each VEOG position, only when the linear correlation index between the template and the segment is greater than a chosen threshold L. The signals from twenty subjects were acquired using a behavioral test and were treated using FilterBlink for subsequent ERP analysis. A model was designed to test the method for each subject using twenty copies of the EEG signal from the subject's mid-central channel (with nearly no VEOG) representing the EEG channels and their respective blink templates. At the same 200 equidistant time points (marks), a signal (2.5 sinusoidal cycles at 1050 ms emulating an ERP) was mixed with each model channel and the respective blink template of that channel, between 500 to 1200 ms after each mark. According to the model, VEOGs interfered with both ERPs and the ongoing EEG, mainly on the anterior medial leads, and no significant effect was observed on the mid-central channel (Cz). FilterBlink recovered approximately 90% (Fp1) to 98% (Fz) of the original ERP and EEG signals for L = 0.1. The method reduced the VEOG effect on the EEG after ERP and blink-artifact averaging in analyzing real signals. The method is straightforward and effective for VEOG attenuation without significant distortion in the EEG signal and embedded ERPs.

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).

EOG acquisition system based on ATmega AVR microcontroller

This paper presents a hardware and software of an electrooculogram (EOG) acquisition system based on ATmega AVR microcontroller for the acquisition of vertical and horizontal eye movements. The presented system is used to acquire a dataset of eye movements for volunteers. This system gives two channels representing vertical and horizontal EOG signals. The frequency range of the EOG signal is known to be 0.1 to 10 Hz, and hence this frequency range is isolated with a High-Pass Filter (HPF) with a cutoff frequency of 0.1 Hz followed by a Low-Pass Filter (LPF) with a cutoff frequency of 10 Hz. The EOG acquisition system is interfaced with an ATmega AVR microcontroller to acquire a dataset that can be used for controlling hardware such as Light Emitting Diodes (LEDs), wheelchair, and robot arm. The presented system is composed of EOG signal acquisition, Ag/AgCl electrodes, analog-to-digital converter through Arduino Mega 2560 board microcontroller unit, trainer board, laptop, keypad, and Liquid Crystal Display (LCD). The eye movement is detected by measuring the potential difference between cornea and retina using five Ag–Agcl disposable electrodes. Different volunteers of different ages at different times have been treated with the presented system to obtain data. Classified vertical and horizontal EOG signals and the basic eye movements e.g., open eye, left, right, up, and down can be used to control robots and wheelchairs for rehabilitation purposes.

DWT-BSS: Blind Source Separation applied to EEG signals by extracting wavelet transform’s approximation coefficients

The Electroencephalogram (EEG) signal is widely contaminated by a physiological artifact, such as muscle activity, heart rhythm, and eye movement. The researcher has proposed a number of methods to clean the EEG signal. A group of these methods is called Blind Source Separation (BSS). In this paper, we suggest an approach that combines the BSS methods and the Discrete Wavelet Transform (DWT) algorithm, in order to evaluate the BSS methods after applying them to the approximation coefficients extracted using the DWT. The aim of this work is to identify which BSS algorithms, using which family of wavelet and at which decomposition level, would provide excellent performance. We used the Spearman Correlation Coefficient (SCC) to rate our methods. The technique that performs the best, as evaluated by the SCC between the generated component and the approximation coefficient obtained from the Horizontal EOG results, is AMICA, which obtains a value of 0.81 for levels 2 while using the wavelet symlet at scales 7 and 11. With a value of 0.70 and the use of the wavelet Daubechies at scale 9 and Coiflets at scales 2 and 5 for level 1, AMICA also has the best SCC value calculated between the separated component and the approximation coefficient recovered from the Vertical EOG. While employing the wavelet symlets at scales 5, 7, 8, and 11. for level 2, and level 3 when using the wavelet symlets at scales 1 and 2.

Monopolar and bipolar electrooculography signal characteristics due to target displacements—have we seen the whole picture?

The development of electrooculography (EOG)-based human-computer interface systems is generally based on the processing of the commonly referred to horizontal and vertical bipolar EOG channels, which are computed from a horizontally-aligned and another vertically-aligned pair of electrodes, respectively. Horizontal (vertical) target displacements are assumed to result in changes in the horizontal (vertical) EOG channel only, and any cross-talk between the bipolar channels is often neglected or incorrectly attributed solely to electrode misalignment with respect to the ocular rotation axes. Objective. The aim of this work is to demonstrate that such cross-talk is intrinsic to the geometric relationship between the orientation of the verging ocular globes and the planar displacement of the gaze target with respect to the primary gaze position. Approach. Since it is difficult to record actual EOG data with electrodes which are perfectly-aligned with the ocular rotation axes, this is studied by simulating the EOG potential values for various horizontally- and vertically-displacing targets using a dipole model of the eye. Main results. We show that cross-talk between the horizontal and vertical bipolar EOG channels is manifested even if the electrodes are aligned with the ocular rotation axes. Specifically, for a horizontally- (vertically-)displaced target, while the monopolar EOG signals obtained from the horizontally- (vertically-)aligned electrodes exhibit an expected predominant potential displacement, a smaller displacement is also exhibited in the monopolar EOG signals obtained from the vertically- (horizontally-)aligned electrodes. These unexpected displacements in the vertically- (horizontally-)aligned monopolar channels may have different magnitudes, resulting in an effective potential displacement in the vertical (horizontal) bipolar EOG channel. Significance. This is significant as it shows that, unlike in many works published so far for EOG-based ocular pose estimation, it is not sufficient to only use the horizontal (vertical) bipolar EOG channel to estimate the horizontal (vertical) displacement of the ocular pose.

SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals.

Electroencephalogram (EEG) signals are often corrupted by undesirable sources like electrooculogram (EOG) artifacts, which have a substantial impact on the performance of EEG-based systems. This study proposes a new singular spectrum analysis (SSA)-non-negative matrix factorization (NMF)-based ocular artifact removal (SNOAR) method to suppress ocular artifacts from multi-channel EEG signals. First, SSA was used to estimate EOG artifacts using a small subset of frontal electrodes. Then, NMF was applied to decompose the estimated EOG artifacts into vertical EOG (VEOG) and horizontal EOG (HEOG) signals. Finally, a simple linear regression with estimated VEOG and HEOG signals was used to remove artifacts from multi-channel EEG signals. EEG recordings from two EEG datasets (Klados dataset and KARA ONE) were used to evaluate the efficiency of the proposed method. From the simulation results, it was observed that the proposed method achieved betters results in terms of low root-mean-square error (RMSE), low delta band energy ratio, and less power spectral density (PSD) difference between the original clean EEG signal and its filtered version of contaminated EEG signal compared to selected EOG artifact removal methods (independent component analysis (ICA), wavelet-enhanced ICA (wICA), improved wICA, and multivariate empirical mode decomposition (MEMD)).

A novel approach for detection of dyslexia using convolutional neural network with EOG signals

Dyslexia is a learning disability in acquiring reading skills, even though the individual has the appropriate learning opportunity, adequate education, and appropriate sociocultural environment. Dyslexia negatively affects children's educational development; hence, early detection is highly important. Electrooculogram (EOG) signals are one of the most frequently used physiological signals in human-computer interfaces applications. EOG is a method based on the examination of the electrical potential of eye movements. The advantages of EOG-based systems are non-invasive, affordable, easy to record, and can be processed in real time.In this paper, a novel 1D CNN approach using EOG signals is proposed for the diagnosis of dyslexia. The proposed approach aims to diagnose dyslexia using EOG signals that are recorded simultaneously during reading texts, which are prepared in different typefaces and fonts. EOG signals were recorded from both horizontal and vertical channels, thus comparing the success of vertical and horizontal EOG signals in detecting dyslexia. The proposed approach provided an effective classification without requiring any hand-crafted feature extraction techniques. The proposed method achieved classifier accuracy of 98.70% and 80.94% for horizontal and vertical channel EOG signals, respectively. The results show that the EOG signals-based approach gives successful results for the diagnosis of dyslexia.

Discomfort Glare Perception by Drivers—Establishing a Link between Subjective and Psychophysiological Assessment

The broad application of LEDs for automotive lighting purposes, together with new discoveries in vision physiology, is creating new challenges in the field of glare perception. The purpose of this study was to link subjective and objective measures of driver-perceived glare following different light sources used in car headlamps. In order to achieve this, a combination of subjective evaluation using an adapted version of the de Boer scale and objective measures based on psychophysiological data was applied. Predominantly, skin conductance response (SCR), heart rate variability (HRV), and eye-blinking frequency (vertical electrooculography, vEOG) were recorded. Though there was some evidence suggesting lower discomfort with glare from light sources with a lower correlated color temperature, the results were generally inconclusive. This illustrates the urgent need to study the linkage between light source properties and subjective and objective glare measures in deeper detail, so that the technical norms governing car headlamps can reflect the needs of human physiology and psychophysiology.

Evaluating the Modulation of the Acoustic Startle Reflex in Children and Adolescents via Vertical EOG and EEG: Sex, Age, and Behavioral Effects.

Acoustic startle reflex (ASR) constitutes a reliable, cross-species indicator of sensorimotor and inhibitory mechanisms, showing distinct signature in cognitive aging, sex, and psychopathological characterization. ASR can be modulated by the prepulse inhibition (PPI) paradigm, which comprises the suppression of reactivity to a startling stimulus (pulse) following a weak prepulse (30- to 500-ms time difference), being widely linked to inhibitory capabilities of the sensorimotor system. If the prepulse–pulse tones are more clearly separated (500–2,000 ms), ASR amplitude is enhanced, termed as prepulse facilitation (PPF), reflecting sustained or selective attention. Our study aimed to investigate early-life sensorimotor sex/age differences using Electroencephalographic recordings to measure muscular and neural ASR in a healthy young population. Sixty-three children and adolescents aged 6.2–16.7 years (31 females) took part in the experiment. Neural ASR was assessed by two different analyses, namely, event-related potentials (ERPs) and first-derivative potentials (FDPs). As expected, PPF showed enhanced responses compared with PPI, as indicated by eyeblink, ERP and FDP measures, confirming the gating effect hypothesis. Sex-related differences were reflected in FDPs, with females showing higher ASR than males, suggesting increased levels of poststartle excitability. Intragroup age effects were evaluated via multipredictor regression models, noticing positive correlation between age versus eyeblink and ERP responses. Attention-related ERPs (N100 and P200) showed distinct patterns in PPI versus PPF, potentially indicative for alternative attentional allocation and block-out of sensory overload. Screening measures of participants’ neurodevelopmental (assessed by Wechsler Intelligence Scale for Children) and behavioral (assessed by Child Behavior Checklist) markers were also associated with increased N100/P200 responses, presumably indexing synergy between perceptual consistency, personality profiling, and inhibitory performance. Conclusively, modulation of ASR by PPI and PPF is associated with biological sex and internal/external personality traits in childhood and adolescence, potentially useful to guide symptomatology and prevention of psychopathology.

Persian emotion elicitation film set and signal database

Emotions are associated to human central and peripheral nervous system activity and visual stimulus has the potential to invoke emotions. In the field of emotion analysis and recognition, films are widely used due to their similarity to real-life situations and various film sets have been collected in different languages. In this work, we present the PEEFS (Persian Emotion Elicitation Film Set) collection. According to the authors’ knowledge, Persian Emotion Elicitation Film Set (PEEFS) is the first emotion elicitation Persian film set that has been collected and validated by an 88-person population using an online server-based website created in this work. We have watched 84 most-watched Iranian films to trim emotional segments, and the final set consists of 21 film clips, duration from 20 to 100 s, with discrete labeling based on six basic emotions and ratings in two dimensions of arousal and valence. These 21 film clips have the highest intensity at each target emotion. In addition, emotion scores of the film clips are also included. This database can be used to stimulate emotions and to design automatic emotion recognition systems at different levels. In this work, a signal database has been provided by recording 2 channels of forehead EEG (Fp1 and Fp2) and 8 peripheral signals that include ECG lead II, GSR, trapezius and zygomatic EMG (tEMG and zEMG), vertical EOG (vEOG), finger pulse, Respiratory signal and skin temperature, using the PEEFS film set and selected film clips from the previous English and French databases.

Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot.

People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user’s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.

An Electro-Oculogram Based Vision System for Grasp Assistive Devices-A Proof of Concept Study.

Humans typically fixate on objects before moving their arm to grasp the object. Patients with ALS disorder can also select the object with their intact eye movement, but are unable to move their limb due to the loss of voluntary muscle control. Though several research works have already achieved success in generating the correct grasp type from their brain measurement, we are still searching for fine controll over an object with a grasp assistive device (orthosis/exoskeleton/robotic arm). Object orientation and object width are two important parameters for controlling the wrist angle and the grasp aperture of the assistive device to replicate a human-like stable grasp. Vision systems are already evolved to measure the geometrical attributes of the object to control the grasp with a prosthetic hand. However, most of the existing vision systems are integrated with electromyography and require some amount of voluntary muscle movement to control the vision system. Due to that reason, those systems are not beneficial for the users with brain-controlled assistive devices. Here, we implemented a vision system which can be controlled through the human gaze. We measured the vertical and horizontal electrooculogram signals and controlled the pan and tilt of a cap-mounted webcam to keep the object of interest in focus and at the centre of the picture. A simple ‘signature’ extraction procedure was also utilized to reduce the algorithmic complexity and system storage capacity. The developed device has been tested with ten healthy participants. We approximated the object orientation and the size of the object and determined an appropriate wrist orientation angle and the grasp aperture size within 22 ms. The combined accuracy exceeded 75%. The integration of the proposed system with the brain-controlled grasp assistive device and increasing the number of grasps can offer more natural manoeuvring in grasp for ALS patients.

Evaluation of eyestrain with vertical electrooculogram

Background and objectiveEyestrain has been increasingly severe in our lives and works as the progress of computers and smartphones. Evaluating eyestrain helps to prevent and relieve eyestrain. Our study aimed to evaluate eyestrain by analyzing vertical electrooculogram (VEOG). Methods21 young subjects were asked to watch a video on the computer for a totally 120 minutes each, during which the VEOG signal was acquired using only three electrodes, and the questionnaire was answered every 30 minutes. The VEOG signal was divided into four 30-minute phases, from which VEOG signal power probability (VEOGSPP) features and blink features were extracted. The blink features include the changes of blink number (BN), group blinks number (GBN) and ratio (GBR), mean blink amplitude (Mean_BA) and duration (Mean_BD), mean blink duration at 50% (Mean_BD50), mean closing duration (Mean_CD) and opening duration (Mean_OD), mean opening duration at early 50% (Mean_ODE50) and late 50% (Mean_ODL50), mean blink maximum rising slope (Mean_BMRS) and falling slope (Mean_BMFS). ResultsThe results showed that the VEOGSPP in the high-frequency band (0.8-6.3Hz), BN, GBN, and GBR significantly increased while the VEOGSPP in the low-frequency band (0.1-0.4Hz), Mean_BA, Mean_OD, and Mean_ODL50 significantly decreased with eyestrain (P<0.05). ConclusionsIn conclusion, eyestrain induced by watching videos for a long time could be well evaluated by analyzing the VEOG signal.

Elimination of EOG signals from raw EEG signals using step size based recursive least squares- least mean fourth adaptive algorithm

A novel Step Size based Recursive least squares- Least mean fourth(SSRL) adaptive algorithm is proposed as a noise canceller, in this paper, to eliminate the Ocular artifacts(OAs) from the recorded raw EEG signals. Here, both second and fourth-order power optimization algorithms have been used to eliminate the OAs from raw EEG signals. The Reference signals, such as vertical Electrooculogram and horizontal Electrooculogram signals, have been recorded separately.FIR filter has been used to process the reference signals by updating filter coefficients using the proposed SSRL algorithm. Thereby, true EEG signals are obtained by subtracting the processed signals from the raw signals. Moreover, a mathematical model for the mean square deviation analysis is proposed for the SSRL algorithm, which provides better results compared to the conventional methods.

Analysis of vertical eye movements in Parkinson’s disease and its potential for diagnosis

It is well known that eye movements are highly affected by Parkinson’s disease. The majority of studies related to effects of Parkinson’s disease on eye movements have been performed for rapid eye movements during sleep. However, in the current study, eye movements during resting-state (eyes-open and eyes-closed conditions) were studied to evaluate Parkinson’s disease. To measure eye movements, vertical electrooculography (VEOG) was used. Using extracted features in time-, frequency-, and time-frequency domains of VEOG time-series, a classification analysis between healthy subjects and Parkinson’s disease patients in OFF- and ON-medication states was performed. The most-informative features for an error-correcting output codes support vector machine classifier were selected according to the multiple-comparison corrected p-values. VEOG data obtained 69.10 % and 87.27 % discrimination accuracy for OFF- and ON-medication states, respectively. Interestingly, higher discrimination was obtained for the lower frequency contents of VEOG time-series (0.1–1.25 Hz). The most discriminative features were related to the variation of amplitude and frequency content for OFF-medication, while in ON-medication the features according to the variation of VEOG amplitudes were removed from the most discriminative features list. Based on the obtained results, it was concluded that vertical eye movements of Parkinson’s disease patients had lower amplitude variation compared with healthy subjects in OFF-medication, while levodopa prescription increased such variation in vertical eye movements during the eyes-closed condition and decreased the variation during the eyes-open condition. Levodopa prescription possibly affects the amplitude variation of VEOG time-series, while it had no effect on the movement rates (frequency contents) of vertical eye movements.

Mixed step size normalized least mean fourth adaptive algorithm for artifact elimination from raw EEG signals

In this paper, a novel algorithm is proposed to eliminate the ocular artifacts (OAs) using mixed step size normalized least mean fourth adaptive algorithm from the raw EEG signals. Here, the fourth-order power optimization technique is used to eliminate the OAs effectively. The reference signals, such as vertical electrooculogram and horizontal electrooculogram, are recorded separately. Thereby, these signals are processed by the FIR filter, whose coefficients are adaptively updated at each iteration by using the proposed algorithm. The processed signals are subtracted from the raw EEG signals in order to obtain clean EEG signals. In addition, the mathematical model for the mean square deviation analysis is provided and compared with conventional methods like combined step size normalized least mean squares and variable parameter normalized mixed norm adaptive algorithm. From the simulation results, it is found that the proposed algorithm exhibits better performance in terms of minimum mean square deviation.

RECURRENCE QUANTIFICATION ANALYSIS OF ELECTROOCULOGRAPHY SIGNAL TO A CONTROL QUESTION TEST: A NEW APPROACH FOR THE DETECTION OF DECEPTION

This study aimed to evaluate a lie-detection system by nonlinear analysis of electrooculography (EOG) signals in the polygraph test. The physiological signals such as photoplethysmography signal, electrodermal response, respiratory changes as well as EOG signal were recorded based on a Control Question Test (CQT). Three psychophysiological signals were evaluated based on the extracted features in the seven-position numerical scoring. The dynamics of EOG signals in subjects that had a total negative score were analyzed by recurrence quantification analysis (RQA). The six values of RQA were calculated to analyze the EOG signals in relevant questions compared to other questions. A one-way ANOVA with multiple comparisons was performed to evaluate the extracted variables in different questions. Eleven subjects had a total score of [Formula: see text]2 and less, so the EOG signals of these subjects were evaluated. Recurrence plots (RPs) of EOG signals showed clear differences in the two types of questions. The recurrence quantification analysis of vertical EOG signal indicated that [Formula: see text] and determinism (DET) values decreased significantly for relevant questions compared to other questions. Moreover, a significant decrease was observed in all RQA parameters except RR for the horizontal EOG signal. The differences of EOG signals in relevant questions observed using RPs and RQA were possibly related to the underlying changes in rapid eye movement due to the stress. The results of this study illustrate that the RQA technique is well suited to analyze the EOG signals in the detection of deception.

Objective assessment of blinking and facial expressions in Parkinson’s disease using a vertical electro-oculogram and facial surface electromyography

Objective: Hypomimia is a common and early symptom of Parkinson’s disease (PD), which reduces the ability of PD patients to manifest emotions. Currently, it is visually evaluated by the neurologist during neurological examinations for PD diagnosis, as described in task 3.2 of the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Since such an evaluation is semi-quantitative and affected by inter-variability, this paper aims to measure the physiological parameters related to eye blink and facial expressions extracted from a vertical electro-oculogram (VEOG) and facial surface electromyography (fsEMG) to differentiate PD patients from healthy control subjects (HCs). Approach: The spontaneous eye blink rate-minute (sEBR), its maximum amplitude (BMP), and facial cutaneous muscle activity were measured in 24 PD patients and 24 HCs while the subjects looked at a visual-tester composed of three main parts: static vision, dynamic vision and reading silently. Specificity and sensitivity for each parameter were calculated. Main results: The VEOG and the fsEMG allowed the identification of some parameters related to eye blink and facial expressions (i.e. sEBR, BMP, frontal and peribuccal muscular activities), being able to distinguish between PD patients and HCs with high sensitivity and specificity. Significance: The demonstration that the combination of parameters related to eye blink and facial expressions can discriminate (with high accuracy) between PD patients versus HCs, thus resulting in a useful tool to support the neurologist in objective assessment of hypomimia for improving PD diagnosis.

Driving Fatigue Classification Based on Fusion Entropy Analysis Combining EOG and EEG

The rising number of traffic accidents has become a major issue in our daily life, which has attracted the concern of society and governments. To deal with this issue, in our previous study, we have designed a real-time driving fatigue detection system using power spectrum density and sample entropy. By using the wireless technology and dry electrodes for EEG collection, we further integrated virtual reality simulated driving environment, which made our study more applicable to realistic settings. However, the high accuracy of classification for driving fatigue has not been obtained. To measure the time series complexity of the EEG signal, we proposed a fusion entropy (sample entropy, approximate entropy, and spectral entropy) analysis method of EEG and EOG. First, a sample entropy was applied for feature extraction from the horizontal and vertical EOG. Second, an approximate entropy, sample entropy, and spectral entropy features of each sub-band of EEG are extracted. Third, feature fusion for sub-band is performed by canonical correlation analysis (CCA). Finally, the features of EOG and EEG are classified using a relevant vector machine (RVM). Twenty-two subjects participated in the driving fatigue experiments for a duration of 90 min. The results demonstrated that the fusion entropy analysis combining EOG and EEG could provide an alternative method for driving fatigue detection, and the average accuracy rate was up to 99.1 ± 1.2%. The authors further analyzed the effect of feature fusion in four sub-bands (δ, α, β and θ) and compared with every single sub-band on classification performance, it is proved that the former is superior to the latter presenting the proposed method can provide effective indicators for driving fatigue detection.

Eye activity tracks task-relevant structures during speech and auditory sequence perception

The sensory and motor systems jointly contribute to complex behaviors, but whether motor systems are involved in high-order perceptual tasks such as speech and auditory comprehension remain debated. Here, we show that ocular muscle activity is synchronized to mentally constructed sentences during speech listening, in the absence of any sentence-related visual or prosodic cue. Ocular tracking of sentences is observed in the vertical electrooculogram (EOG), whether the eyes are open or closed, and in eye blinks measured by eyetracking. Critically, the phase of sentence-tracking ocular activity is strongly modulated by temporal attention, i.e., which word in a sentence is attended. Ocular activity also tracks high-level structures in non-linguistic auditory and visual sequences, and captures rapid fluctuations in temporal attention. Ocular tracking of non-visual rhythms possibly reflects global neural entrainment to task-relevant temporal structures across sensory and motor areas, which could serve to implement temporal attention and coordinate cortical networks.