An automatic algorithm for blink-artifact suppression based on iterative template matching: application to single channel recording of cortical auditory evoked potentials

Psychoacoustic and electroencephalographic responses to changes in amplitude modulation depth and frequency in relation to speech recognition in cochlear implantees

Temporal envelope modulations (TEMs) are one of the most important features that cochlear implant (CI) users rely on to understand speech. Electroencephalographic assessment of TEM encoding could help clinicians to predict speech recognition more objectively, even in patients unable to provide active feedback. The acoustic change complex (ACC) and the auditory steady-state response (ASSR) evoked by low-frequency amplitude-modulated pulse trains can be used to assess TEM encoding with electrical stimulation of individual CI electrodes. In this study, we focused on amplitude modulation detection (AMD) and amplitude modulation frequency discrimination (AMFD) with stimulation of a basal versus an apical electrode. In twelve adult CI users, we (a) assessed behavioral AMFD thresholds and (b) recorded cortical auditory evoked potentials (CAEPs), AMD-ACC, AMFD-ACC, and ASSR in a combined 3-stimulus paradigm. We found that the electrophysiological responses were significantly higher for apical than for basal stimulation. Peak amplitudes of AMFD-ACC were small and (therefore) did not correlate with speech-in-noise recognition. We found significant correlations between speech-in-noise recognition and (a) behavioral AMFD thresholds and (b) AMD-ACC peak amplitudes. AMD and AMFD hold potential to develop a clinically applicable tool for assessing TEM encoding to predict speech recognition in CI users.

OVME-REG: Harris hawks optimization algorithm based optimized variational mode extraction for eye blink artifact removal from EEG signal

The electroencephalogram (EEG) recordings from the human brain are useful for detecting various brain syndromes. These recordings are typically contaminated by high amplitude eye blink artifacts, which leads to deliberate misinterpretation of the EEG signal. Recently, variational mode extraction (VME) has been used to detect eye blink artifacts. But, the VME performance is impacted by the balancing parameter and center frequency selection. Therefore, this research uses two metaheuristic algorithms, particle swarm optimization and Harris hawks optimization, to determine the optimal set of the VME parameters. In the proposed method, the optimized VME (OVME) extracts the desired mode to locate the eye blink artifactual intervals. Then, the regression analysis (REG) filters the identified artifactual intervals from short EEG data segments. The significance of the proposed OVME-REG algorithm is that it is adequate for determining the optimum values of the VME algorithm. The analysis is carried out on the CHB-MIT Scalp EEG, BCI Competition, and EEG motor movement/imagery datasets. The proposed OVME-REG method provides an improved performance for suppressing single and repeated eye blink artifacts as compared to the current approaches in terms of (a) high correlation coefficient (93.08%, 87.3%, 82.17%), respectively, (b) low value of RRMSE (0.379, 0.506, 0.502), respectively, (c) high SSIM (0.892, 0.842, 0.694), and (d) low computation time and better preservation of the EEG data.

The P300 Auditory Evoked Potential: A Physiological Measure of the Engagement of Cognitive Systems Contributing to Listening Effort?

OBJECTIVES

This study aimed to explore the potential of the P300 (P3b) as a physiological measure of the engagement of cognitive systems contributing to listening effort.

DESIGN

Nineteen right-handed young adults (mean age: 24.79 years) and 20 right-handed older adults (mean age: 58.90 years) with age-appropriate hearing were included. The P300 was recorded at Fz, Cz, and Pz using a two-stimulus oddball paradigm with the Flemish monosyllabic numbers "one" and "three" as standard and deviant stimuli, respectively. This oddball paradigm was conducted in three listening conditions, varying in listening demand: one quiet and two noisy listening conditions (+4 and -2 dB signal to noise ratio [SNR]). At each listening condition, physiological, behavioral, and subjective tests of listening effort were administered. P300 amplitude and latency served as a potential physiological measure of the engagement of cognitive systems contributing to listening effort. In addition, the mean reaction time to respond to the deviant stimuli was used as a behavioral listening effort measurement. Last, subjective listening effort was administered through a visual analog scale. To assess the effects of listening condition and age group on each of these measures, linear mixed models were conducted. Correlation coefficients were calculated to determine the relationship between the physiological, behavioral, and subjective measures.

RESULTS

P300 amplitude and latency, mean reaction time, and subjective scores significantly increased as the listening condition became more taxing. Moreover, a significant group effect was found for all physiological, behavioral, and subjective measures, favoring young adults. Last, no clear relationships between the physiological, behavioral, and subjective measures were found.

CONCLUSIONS

The P300 was considered a physiological measure of the engagement of cognitive systems contributing to listening effort. Because advancing age is associated with hearing loss and cognitive decline, more research is needed on the effects of all these variables on the P300 to further explore its usefulness as a listening effort measurement for research and clinical purposes.

Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density

Similar to short-term memory, working memory cannot hold information for a long period of time. Studies have shown that binaural beats (BB) can stimulate the brain through sound, affecting working memory function. Although the literature is not conclusive regarding the effects of BB stimulation (stim) on memory, some studies have shown that gamma-BB stim (40 Hz) can increase attentional focusing and improve visual working memory. To better understand the relationship between BB stim and memory, we collected electroencephalographic data (EEG) from 30 subjects in 3 phases—a baseline, with gamma-BB stim, and control stim—in a rest state, with eyes closed, and while performing memory tasks. Both EEG data and memory task performance were analyzed. The results showed no significant changes in the memory task performance or the EEG data when comparing experimental and control conditions. We concluded that brain entrainment was not achieved with our parameters of gamma-BB stimulation when analyzing EEG power spectral density (PSD) and memory task performance. Hence, we suggest that other aspects of EEG data, such as connectivity and correlations with task performance, should also be analyzed for future studies.

Links between excessive daytime sleepiness and EEG power and activation in two subtypes of ADHD

OBJECTIVES

This study aimed to replicate previously reported EEG characteristics between typically developing (TD) children and two subtypes of Attention Deficit Hyperactivity Disorder (ADHD) using a frontal, single-channel, dry-sensor portable EEG device, and explore whether differences are moderated by excessive daytime sleepiness (EDS).

METHODS

Children with ADHD Inattentive (ADHD-I) and ADHD Combined presentation (ADHD-C) and typically-developing (TD) children (N = 34 in each group) had frontal EEG recorded during eyes-closed resting, eyes-open resting, and focus tasks. Participants also completed the Children's Self-Report Sleep Patterns - Sleepiness Scale as a measure of EDS.

RESULTS

Consistent with previous literature, there were increases in frontal delta and theta power in the ADHD-C compared to ADHD-I and TD groups, in all conditions. Novel power and activation effects in ADHD subtypes, as well as significant group and EDS interactions for alpha and beta power were also found.

CONCLUSIONS

These findings highlight the importance of considering ADHD subtypes and EDS when exploring EEG characteristics, and have important implications for the diagnosis and treatment of children with ADHD.

Subspace-constrained deconvolution of auditory evoked potentials

Auditory evoked potentials can be estimated by synchronous averaging when the responses to the individual stimuli are not overlapped. However, when the response duration exceeds the inter-stimulus interval, a deconvolution procedure is necessary to obtain the transient response. The iterative randomized stimulation and averaging and the equivalent randomized stimulation with least squares deconvolution have been proven to be flexible and efficient methods for deconvolving the evoked potentials, with minimum restrictions in the design of stimulation sequences. Recently, a latency-dependent filtering and down-sampling (LDFDS) methodology was proposed for optimal filtering and dimensionality reduction, which is particularly useful when the evoked potentials involve the complete auditory pathway response (i.e., from the cochlea to the auditory cortex). In this case, the number of samples required to accurately represent the evoked potentials can be reduced from several thousand (with conventional sampling) to around 120. In this article, we propose to perform the deconvolution in the reduced representation space defined by LDFDS and present the mathematical foundation of the subspace-constrained deconvolution. Under the assumption that the evoked response is appropriately represented in the reduced representation space, the proposed deconvolution provides an optimal least squares estimation of the evoked response. Additionally, the dimensionality reduction provides a substantial reduction of the computational cost associated with the deconvolution. matlab/Octave code implementing the proposed procedures is included as supplementary material.

Multi-dimensional Feature Optimization based Eye Blink Detection under Epileptiform Discharges

OBJECTIVES

Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical.

METHODS

In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multi-dimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection.

RESULTS

Experiments on EEGs of 16 subjects from the Children's Hospital of Zhejiang University School of Medicine (CHZU) show that our method achieves the highest average sensitivity, specificity and accuracy of 95.04, 89.52, and 93.01, respectively. That outperforms 5 recent and state-of-the-art (SOTA) eye blink detection algorithms.

SIGNIFICANCE

The proposed method is robust in eye blink artifact detection for EEGs containing high-frequency epileptiform discharges. It is also effective in dealing with individual differences in EEGs, which is usually ignored in conventional methods.

Design and Evaluation of a Custom-Made Electromyographic Biofeedback System for Facial Rehabilitation

Background

In the rehabilitation of postoperative facial palsy, physical therapy is of paramount importance. However, in the early rehabilitation phase, voluntary movements are often limited, and thus, the motivation of patients is impacted. In these situations, biofeedback of facial electromyographic (EMG) signals enables the visual representation of the rehabilitation progress, even without apparent facial movements. In the present study, we designed and evaluated a custom-made EMG biofeedback system enabling cost-effective facial rehabilitation.

Methods

This prospective study describes a custom-made EMG system, consisting of a microcontroller board and muscle sensors, which was used to record the EMG of frontal and zygomatic facial muscles during frowning and smiling. First, the mean EMG amplitudes and movement onset detection rates (ACC) achieved with the custom-made EMG system were compared with a commercial EMG device in 12 healthy subjects. Subsequently, the custom-made device was applied to 12 patients with and without postoperative facial paresis after neurosurgical intervention. Here, the ratio [laterality index (LI)] between the mean EMG amplitude of the healthy and affected side was calculated and related to the facial function as measured by the House and Brackmann scale (H&B) ranging from 1 (normal) to 6 (total paralysis).

Results

In healthy subjects, a good correlation was measured between the mean EMG amplitudes of the custom-made and commercial EMG device for both frontal (r = 0.84, p = 0.001) and zygomatic muscles (r = 0.8, p = 0.002). In patients, the LI of the frontal and zygomatic muscles correlated significantly with the H&B (r = −0.83, p = 0.001 and r = −0.65, p = 0.023). The ACC of the custom-made EMG system varied between 65 and 79% depending on the recorded muscle and cohort.

Conclusion

The present study demonstrates a good application potential of our custom-made EMG biofeedback device to detect facial EMG activity in healthy subjects as well as patients with facial palsies. There is a correlation between the electrophysiological measurements and the clinical outcome. Such a device might enable cost-efficient home-based facial EMG biofeedback. However, movement detection accuracy should be improved in future studies to reach ranges of commercial devices.

Novel approach to remove Electrical Shift and Linear Trend artifact from single channel EEG

Electroencephalogram (EEG) signals are crucial to Brain-Computer Interfacing (BCI). However, these are vulnerable to a variety of unintended artifacts that could negatively impact the precise brain function assessment. This paper provides a new algorithm to eliminate Electrical Shift and Linear Trend artifact (ESLT) in EEG using Singular Spectrum Analysis (SSA) and Enhanced local Polynomial (LP) Approximation-based Total Variation (EPATV). The contaminated single channel EEG is subdivided into multiple bands of frequency components by SSA. In order to acquire all LP and TV components, EPATV filtering is applied over the contaminated component frequency band. Filtered sub-signal is collected by subtracting both the LP and TV components from the component contaminated frequency band. Then, the addition of filtered sub-signal and remaining SSA frequency band components yield the final denoised EEG signal. The effectiveness of the proposed method in this paper is evaluated using the data obtained from three databases and compared with the existing methods. From the extensive simulation results, it is inferred that the algorithm discussed in the paper is effective when compared the existing methods, exhibiting a highest averaged Correlation Coefficient (CC) of 0.9534, averaged Signal to Noise Ratio (SNR) of 10.2208dB, lowest averaged Relative Root Mean Square Error (RRMSE) value 0.2787 and averaged Mean absolute Error (MAE) inαband value of 0.0557. The algorithm presented in this paper may be a viable choice for extracting ESLT artifact from a small streaming section of the EEG without requirement of the initial calibration or enormous EEG data.

Unsupervised Eye Blink Artifact Detection From EEG With Gaussian Mixture Model

Eye blink is one of the most common artifacts in electroencephalogram (EEG) and significantly affects the performance of the EEG related applications, such as epilepsy recognition, spike detection, encephalitis diagnosis, etc. To achieve an accurate and efficient eye blink detection, a novel unsupervised learning algorithm based on a hybrid thresholding followed with a Gaussian mixture model (GMM) is presented in this paper. The EEG signal is priliminarily screened by a cascaded thresholding method built on the distributions of signal amplitude, amplitude displacement, as well as the cross channel correlation. Then, the channel correlation of the two frontal electrodes (FP1, FP2), the fractal dimension, and the mean of amplitude difference between FP1 and FP2, are extracted to characterize the filtered EEGs. The GMM trained on these features is applied for the eye blink detection. The performance of the proposed algorithm is studied on two EEG datasets collected by the Temple University Hospital (TUH) and the Children's Hospital, Zhejiang University School of Medicine (CHZU), where the datasets are recorded from epilepsy and encephalitis patients, and contain a lot of eye blink artifacts. Experimental results show that the proposed algorithm can achieve the highest detection precision and F1 score over the state-of-the-art methods.

Simultaneous Eye Blink Characterization and Elimination from Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection

OBJECTIVE

Blink-related features derived from electroencephalography (EEG) have recently arisen as a meaningful measure of drivers cognitive state. Combined with band power features of low-channel prefrontal EEG data, blink-derived features enhance the detection of driver drowsiness. Yet, it remains unanswered whether synergy of combined blink and EEG band power features for the detection of driver drowsiness may be further boosted if a proper eye blink removal is also applied before EEG analysis. This paper proposes an algorithm for simultaneous eye blink feature extraction and elimination from low-channel prefrontal EEG data.

METHODS

Firstly, eye blink intervals (EBIs) are identified from the Fp1 EEG channel using variational mode extraction, and then blink-related features are derived. Secondly, the identified EBIs are projected to the rest of EEG channels and then filtered by a combination of principal component analysis and discrete wavelet transform. Thirdly, a support vector machine with 10-fold cross-validation is employed to classify alert and drowsy states from the derived blink and filtered EEG band power features.

MAIN RESULTS

When compared the synergy of eye blink and EEG features before and after filtering by the proposed algorithm, a significant improvement in the mean accuracy of driver drowsiness detection was achieved (71.2% vs. 78.1%, p<0.05).

SIGNIFICANCE

This paper validates a novel view of eye blinks as both a source of information and artifacts in EEG-based driver drowsiness detection.

Development of Frontal EEG Differences Between Eyes-Closed and Eyes-Open Resting Conditions in Children: Data From a Single-Channel Dry-Sensor Portable Device

Changes in EEG when moving from an eyes-closed to an eyes-open resting condition result from bottom-up sensory processing and have been referred to as activation. In children, activation is characterized by a global reduction in alpha, frontally present reductions for delta and theta, and a frontal increase for beta. The present study aimed to replicate frontal EEG activation effects using single-channel, dry-sensor EEG, and to extend current understanding by examining developmental change in children. Frontal EEG was recorded using a single-channel, dry-sensor EEG device while 182 children aged 7 to 12 years completed eyes-closed resting (EC), eyes-open resting (EO), and focus (FO) tasks. Results indicated that frontal delta, theta, and alpha power were reduced, and frontal beta power was increased, in the EO compared with the EC condition. Exploratory analysis of a form of top-down activation showed that frontal beta power was increased in the FO compared with to the EO condition, with no differences for other bands. The activation effects were robust at the individual level. The bottom-up activation effects reduced with age for frontal delta and theta, increased for frontal alpha, with no developmental change for top-down or bottom-up frontal beta activation. These findings contribute further to validation of the single-channel, dry-sensor, frontal EEG and provide support for use in a range of medical, therapeutic, and clinical domains.

Characterizing Cochlear implant artefact removal from EEG recordings using a real human model

Electroencephography (EEG) recordings from CI listeners are contaminated by electrical artefacts that make it difficult to extract neural responses. Previously, we have removed these artefacts by means of interpolation and spatial filtering. However, the extent to which this method can effectively reduce electrical artefacts has not been fully investigated. Here, we assessed the effectiveness of interpolation and spatial filtering to remove electrical artefacts using recordings from a human head specimen implanted with a CI.•Electrical artefacts were obtained using amplitude-modulated (AM'ed) pulse trains presented at several pulse rates (100-to-902 pps) or using high rate pulse trains (902 pps) in which either a pair of electrodes or AM frequencies alternated periodically at a rate of 1Hz.•By adding auditory change complex (ACC), auditory steady-state response (ASSR), or auditory change following response (AC-FR) template waveforms to the contaminated recordings, we show that interpolation allows for effective artefact removal for pulse rates below 400 pps whilst interpolation and spatial filtering are effective at higher pulse rates, with minimal distortions for ACC and AC-FR, and with a degree of amplitude- and phase-distortions for ASSR.•Recordings from CI listeners agreed with simulations, demonstrating that reliable responses can be recovered.

Aiding diagnosis of childhood attention-deficit/hyperactivity disorder of the inattentive presentation: Discriminant function analysis of multi-domain measures including EEG

INTRODUCTION

We developed a neurocognitive assessment tool (NCAT) in consultation with mental health professionals working with children with AD/HD as a diagnostic aid and screening tool. This study examines the predictive utility of NCAT in the classification of children with AD/HD Inattentive presentation.

METHOD

Fifty three children with AD/HD Inattentive presentation and 161 typically-developing children completed an NCAT assessment. Discriminant function analyses examined group membership prediction for separate components of NCAT and for the components combined.

RESULTS

The combined model correctly classified 93.4 % of participants, with 91.4 % sensitivity and 93.9 % specificity. Contributions to classification were from SNAP-IV, psychological needs satisfaction, self-regulation, executive function performance, and EEG. The combined model resulted in a 9.3 % increase in specificity and 5.9 % increase in sensitivity compared to SNAP-IV alone.

CONCLUSIONS

NCAT provides good discrimination between children with and without AD/HD of the Inattentive presentation, and further investigation including other subtypes and comorbidities is warranted.

VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel

OBJECTIVE

Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel.

METHOD

The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel.

RESULTS

The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from -8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87).

SIGNIFICANCE

The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference.

Neural encoding of spectro-temporal cues at slow and near speech-rate in cochlear implant users

The ability to process rapid modulations in the spectro-temporal structure of sounds is critical for speech comprehension. For users of cochlear implants (CIs), spectral cues in speech are conveyed by differential stimulation of electrode contacts along the cochlea, and temporal cues in terms of the amplitude of stimulating electrical pulses, which track the amplitude-modulated (AM'ed) envelope of speech sounds. Whilst survival of inner-ear neurons and spread of electrical current are known factors that limit the representation of speech information in CI listeners, limitations in the neural representation of dynamic spectro-temporal cues common to speech are also likely to play a role. We assessed the ability of CI listeners to process spectro-temporal cues varying at rates typically present in human speech. Employing an auditory change complex (ACC) paradigm, and a slow (0.5Hz) alternating rate between stimulating electrodes, or different AM frequencies, to evoke a transient cortical ACC, we demonstrate that CI listeners-like normal-hearing listeners-are sensitive to transitions in the spectral- and temporal-domain. However, CI listeners showed impaired cortical responses when either spectral or temporal cues were alternated at faster, speech-like (6-7Hz), rates. Specifically, auditory change following responses-reliably obtained in normal-hearing listeners-were small or absent in CI users, indicating that cortical adaptation to alternating cues at speech-like rates is stronger under electrical stimulation. In CI listeners, temporal processing was also influenced by the polarity-behaviourally-and rate of presentation of electrical pulses-both neurally and behaviorally. Limitations in the ability to process dynamic spectro-temporal cues will likely impact speech comprehension in CI users.

Efficient Detection of Cortical Auditory Evoked Potentials in Adults Using Bootstrapped Methods

BACKGROUND

Statistical detection methods are useful tools for assisting clinicians with cortical auditory evoked potential (CAEP) detection, and can help improve the overall efficiency and reliability of the test. However, many of these detection methods rely on parametric distributions when evaluating test significance, and thus make various assumptions regarding the electroencephalogram (EEG) data. When these assumptions are violated, reduced test sensitivities and/or increased or decreased false-positive rates can be expected. As an alternative to the parametric approach, test significance can be evaluated using a bootstrap, which does not require some of the aforementioned assumptions. Bootstrapping also permits a large amount of freedom when choosing or designing the statistical test for response detection, as the distributions underlying the test statistic no longer need to be known prior to the test.

OBJECTIVES

To improve the reliability and efficiency of CAEP-related applications by improving the specificity and sensitivity of objective CAEP detection methods.

DESIGN

The methods included in the assessment were Hotelling's T2 test, the Fmp, four modified q-sample statistics, and various template-based detection methods (calculated between the ensemble coherent average and some predefined template), including the correlation coefficient, covariance, and dynamic time-warping (DTW). The assessment was carried out using both simulations and a CAEP threshold series collected from 23 adults with normal hearing.

RESULTS

The most sensitive method was DTW, evaluated using the bootstrap, with maximum increases in test sensitivity (relative to the conventional Hotelling's T2 test) of up to 30%. An important factor underlying the performance of DTW is that the template adopted for the analysis correlates well with the subjects' CAEP.

CONCLUSION

When subjects' CAEP morphology is approximately known before the test, then the DTW algorithm provides a highly sensitive method for CAEP detection.

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

OBJECTIVE

When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance.

APPROACH

A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact.

MAIN RESULTS

The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset.

SIGNIFICANCE

This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.

Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785 EEG sequences contaminated by artifacts and 785 artifact-free EEG sequences collected from 15 intensive care patients. The obtained results showed an overall accuracy of 0.98, representing high reliability of proposed technique in detecting different types of artifacts and being comparable or outperforming the approaches proposed earlier in the literature.

Latency-dependent filtering and compact representation of the complete auditory pathway response

Auditory evoked potentials (AEPs) include the auditory brainstem response (ABR), middle latency response (MLR), and cortical auditory evoked potentials (CAEPs), each one covering a specific latency range and frequency band. For this reason, ABR, MLR, and CAEP are usually recorded separately using different protocols. This article proposes a procedure providing a latency-dependent filtering and down-sampling of the AEP responses. This way, each AEP component is appropriately filtered, according to its latency, and the complete auditory pathway response is conveniently represented (with the minimum number of samples, i.e., without unnecessary redundancies). The compact representation of the complete response facilitates a comprehensive analysis of the evoked potentials (keeping the natural continuity related to the neural activity transmission along the auditory pathway), which provides a new perspective in the design and analysis of AEP experiments. Additionally, the proposed compact representation reduces the storage or transmission requirements when large databases are manipulated for clinical or research purposes. The analysis of the AEP responses shows that a compact representation with 40 samples/decade (around 120 samples) is enough for accurately representing the response of the complete auditory pathway and provides appropriate latency-dependent filtering. MatLab/Octave code implementing the proposed procedure is included in the supplementary materials.

Design and evaluation of the effectiveness of a corpus of congruent and incongruent English sentences for the study of event related potentials

OBJECTIVE

To design and evaluate the effectiveness of a stimulus material in eliciting the N400 event related potential (ERP).

DESIGN

A set of 700 semantically congruent and incongruent sentences was developed in accordance with current linguistic norms, and validated with an electroencephalography (EEG) study, in which the influence of age and gender on the N400 ERP magnitude was analysed.

STUDY SAMPLE

Forty-five normal-hearing subjects (19-57 years, 21 females) participated in the EEG study.

RESULTS

The stimulus material used in the EEG study elicited a robust N400 ERP, with a morphology consistent with the literature. Results also showed no statistically significant effect of age or gender on the N400 magnitude.

CONCLUSIONS

The material presented in this paper constitutes the largest complete stimulus set suitable for both auditory and text-based N400 experiments. This material may help facilitate the efficient implementation of future N400 ERP studies, as well as promote standardisation and consistency across studies.

Matrix-based formulation of the iterative randomized stimulation and averaging method for recording evoked potentials

The iterative randomized stimulation and averaging (IRSA) method was proposed for recording evoked potentials when the individual responses are overlapped. The main inconvenience of IRSA is its computational cost, associated with a large number of iterations required for recovering the evoked potentials and the computation required for each iteration [involving the whole electroencephalogram (EEG)]. This article proposes a matrix-based formulation of IRSA, which is mathematically equivalent and saves computational load (because each iteration involves just a segment with the length of the response, instead of the whole EEG). Additionally, it presents an analysis of convergence that demonstrates that IRSA converges to the least-squares (LS) deconvolution. Based on the convergence analysis, some optimizations for the IRSA algorithm are proposed. Experimental results (configured for obtaining the full-range auditory evoked potentials) show the mathematical equivalence of the different IRSA implementations and the LS-deconvolution and compare the respective computational costs of these implementations under different conditions. The proposed optimizations allow the practical use of IRSA for many clinical and research applications and provide a reduction of the computational cost, very important with respect to the conventional IRSA, and moderate with respect to the LS-deconvolution. matlab/Octave implementations of the different methods are provided as supplementary material.

Mathematical Modeling of EEG Signals-Based Brain-Control Behavior

Brain-control behaviors (BCBs) are behaviors of humans that communicate with external devices by means of the human brain rather than peripheral nerves or muscles. In this paper, to understand and simulate such behaviors, we propose a mathematical model by combining a queuing network-based encoding model with a brain-computer interface model. Experimental results under the static tests show the effectiveness of the proposed model in simulating real BCBs. Furthermore, we verify the effectiveness and applicability of the proposed model through the dynamic experimental tests in a simulated vehicle. This paper not only promotes the understanding and prediction of BCBs, but also provides some insights into assistive technology on brain-controlled systems and extends the scope of research on human behavior modeling.