Physiological Artifacts Research Articles

Identification of ocular artifact in EEG signals using VMD and Hurst exponent.

Electroencephalographic (EEG) readings are usually infected with unavoidable artifacts, especially physiological ones. One such physiological artifact is the ocular artifacts (OAs) that are generally related to eyes and are characterized by high magnitude and a specific spike pattern in the prefrontal region of the brain. During the long-duration EEG acquisition, the retrieval of important information becomes quite complicated in prefrontal regions as ocular artifacts dominate the EEG recorded, making it difficult to discern underlying brain activity. With the progress and development in signal processing techniques, artifact handling has become a progressive field of investigation. This paper presents a framework for the detection and correction of ocular artifacts. This study emphasizes improving the quality and reducing the time complexity by using higher-order statistics (HOS) for artifact identification and variational mode decomposition (VMD) for OA correction. An overall SNR of 14 dB, MAE of 0.09, and PSNR of 33.59 dB has been attained by the proposed framework. It was observed that the proposed HOS-VMD surpassed the state-of-the-art mode decomposition techniques.

Estimation of Interferences in Magnetoencephalography (MEG) Brain Data Using Intelligent Methods for BCI-based Neurorehabilitation Applications

<p>Brain-Computer Interface (BCI) neurorehabilitation offers the potential to improve recovery and quality of life for stroke survivors. It aims to restore lost physical and mental abilities through motor and cognitive therapies. Magnetoencephalography (MEG) signals are a major advancement in BCI technology as they provide accurate and consistent assessments of brain activity for control and interaction applications. MEG is indispensable for recording the magnetic fields produced in the brain during motor imagery tasks due to its capability to evaluate cerebral activity with remarkable temporal resolution. However, one of the major challenges associated with MEG recording is the loss of signal quality due to physiological artifacts and ambient noise. Additionally, the head movement of the individual during the recording process can result in the introduction of artifacts into the recorded data, which can distort the spatial mapping of brain activity. This, in turn, can jeopardize the reliability and accuracy of the results obtained. This study aims to identify the most effective technique for removing artifacts from MEG signals by conducting a comparative performance analysis of prominent denoising algorithms, such as Infomax, FastICA, SOBI, and SWT. The findings conclude that Infomax is the most effective algorithm for removing physiological artifacts from a signal while maintaining the integrity and essential features of the original data. FastICA was found to be the second most effective algorithm. Infomax outperformed FastICA in Power Spectral Density (PSD) and Percentage Root mean square error Difference (PRD) measurements.</p>

Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

Abstract Intrinsic neural timescales (INT) reflect the time window of neural integration within a brain region and can be measured via resting-state fMRI (rs-fMRI). Despite the potential relevance of INT to cognition, brain organization, and neuropsychiatric illness, the influences of physiological artifacts on rs-fMRI INT have not been systematically considered. Two artifacts, head motion and respiration, pose serious issues in rs-fMRI studies. Here, we described their impact on INT estimation and tested the ability of two denoising strategies for mitigating these artifacts, high-motion frame censoring and global signal regression (GSR). We used a subset of the HCP Young Adult dataset with runs annotated for breathing patterns (Lynch et al., 2020) and at least one “clean” (reference) run that had minimal head motion and no respiration artifacts; other runs from the same participants (n = 46) were labeled as “non-clean.” We found that non-clean runs exhibited brain-wide increases in INT compared to their respective clean runs and that the magnitude of error in INT between non-clean and clean runs correlated with the amount of head motion. Importantly, effect sizes were comparable to INT effects reported in the clinical literature. GSR and high-motion frame censoring improved the similarity between INT maps from non-clean runs and their respective clean run. Using a pseudo-random frame-censoring approach, we uncovered a relationship between the amount of censored frames and both the mean INT and mean error, suggesting that frame censoring itself biases INT estimation. A group-level correction procedure reduced this bias and improved similarity between non-clean runs and their respective clean run. Based on our findings, we offer recommendations for rs-fMRI INT studies, which include implementing GSR and high-motion frame censoring with Lomb-Scargle interpolation of censored data, and performing group-level correction of the bias introduced by frame censoring.

Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework

Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets.

Speech synthesis from three-axis accelerometer signals using conformer-based deep neural network

Silent speech interfaces (SSIs) have emerged as innovative non-acoustic communication methods, and our previous study demonstrated the significant potential of three-axis accelerometer-based SSIs to identify silently spoken words with high classification accuracy. The developed accelerometer-based SSI with only four accelerometers and a small training dataset outperformed a conventional surface electromyography (sEMG)-based SSI. In this study, motivated by the promising initial results, we investigated the feasibility of synthesizing spoken speech from three-axis accelerometer signals. This exploration aimed to assess the potential of accelerometer-based SSIs for practical silent communication applications. Nineteen healthy individuals participated in our experiments. Five accelerometers were attached to the face to acquire speech-related facial movements while the participants read 270 Korean sentences aloud. For the speech synthesis, we used a convolution-augmented Transformer (Conformer)-based deep neural network model to convert the accelerometer signals into a Mel spectrogram, from which an audio waveform was synthesized using HiFi-GAN. To evaluate the quality of the generated Mel spectrograms, ten-fold cross-validation was performed, and the Mel cepstral distortion (MCD) was chosen as the evaluation metric. As a result, an average MCD of 5.03 ± 0.65 was achieved using four optimized accelerometers based on our previous study. Furthermore, the quality of generated Mel spectrograms was significantly enhanced by adding one more accelerometer attached under the chin, achieving an average MCD of 4.86 ± 0.65 (p < 0.001, Wilcoxon signed-rank test). Although an objective comparison is difficult, these results surpass those obtained using conventional SSIs based on sEMG, electromagnetic articulography, and electropalatography with the fewest sensors and a similar or smaller number of sentences to train the model. Our proposed approach will contribute to the widespread adoption of accelerometer-based SSIs, leveraging the advantages of accelerometers like low power consumption, invulnerability to physiological artifacts, and high portability.

Machine learning of brain-specific biomarkers from EEG

Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS). We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses. Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals. We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML. All authors have been working for F. Hoffmann-La Roche Ltd.

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial-temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.

Respiration recording for fMRI: breathing belt versus spine coil sensor

Abstract Physiological signals such as pulse and respiration strongly contribute to non-neuronal signal change of the blood oxygenation level-dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI). This has been observed not only during task-based but also during resting-state fMRI measurements, where the confounding influence of physiological signals is most pronounced. Over the last decades, a variety of techniques evolved, aiming at detecting and removing physiological artifacts in fMRI time series. These follow either a solely data-driven approach or rely on externally recorded physiological data. To record cardiac and respiratory signals, typically pulse oximetry or electrocardiography (ECG) and a respiration belt are used, respectively. New technologies allow to capture respiratory signal directly with a sensor placed within the spine coil in the patient table, eliminating the need of a respiration belt, which considerably increases participants’ comfort. However, little is known about the effectiveness of these new technologies and how they compare to the standard respiration belt recording. In the current study, we compared the two devices, respiration belt and spine coil sensor, in their suitability for physiological noise removal during a visual perception task and during rest. We did not find any differences in resting-state functional connectivity (RSFC), stimulus-related activity, or residual noise between data corrected with the two recording devices. Our results show that spine coil-derived respiration recordings are as good as belt respiration recordings for physiological noise removal in task-induced activity, with spine coil recordings having an additional advantage in terms of participants’ comfort and artifact susceptibility.

Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA.

To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp. A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment. Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects. This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.

Transcranial magnetic stimulation of primary motor cortex elicits an immediate transcranial evoked potential

BackgroundTranscranial evoked potentials (TEPs) measured via electroencephalography (EEG) are widely used to study the cortical responses to transcranial magnetic stimulation (TMS). Immediate transcranial evoked potentials (i-TEPs) have been obscured by pulse and muscular artifacts. Thus, the TEP peaks that are commonly reported have latencies that are too long to be caused by direct excitation of cortical neurons. MethodsIn 25 healthy individuals, we recorded i-TEPs evoked by a single biphasic TMS pulse targeting the primary motor hand area (M1HAND) or parietal or midline control sites. Sampling EEG at 50 kHz enabled us to reduce the duration of the TMS pulse artifact to a few milliseconds, while minor adjustments of the TMS coil tilt or position enabled us to avoid cranial muscular twitches during the experiment. ResultsWe observed an early positive EEG deflection starting after approx. 2 ms followed by a series of superimposed peaks with an inter-peak interval of ∼1.1–1.4 ms in multiple electrodes surrounding the stimulated sensorimotor region. This multi-peak i-TEP response was only evoked by TMS of the M1HAND region and was modified by changes in stimulation intensity and current direction. DiscussionSingle-pulse TMS of the M1HAND evokes an immediate local multi-peak response at the cortical site of stimulation. Our results suggest that the observed i-TEP patterns are genuine cortical responses evoked by TMS caused by synchronized excitation of pyramidal neurons in the targeted precentral cortex. This notion needs to be corroborated in future studies, including further investigations into the potential contribution of instrumental or physiological artifacts.

Methods for Processing Physiological Artifacts in Single/Few-Channel EEG Signals

Electroencephalogram (EEG) is a non-invasive measurement method of brain electrical activity. In recent years, single/few-channel EEG has been used more and more, but various types of physiological artifacts seriously affect the analysis and wide application of single/few-channel EEG. In this paper, the regression and filtering methods, decomposition methods, blind source separation methods and machine learning methods involved in the various physiological artifacts in single/few-channel EEG are reviewed. According to the characteristics of single/few-channel EEG signals, hybrid EEG artifact removal methods for different scenarios are analyzed and summarized, mainly including single-artifact/multi-artifact scenes and online/offline scenes. In addition, the methods and metrics for validating the performance of the algorithm on semi-simulated and real EEG data are also reviewed. Finally, the development trend of single/few-channel EEG application and physiological artifact processing is briefly described.

Objective QC for diffusion MRI data: Artefact detection using normative modelling

Abstract Diffusion MRI is a neuroimaging modality used to evaluate brain structure at a microscopic level and can be exploited to map white matter fibre bundles and microstructure in the brain. One common issue is the presence of artefacts, such as acquisition artefacts, physiological artefacts, distortions, or image processing-related artefacts. These may lead to problems with other downstream processes and can bias subsequent analyses. In this work, we use normative modelling to create a semi-automated pipeline for detecting diffusion imaging artefacts and errors by modelling 24 white matter imaging-derived phenotypes from the UK Biobank dataset. The considered features comprised four microstructural features (from models with different complexity such as fractional anisotropy and mean diffusivity from a diffusion tensor model and parameters from neurite orientation, dispersion, and density models), each within six pre-selected white matter tracts of various sizes and geometrical complexity (corpus callosum, bilateral corticospinal tract and uncinate fasciculus and fornix). Our method was compared to two traditional quality control approaches: a visual quality control protocol performed on 500 subjects and quantitative quality control using metrics derived from image pre-processing. The normative modelling framework proves to be comprehensive and efficient in detecting diffusion imaging artefacts arising from various sources (such as susceptibility induced distortions or motion), as well as outliers resulting from inaccurate processing (such as erroneous spatial registrations). This is an important contribution by virtue of this methods’ ability to identify the two problem sources (i) image artefacts and (ii) processing errors, which subsequently allows for a better understanding of our data and informs on inclusion/exclusion criteria of participants.

Frequency of Different Types of Artifacts in Electroencephalography of the Brain of Hospitalized and Outpatient Patients Referred to the Clinics of Al-Zahra Hospital and Khorshid Hospital in Isfahan

Background: Artifact is a common error in patient’s electroencephalogram that if not addressed, it can lead to reduced quality and ultimately misdiagnosis. The aim of this study was to determine the frequency of different types of artifacts in electroencephalography of the brain of hospitalized and outpatient patients referred to the clinics of Al-Zahra Hospital and Khorshid Hospital in Isfahan. Methods: In this cross-sectional study that was performed in Al-Zahra and Noor hospitals in Isfahan in 1399, 256 patients were studied by electroencephalography and examined for their electroencephalography, the presence of physiological and non-physiological artifacts and the source of artifacts in them. Results: Out of 256 patients studied, in 237 cases (92.6%) the artifacts were physiological and 19 (7.4%) were non-physiological. The type of physiological artifacts included 217 cases (91.6%) ocular, 1 case (0.4%) cardiac, 5 cases (2.1%) sweating and 14 cases (5.9%) motor. Among non-physiological artifacts, 11 (57.9%) were electrode and 8 (42.1%) were external artifacts. There was no significant difference in the type of artifacts performed according to the age and sex of the patients. Conclusion: The findings of the present study show that the presence of artifacts in patient’s electroencephalogram is very common, and among them, physiological ocular artifacts and non-physiological electrode artifacts are the most common errors in patients’ electroencephalogram and therefore it is necessary to Investigate the possibility of different types of artifacts and take the necessary strategies to reduce them.

Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals

Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8–19 Hz and 19–30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, and Kappa index of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

Effect of Phase Encoding Direction on Image Quality in Single-Shot EPI Diffusion-Weighted Imaging of the Breast.

In breast diffusion-weighted imaging (DWI), distortion and physiologic artifacts affect clinical interpretation. Image quality can be optimized by addressing the effect of phase encoding (PE) direction on these artifacts. To compare distortion artifacts in breast DWI acquired with different PE directions and polarities, and to discuss their clinical implications. Prospective. Eleven healthy volunteers (median age: 47 years old; range: 22-74 years old) and a breast phantom. Single-shot echo planar DWI and three-dimensional fast gradient echo sequences at 3 T. All DWI data were acquired with left-right, right-left, posterior-anterior, and anterior-posterior PE directions. In phantom data, displacement magnitude was evaluated by comparing the location of landmarks in anatomical and DWI images. Three breast radiologists (5, 17, and 23 years of experience) assessed the presence or absence of physiologic artifacts in volunteers' DWI datasets and indicated their PE-direction preference. Analysis of variance with post-hoc tests were used to assess differences in displacement magnitude across DWI datasets and observers. A binomial test and a chi-squared test were used to evaluateif each in vivo DWI dataset had an equal probability (25%) of being preferred by radiologists. Inter-reader agreement was evaluated using Gwet's AC1 agreement coefficient. A P-value <0.05 was considered statistically significant. In the phantom study, median displacement was the significantly largest in posterior-anterior data. While the displacement in the anterior-posterior and left-right data were equivalent (P = 0.545). In the in vivo data, there were no physiological artifacts observed in any dataset, regardless of PE direction. In the reader study, there was a significant preference for the posterior-anterior datasets which were selected 94% of the time. There was good agreement between readers (0.936). This study showed the impact of PE direction on distortion artifacts in breast DWI. In healthy volunteers, the posterior-to-anterior PE direction was preferred by readers. 2 TECHNICAL EFFICACY: Stage 1.

A GAN Guided Parallel CNN and Transformer Network for EEG Denoising.

Electroencephalography (EEG) signals are often contaminated with various physiological artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts is an essential step in practice. As of now, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. However, they still suffer from the following limitations. The existing structure designs have not fully taken into account the temporal characteristics of artifacts. Meanwhile, the existing training strategies usually ignore the holistic consistency between denoised EEG signals and authentic clean ones. To address these issues, we propose a GAN guided parallel CNN and transformer network, named GCTNet. The generator contains parallel CNN blocks and transformer blocks to respectively capture local and global temporal dependencies. Then, a discriminator is employed to detect and correct the holistic inconsistencies between clean and denoised EEG signals. We evaluate the proposed network on both semi-simulated and real data. Extensive experimental results demonstrate that GCTNet significantly outperforms state-of-the-art networks in various artifact removal tasks, as evidenced by its superior objective evaluation metrics. For example, in the task of removing electromyography artifacts, GCTNet achieves 11.15% reduction in RRMSE and 9.81% improvement in SNR over other methods, highlighting the potential of the proposed method as a promising solution for EEG signals in practical applications.

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance.

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.

EEG Signal Filtering Method and Comparison Between Modern to Classical Filter

In neuroscience and therapeutic applications, electroencephalography (EEG) has become a crucial technique for gaining understanding of brain activity and cognitive processes. In order to improve signal quality, lower noise, and extract useful information from EEG data, preprocessing is essential. In the context of EEG signal processing, this study compares the effectiveness of three frequently used filtering methods: the median filter, average filter, and Gaussian filter.This study's main goal is to assess and compare how well these filters work to reduce the various forms of noise that may be found in EEG data, such as physiological artifacts, environmental interference, and electrode noise. The study makes use of a large dataset of EEG recordings that spans a range of experimental settings and mental states. The filtering methods are methodically applied to the unprocessed EEG data, and then the effects on signal fidelity and quality are thoroughly examined.The findings of this investigation shed important light on the advantages and disadvantages of each filtering strategy used in EEG data processing. In the end, the conclusions from this comparison study help offer criteria for choosing a suitable filtering approach based on the unique properties of EEG data and the research goals.The findings of this investigation offer important new understandings of the advantages and disadvantages of each filtering strategy used in EEG data processing. Finally, depending on the unique properties of the EEG data and the study goals, the findings from this comparison analysis help offer criteria for choosing an acceptable filtering approach.

Optical Estimation of Bioelectric Patterns in Living Embryos.

Fluorescent lifetime imaging (FLIM) is a powerful tool for visualizing physiological parameters in vivo. We present here a 3-dye strategy for mapping bioelectric patterns in living Xenopus laevis embryos leveraging the quantitative power of fluorescent lifetime imaging. We discuss a general strategy for disentangling physiological artifacts from true bioelectric signals, a method for dye delivery via transcardial injection, and how to visualize and interpret the fluorescent lifetime of the dyes in vivo.

55 Within-Individual BOLD Signal Variability During a Letter N-Back Task: Implications for a Verbal Fluency Network

Objective:Functional magnetic resonance imaging (fMRI) research has generally focused on drawing conclusions from average brain activation patterns. Importantly, the brain is inherently variable; growing literature has found that within-individual blood oxygen level-dependent (BOLD) signal variability may be meaningful, and not just “noise.” For example, recent research has identified increased BOLD signal variability in healthy younger and older adults during more effortful/complex task loads of n-back paradigms, commonly used tasks that involve important elements of executive function (e.g., attention, working memory, planning, inhibition, etc.). Verbal fluency is a complex cognitive domain that also involves similar processes to generate words given certain rules. As a result, the current study builds on existing literature to investigate within-individual BOLD signal variability patterns in peak coordinates of a verbal fluency network during different loads of a letter n-back task. Due to greater executive demands, greater variability was expected during more effortful/complex n-back task loads in regions of a verbal fluency network.Participants and Methods:Forty-eight healthy young adults (Mage(SD) = 22.41(4.47), 25 females) from the Atlanta area completed a letter n-back task in an MRI scanner. After standard processing in AFNI, images were corrected for motion and physiological artifacts, which may be confounding sources of variability. Volumes associated with each load of the letter n-back task (0-back, 1-back, 2-back, 3-back, crosshair) were identified. Task runs were normalized and respective run means were subtracted prior to concatenating all runs for each load type. Standard deviations were calculated across this mean-run corrected time series. Ten peak regions of interest (ROIs) were identified from a verbal fluency network generated from 84 peer-reviewed publications for this domain gathered on NeuroSynth. Paired samples t-tests with Benjamini-Hochberg correction for multiple comparisons were conducted to explore differences in variability during n-back task loads.Results:In several of the verbal fluency network ROIs, within-individual BOLD signal variability was significantly greater for 2-back versus 0-back loads with medium to large effect sizes (p’s &lt; .001 - &lt; .01, Cohen’s d range: .53-.93). Variability was also significantly greater for 3-back versus 0-back loads with small to medium effect sizes (p’s &lt; .001 - &lt; .01, Cohen’s d range: .48-.74). Specific regions that evidenced this pattern included ROIs in the left inferior frontal gyrus, left cingulate, right inferior frontal gyrus, left middle frontal gyrus, and left superior parietal lobule. Only two regions demonstrated increased variability in the 1-back load versus crosshair (left middle frontal gyrus, p &lt; .001, d = .63; left lentiform nucleus, p &lt; .05, d = .42). No regions demonstrated a significant difference in variability in the 0-back load versus crosshair.Conclusions:This study contributes to growing literature examining within-individual BOLD signal variability in healthy individuals by exploring variability patterns in a verbal fluency network. The observed pattern of results supports the hypothesis and is in line with previous research, demonstrating that greater variability occurs with greater executive task demands. Future research can use an inscanner task of verbal fluency and can extend variability findings during this in-scanner task to out-of-scanner measures of verbal fluency.