From Lab to Field Conditions: A Pilot Study on EEG Methodology in Applied Sports Sciences

Although neurophysiological aspects have become more important in sports and exercise sciences in the last years, it was not possible to measure cortical activity during performance outside a laboratory due to equipment limits or movement artifacts in particular. With this pilot study we want to investigate whether Electroencephalography (EEG) data obtained in a laboratory golf putting performance differ from a suitable putting task under field conditions. Therefore, parameters of the working memory (frontal Theta and parietal Alpha 2 power) were recorded during these two conditions. Statistical calculations demonstrated a significant difference only for Theta power at F4 regarding the two putting conditions “field” and “laboratory”. These findings support the idea that brain activity patterns obtained under laboratory conditions are comparable but not equivalent to those obtained under field conditions. Additionally, we were able to show that the EEG methodology seems to be a reliable tool to observe brain activity under field conditions in a golf putting task. However, considering the still existing problems of movement artifacts during EEG measurements, eligible sports and exercises are limited to those being relatively motionless during execution. Further studies are needed to confirm these pilot results.     

Image Corruption Detection in Diffusion Tensor Imaging for Post-Processing and Real-Time Monitoring

Due to the high sensitivity of diffusion tensor imaging (DTI) to physiological motion, clinical DTI scans often suffer a significant amount of artifacts. Tensor-fitting-based, post-processing outlier rejection is often used to reduce the influence of motion artifacts. Although it is an effective approach, when there are multiple corrupted data, this method may no longer correctly identify and reject the corrupted data. In this paper, we introduce a new criterion called “corrected Inter-Slice Intensity Discontinuity” (cISID) to detect motion-induced artifacts. We compared the performance of algorithms using cISID and other existing methods with regard to artifact detection. The experimental results show that the integration of cISID into fitting-based methods significantly improves the retrospective detection performance at post-processing analysis. The performance of the cISID criterion, if used alone, was inferior to the fitting-based methods, but cISID could effectively identify severely corrupted images with a rapid calculation time. In the second part of this paper, an outlier rejection scheme was implemented on a scanner for real-time monitoring of image quality and reacquisition of the corrupted data. The real-time monitoring, based on cISID and followed by post-processing, fitting-based outlier rejection, could provide a robust environment for routine DTI studies.     

A Comparison of Two Recorders for Obtaining In-flight Heart Rate Data

Measurement of mental workload has been widely used for evaluation of aircraft design, mission analysis and assessment of pilot performance during flight operations. Heart rate is the psychophysiological measure that has been most frequently used for this purpose. The risk of interference with flight safety and pilot performance, as well as the generally constrained access to flights, make it difficult for researchers to collect in-flight heart rate data. Thus, this study was carried out to investigate whether small, non-intrusive sports recorders can be used for in-flight data collection for research purposes. Data was collected from real and simulated flights with student pilots using the Polar Team System sports recorder and the Vitaport II, a clinical and research recording device. Comparison of the data shows that in-flight heart rate data from the smaller and less intrusive sports recorder have a correlation of.981 with that from the clinical recorder, thus indicating that the sports recorder is reliable and cost-effective for obtaining heart rate data for many research situations.     

Epilepsy in kcnj10 Morphant Zebrafish Assessed with a Novel Method for Long-Term EEG Recordings

We aimed to develop and validate a reliable method for stable long-term recordings of EEG activity in zebrafish, which is less prone to artifacts than current invasive techniques. EEG activity was recorded with a blunt electrolyte-filled glass pipette placed on the zebrafish head mimicking surface EEG technology in man. In addition, paralysis of agarose-embedded fish using D-tubocurarine excluded movement artifacts associated with epileptic activity. This non-invasive recording technique allowed recordings for up to one hour and produced less artifacts than impaling the zebrafish optic tectum with a patch pipette. Paralyzed fish survived, and normal heartbeat could be monitored for over 1h. Our technique allowed the demonstration of specific epileptic activity in kcnj10a morphant fish (a model for EAST syndrome) closely resembling epileptic activity induced by pentylenetetrazol. This new method documented that seizures in the zebrafish EAST model were ameliorated by pentobarbitone, but not diazepam, validating its usefulness. In conclusion, non-invasive recordings in paralyzed EAST syndrome zebrafish proved stable, reliable and robust, showing qualitatively similar frequency spectra to those obtained from pentylenetetrazol-treated fish. This technique may prove particularly useful in zebrafish epilepsy models that show infrequent or conditional seizure activity.     

Double calibration: an accurate, reliable and easy-to-use method for 3D scapular motion analysis

The most recent non-invasive methods for the recording of scapular motion are based on an acromion marker (AM) set and a single calibration (SC) of the scapula in a resting position. However, this method fails to accurately measure scapular kinematics above 90° of arm elevation, due to soft tissue artifacts of the skin and muscles covering the acromion. The aim of this study was to evaluate the accuracy, and inter-trial and inter-session repeatability of a double calibration method (DC) in comparison with SC. The SC and DC data were measured with an optoelectronic system during arm flexion and abduction at different angles of elevation (0-180°). They were compared with palpation of the scapula using a scapula locator. DC data was not significantly different from palpation for 5/6 axes of rotation tested (Y, X, and Z in abduction and flexion), where as SC showed significant differences for 5/6 axes. The root mean square errors ranged from 2.96° to 4.48° for DC and from 6° to 9.19° for SC. The inter-trial repeatability was good to excellent for SC and DC. The inter-session repeatability was moderate to excellent for SC and moderate to good for DC. Coupling AM and DC is an easy-to-use method, which yields accurate and reliable measurements of scapular kinematics for the complete range of arm motion. It can be applied to the measurement of shoulder motion in many fields (sports, orthopaedics, and rehabilitation), especially when large ranges of arm motion are required.     

Relevance of motion artifact in electromyography recordings during vibration treatment

Electromyography readings (EMGs) from quadriceps of fifteen subjects were recorded during whole body vibration treatment at different frequencies (10–50 Hz). Additional electrodes were placed on the patella to monitor the occurrence of motion artifact, triaxial accelerometers were placed onto quadriceps to monitor motion. Signal spectra revealed sharp peaks corresponding to vibration frequency and its harmonics, in accordance with the accelerometer data. EMG total power was compared to that associated with vibration harmonics narrow bands, before and during vibration. On average, vibration associated power resulted in only 3% (±0.9%) of the total power prior to vibration and 29% (±13.4%) during vibration. Often, studies employ surface EMG to quantitatively evaluate vibration evoked muscular activity and to set stimulation frequency. However, previous research has not accounted for motion artifacts. The data presented in this study emphasize the need for the removal of motion artifacts, as they consistently affect RMS estimation, which is often used as a concise muscle activity index during vibrations. Such artifacts, rather unpredictable in amplitude, might be the cause of large inter-study differences and must be eliminated before analysis. Motion artifact filtering will contribute to thorough and precise interpretation of neuromuscular response to vibration treatment.     

Enhancing Life-Cycle Inventories via Reconciliation with the Laws of Thermodynamics

Abstract: Obtaining reliable results from life-cycle assessment studies is often quite difficult because life-cycle inventory (LCI) data are usually erroneous, incomplete, and even physically meaningless. The real data must satisfy the laws of thermodynamics, so the quality of LCI data may be enhanced by adjusting them to satisfy these laws. This is not a new idea, but a formal thermodynamically sound and statistically rigorous approach for accomplishing this task is not yet available. This article proposes such an approach based on methods for data rectification developed in process systems engineering. This approach exploits redundancy in the available data and models and solves a constrained optimization problem to remove random errors and estimate some missing values. The quality of the results and presence of gross errors are determined by statistical tests on the constraints and measurements. The accuracy of the rectified data is strongly dependent on the accuracy and completeness of the available models, which should capture information such as the life-cycle network, stream compositions, and reactions. Such models are often not provided in LCI databases, so the proposed approach tackles many new challenges that are not encountered in process data rectification. An iterative approach is developed that relies on increasingly detailed information about the life-cycle processes from the user. A comprehensive application of the method to the chlor-alkali inventory being compiled by the National Renewable Energy Laboratory demonstrates the benefits and challenges of this approach.     

Multiway analysis of epilepsy tensors

MOTIVATION: The success or failure of an epilepsy surgery depends greatly on the localization of epileptic focus (origin of a seizure). We address the problem of identification of a seizure origin through an analysis of ictal electroencephalogram (EEG), which is proven to be an effective standard in epileptic focus localization. SUMMARY: With a goal of developing an automated and robust way of visual analysis of large amounts of EEG data, we propose a novel approach based on multiway models to study epilepsy seizure structure. Our contributions are 3-fold. First, we construct an Epilepsy Tensor with three modes, i.e. time samples, scales and electrodes, through wavelet analysis of multi-channel ictal EEG. Second, we demonstrate that multiway analysis techniques, in particular parallel factor analysis (PARAFAC), provide promising results in modeling the complex structure of an epilepsy seizure, localizing a seizure origin and extracting artifacts. Third, we introduce an approach for removing artifacts using multilinear subspace analysis and discuss its merits and drawbacks. RESULTS: Ictal EEG analysis of 10 seizures from 7 patients are included in this study. Our results for 8 seizures match with clinical observations in terms of seizure origin and extracted artifacts. On the other hand, for 2 of the seizures, seizure localization is not achieved using an initial trial of PARAFAC modeling. In these cases, first, we apply an artifact removal method and subsequently apply the PARAFAC model on the epilepsy tensor from which potential artifacts have been removed. This method successfully identifies the seizure origin in both cases.     

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator''s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.     

Method for Simultaneous fMRI/EEG Data Collection during a Focused Attention Suggestion for Differential Thermal Sensation

In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density 256-channel sensor net. The EEG amplifier itself is contained in a field isolation containment system (FICS), and MRI clock signals are synchronized with EEG data collection for subsequent MR artifact characterization and removal. We demonstrate this method first for resting state data collection. Thereafter, we demonstrate a protocol for EEG/fMRI data recording, while subjects listen to a tape asking them to visualize that their left hand is immersed in a cold-water bath and referred to, here, as the cold glove paradigm. Thermal differentials between each hand are measured throughout EEG/fMRI data collection using an MR compatible temperature sensor that we developed for this purpose. We collect cold glove EEG/fMRI data along with simultaneous differential hand temperature measurements both before and after hypnotic induction. Between pre and post sessions, single modality EEG data are collected during the hypnotic induction and depth assessment process. Our representative results demonstrate that significant changes in the EEG power spectrum can be measured during hypnotic induction, and that hand temperature changes during the cold glove paradigm can be detected rapidly using our MR compatible differential thermometry device.     

Bayesian classifier for brain-computer interface based on mental representation of movements

This paper proposes Bayesian approach to classification of EEG patterns on the basis of imaginary movements of extremities based on analysis ofcovariance matrices of native EEG recordings. An efficacy of a Brain-Computer Interface (BCI) based on the proposed classifier is evaluated. Bayesian classifier is shown to be competitive with the MCSP (Multiclass Common Spatial Patterns) classifier known from the literature as one of the efficient variant for BCI implementation. The influence of eye movement and blinking artifacts on the BCI performance is investigated. It is shown that the presence of such artifacts does not affect the classification accuracy.     

Method for recording short latency evoked potentials using an EKG artifact elimination procedure

This paper describes the application of an EKG elimination procedure¹, previously reported by the authors, to evoked potentials (EPs) recording using a non-cephalic reference. The method consists of three separate steps: data acquisition, EKG artifact elimination, and EP averaging. EKG artifacts are eliminated from the raw EEG by applying a four step procedure to the simultaneously recorded EEG, EKG and stimulus pulse. The steps are: synchronized partition of the raw EEG, EKG averaging, synchronized repetition, and synchronized subtraction of the EKG estimate from the raw EEG. Average EP values are then obtained by averaging the processed EEG using the stimulus pulse as a trigger. Somatosensory evoked potentials to a hand reference, averaged using the proposed method, were compared with those obtained by two conventional averaging methods, and were shown to be more clearly defined. The advantage of the proposed method for recording short latency EP values with a non-cephalic reference is that it requires fewer sweeps and thus takes less time than other methods. The proposed method may also be applicable to the recording of other EP values.     

Markerless 2D kinematic analysis of underwater running: A deep learning approach

Kinematic analysis is often performed with a camera system combined with reflective markers placed over bony landmarks. This method is restrictive (and often expensive), and limits the ability to perform analyses outside of the lab. In the present study, we used a markerless deep learning-based method to perform 2D kinematic analysis of deep water running, a task that poses several challenges to image processing methods. A single GoPro camera recorded sagittal plane lower limb motion. A deep neural network was trained using data from 17 individuals, and then used to predict the locations of markers that approximated joint centres. We found that 300–400 labelled images were sufficient to train the network to be able to position joint markers with an accuracy similar to that of a human labeler (mean difference < 3 pixels, around 1 cm). This level of accuracy is sufficient for many 2D applications, such as sports biomechanics, coaching/training, and rehabilitation. The method was sensitive enough to differentiate between closely-spaced running cadences (45–85 strides per minute in increments of 5). We also found high test–retest reliability of mean stride data, with between-session correlation coefficients of 0.90–0.97. Our approach represents a low-cost, adaptable solution for kinematic analysis, and could easily be modified for use in other movements and settings. Using additional cameras, this approach could also be used to perform 3D analyses. The method presented here may have broad applications in different fields, for example by enabling markerless motion analysis to be performed during rehabilitation, training or even competition environments.     

A method for repetitive artifact suppression in multichannel EEG recordings

An artifact suppression method based on the assumption of linear independence of EEG and artifact signals is described. This assumption allows the use of principal component analysis for their separation. The method makes it possible to eliminate electrooculogram (EOG) signals from multichannel EEG recordings regardless of the use of special EOG channels in the recording, as well as to eliminate any other repetitive artifacts, including pulse and electrocardiogram artifacts, ones related to mechanical instability of the reference electrode, etc.     

EMD-based EEG signal enhancement for auditory evoked potential recovery under high stimulus-rate paradigm

Short inter-stimulus interval (ISI) is one inherent characteristic of the high stimulus-rate (HSR) paradigms for studying auditory evoked potentials (AEPs). At short ISIs, the AEPs to adjacent stimuli overlap. To resolve the AEP to a specific stimulus requires an inverse process of overlapping. Inverse filtering (also called as deconvolution) has been commonly employed to achieve this goal. However, the resulted signal may be severely distorted as inverse filtering can substantially amplify such undesired components as noises and artifacts in the raw EEG recordings. In practice, even if care be taken to obtain quality EEGs, noises and artifacts are unavoidable. It is thus critical to remove or at least supress these undesired components for studies using HSR paradigms. In this paper, we propose a systematic approach to EEG signal enhancement based on empirical mode decomposition (EMD) and threshold filtering/rejection. Using synthetic and real data, we test the effectiveness of our approach. Results for both types of data consistently demonstrate that our methods can significantly improve the quality of recovered AEPs, according to visual inspection and SNRs estimated using two metrics.     

Pupil Dilation and EEG Alpha Frequency Band Power Reveal Load on Executive Functions for Link-Selection Processes during Text Reading

Executive working memory functions play a central role in reading comprehension. In the present research we were interested in additional load imposed on executive functions by link-selection processes during computer-based reading. For obtaining process measures, we used a methodology of concurrent electroencephalographic (EEG) and eye-tracking data recording that allowed us to compare epochs of pure text reading with epochs of hyperlink-like selection processes in an online reading situation. Furthermore, this methodology allowed us to directly compare the two physiological load-measures EEG alpha frequency band power and pupil dilation. We observed increased load on executive functions during hyperlink-like selection processes on both measures in terms of decreased alpha frequency band power and increased pupil dilation. Surprisingly however, the two measures did not correlate. Two additional experiments were conducted that excluded potential perceptual, motor, or structural confounds. In sum, EEG alpha frequency band power and pupil dilation both turned out to be sensitive measures for increased load during hyperlink-like selection processes in online text reading.     

Modeling of movement-related potentials using a fractal approach

In bio-signal applications, classification performance depends greatly on feature extraction, which is also the case for electroencephalogram (EEG) based applications. Feature extraction, and consequently classification of EEG signals is not an easy task due to their inherent low signal-to-noise ratios and artifacts. EEG signals can be treated as the output of a non-linear dynamical (chaotic) system in the human brain and therefore they can be modeled by their dimension values. In this study, the variance fractal dimension technique is suggested for the modeling of movement-related potentials (MRPs). Experimental data sets consist of EEG signals recorded during the movements of right foot up, lip pursing and a simultaneous execution of these two tasks. The experimental results and performance tests show that the proposed modeling method can efficiently be applied to MRPs especially in the binary approached brain computer interface applications aiming to assist severely disabled people such as amyotrophic lateral sclerosis patients in communication and/or controlling devices.     

Rectification of SEMG as a tool to demonstrate synchronous motor unit activity during vibration

The use of surface electromyography (SEMG) in vibration studies is problematic since motion artifacts occupy the same frequency band with the SEMG signal containing information on synchronous motor unit activity. We hypothesize that using a harsher, 80–500 Hz band-pass filter and using rectification can help eliminate motion artifacts and provide a way to observe synchronous motor unit activity that is phase locked to vibration using SEMG recordings only. Multi Motor Unit (MMU) action potentials using intramuscular electrodes along with SEMG were recorded from the gastrocnemius medialis (GM) of six healthy male volunteers. Data were collected during whole body vibration, using vibration frequencies of 30 Hz, 35 Hz, 40 Hz or 50 Hz. A computer simulation was used to investigate the efficacy of filtering under different scenarios: with or without artifacts and/or motor unit synchronization. Our findings indicate that motor unit synchronization took place during WBV as verified by MMU recordings. A harsh filtering regimen along with rectification proved successful in demonstrating motor unit synchronization in SEMG recordings. Our findings were further supported by the results from the computer simulation, which indicated that filtering and rectification was efficient in discriminating motion artifacts from motor unit synchronization. We suggest that the proposed signal processing technique may provide a new methodology to evaluate the effects of vibration treatments using only SEMG. This is a major advantage, as this non-intrusive method is able to overcome movement artifacts and also indicate the synchronization of underlying motor units.