Heart Rate Variability Responses to Visually Induced Motion Sickness.

Analysis of heart rate variability (HRV) can reveal a range of useful information regarding the dynamics of the autonomic nervous system (ANS). It is considered a robust and reliable tool to understand even some subtle changes in ANS activity. Here, we study the "hidden" characteristic changes in HRV during visually induced motion sickness; using nonlinear analytical methods, supplemented by conventional time- and frequency-domain analyses. We computed HRV from electrocardiograms (ECG) of 14 healthy participants measured at baseline and during nausea. Primarily hypothesizing evident differences in measures of physiologic complexity (SampEn; sample entropy, FuzzyEn; fuzzy entropy), chaos (LLE; largest Lyapunov exponent) and Poincaré/Lorenz (CSI; cardiac sympathetic activity, CVI; cardiac vagal index) between the two states. We found that during nausea, participants showed a markedly higher degree of regularity (SampEn, p = 0.0275; FuzzyEn, p = 0.0006), with a less chaotic ANS response (LLE, p = 0.0004). CSI significantly increased during nausea compared to baseline (p = 0.0005), whereas CVI did not appear to be statistically different between the two states (p = 0.182). Our findings suggest that motion sickness-induced ANS perturbations may be quantifiable via nonlinear HRV indices. These findings have implications for understanding the malaise of motion sickness and in turn, aid development of therapeutic interventions to relieve motion sickness symptoms.Clinical relevance- The study suggests potential indices of physiologic complexity and chaos that may be useful in monitoring motion sickness during clinical studies.

Did You Hear That? Detecting Auditory Events with EEGNet.

The behavioural nature of pure-tone audiometry (PTA) limits those who can participate in the test, and therefore those who can access accurate hearing threshold measurements. Event Related Potentials (ERPs) from brain signals has shown limited utility on adult subjects, and a neural response that can consistently be identified as a result of pure-tone auditory stimulus has yet to be identified. The in doing so challenge is worsened by the nature of PTA, where stimulus amplitude decrease to a patient's lower threshold of hearing. We investigate whether EEGNet, a compact Convolutional Neural Network, could help in this domain. We trained EEGNet on a dataset collected whilst patients underwent a test designed to mimic a pure-tone audiogram, then assessed EEGNet performance in the detection task. For comparison, we also trained Support Vector Machines (SVMs) and Common Spatial Patterns + Linear Discriminant Analysis (CSPLDA) on the same task, with the same training paradigms. The results show that EEGNet is capable of detecting hearing events with 81.5% accuracy on unseen participants, outperforming SVMs by just over 5%. Whilst EEGNet outperformed SVMs and CSPLDA, it did not, however, always show a statistically significant improvement. Further analysis of EEGNet predictions revealed that, with sufficient test repetition, EEGNet has the potential to accurately ascertain hearing thresholds. The implication of these results is for a brain-signal based hearing test for those with physical or mental disabilities that limit their participation in a PTA. While this research is promising, future research will be needed to address the complexity of test setup, the duration of testing, and to further improve accuracy.

Transcutaneous Auricular Vagus Nerve Stimulation towards Visually Induced Motion Sickness Reduction: A Pilot Study.

Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel neuromodulation application for vagal afferent stimulation. Owing to its non-invasive nature, taVNS is a potent therapeutic tool for a diverse array of diseases and disorders that ail us. Herein, we investigated taVNS-induced effects on neural activity of participants during visually induced motion sickness. 64-channel electroencephalography (EEG) recordings were obtained from 15 healthy participants in a randomized, within-subjects, cross-over design during sham and taVNS conditions. To assess motion sickness severity, we used the motion sickness assessment questionnaire (MSAQ). We observed that taVNS attenuated theta (4-8 Hz) brain activity in the right frontal, right parietal and occipital cortices when compared to sham condition. The total MSAQ scores, and central, peripheral and sopite MSAQ categorical scores were significantly lower after taVNS compared to sham. These findings reveal for the first time the potential therapeutic role of taVNS toward counter-motion sickness, and suggest that taVNS may be reliable in alleviating symptoms of motion sickness in real-time, non-pharmacologically.Clinical relevance- This suggests taVNS potential to offset motion sickness-induced nausea; which may be of translational value to counter e.g., chemotherapy-induced nausea.

Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models.

Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several other variabilities. We hypothesise that this challenge can be addressed by introducing uncertainty-aware models because the rejection of uncertain movements has previously been demonstrated to improve the reliability of sEMG-based hand gesture recognition. With a particular focus on a very challenging benchmark dataset (NinaPro Database 6), we propose a novel end-to-end uncertainty-aware model, an evidential convolutional neural network (ECNN), which can generate multidimensional uncertainties, including vacuity and dissonance, for robust long-term hand grasp recognition. To avoid heuristically determining the optimal rejection threshold, we examine the performance of misclassification detection in the validation set. Extensive comparisons of accuracy under the non-rejection and rejection scheme are conducted when classifying 8 hand grasps (including rest) over 8 subjects across proposed models. The proposed ECNN is shown to improve recognition performance, achieving an accuracy of 51.44% without the rejection option and 83.51% under the rejection scheme with multidimensional uncertainties, significantly improving the current state-of-the-art (SoA) by 3.71% and 13.88%, respectively. Furthermore, its overall rejection-capable recognition accuracy remains stable with only a small accuracy degradation after the last data acquisition over 3 days. These results show the potential design of a reliable classifier that yields accurate and robust recognition performance.

On the potential of transauricular electrical stimulation to reduce visually induced motion sickness.

Perturbations in the autonomic nervous system occur in individuals experiencing increasing levels of motion sickness. Here, we investigated the effects of transauricular electrical stimulation (tES) on autonomic function during visually induced motion sickness, through the analysis of spectral and time-frequency heart rate variability. To determine the efficacy of tES, we compared sham and tES conditions in a randomized, within-subjects, cross-over design in 14 healthy participants. We found that tES reduced motion sickness symptoms by significantly increasing normalized high-frequency (HF) power and decreasing both normalized low-frequency (LF) power and the power ratio of LF and HF components (LF/HF ratio). Furthermore, behavioral data recorded using the motion sickness assessment questionnaire (MSAQ) showed significant differences in decreased symptoms during tES compared to sham condition for the total MSAQ scores and, central and sopite categories of the MSAQ. Our preliminary findings suggest that by administering tES, parasympathetic modulation is increased, and autonomic imbalance induced by motion sickness is restored. This study provides first evidence that tES may have potential as a non-pharmacological neuromodulation tool to keep motion sickness at bay. Thus, these findings may have implications towards protecting people from becoming motion sick and possible accelerated recovery from the malady.

Vibro-motor Reprocessing Therapy towards Managing Motion Sickness Reduction: Evidence from EEG.

This study examines the neural activities of participants undergoing vibro-motor reprocessing therapy (VRT) while experiencing motion sickness. We evaluated the efficacy of vibro-motor reprocessing therapy, a novel therapeutic technique based on eye movement desensitization and reprocessing (EMDR), in reducing motion sickness. Based on visually induced motion sickness in two sets of performed sessions, eight participants were exposed to VRT stimulation in a VRT/non-VRT setting. Simultaneously, brain activity changes were recorded using electroencephalography (EEG) at baseline and during stimulus exposure, and comparisons made across the VRT/non-VRT conditions. A significant reduction in the alpha (8-12 Hz) spectral power was observed in the frontal and occipital locations, consistent across all participants. Furthermore, significant reductions were also found in the frontal and occipital delta (0.5-4 Hz) and theta (4-8 Hz) spectral power frequency bands between non-VRT and VRT conditions (p < 0.05). Our results offer novel insights for a potential nonpharmacological treatment and attenuation of motion sickness. Furthermore, symptoms can be observed, and alleviated, in real-time using the reported techniques. Clinical relevance - Instead of using drugs to treat motion sickness, patients could safely use this VRT technique.

Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks.

Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition.

Attentional bias towards high and low caloric food on repeated visual food stimuli: An ERP study.

Food variety influences appetitive behaviour, motivation to eat and energy intake. Research found that repeated exposure to varied food images increases the motivation towards food in adults and children. This study investigates the effects of repetition on the modulation of early and late components of event-related potentials (ERPs) when participants passively viewed the same food and non-food images repeatedly. The motivational attention to food and non-food images were assessed in frontal, centroparietal, parietooccipital and occipitotemporal areas of the brain. Participants showed increased late positive potential (late ERP component) to high caloric image in the occipitotemporal region compared to low caloric and non-food images. Similar effects could be seen in the early ERP component in the frontal region, but with reversed polarity. Data suggest that both the early and late ERP components show greater ERP amplitude when viewing high caloric images than low caloric and non-food images. Despite repeated exposure to same image, high caloric food continued to show sustained attention compared to low caloric and non-food image.

CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection.

This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation. Then, a back-end deep learning network is used to classify the spectrogram features into categories of respiratory anomaly cycles or diseases. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory-sound analysis. Firstly, we carry out an extensive exploration of the effect of spectrogram types, spectral-time resolution, overlapping/non-overlapping windows, and data augmentation on final prediction accuracy. This leads us to propose a novel deep learning system, built on the proposed framework, which outperforms current state-of-the-art methods. Finally, we apply a Teacher-Student scheme to achieve a trade-off between model performance and model complexity which holds promise for building real-time applications.

Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases.

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this paper. Firstly, we provide an extensive analysis of how factors such as respiratory cycle length, time resolution, and network architecture, affect final prediction accuracy. Secondly, a novel deep learning based framework is proposed for detection of respiratory diseases and shown to perform extremely well compared to state of the art methods.

A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet.

Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier. In this paper, we propose a normalisation approach that allows implementing real-time subject-independent sEMG based hand gesture classification without training the deep learning algorithm subject specifically. We hypothesed that the amplitude ranges of sEMG across channels between forearm muscle contractions for a hand gesture recorded in the same condition do not vary significantly within each individual. Therefore, the min-max normalisation is applied to source domain data but the new maximum and minimum values of each channel used to restrict the amplitude range are calculated from a trial cycle of a new user (target domain) and assigned by the class label. A convolutional neural network (ConvNet) trained with the normalised data achieved an average 87.03% accuracy on our G. dataset (12 gestures) and 94.53% on M. dataset (7 gestures) by using the leave-one-subject-out cross-validation.

Pose Estimation from Electromyographical Data using Convolutional Neural Networks.

This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predict the hand pose with an error rate of 4.6% for the EMG model, and 3.6% when accelerometry data is included. This shows that hand pose can be effectively estimated from EMG data, which can be enhanced with accelerometry data.

DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset.

A fully customisable chip-on board (COB) LED design to evoke two brain responses simultaneously (steady state visual evoked potential (SSVEP) and transient evoked potential, P300) is discussed in this paper. Considering different possible modalities in brain-computer interfacing (BCI), SSVEP is widely accepted as it requires a lesser number of electroencephalogram (EEG) electrodes and minimal training time. The aim of this work was to produce a hybrid BCI hardware platform to evoke SSVEP and P300 precisely with reduced fatigue and improved classification performance. The system comprises of four independent radial green visual stimuli controlled individually by a 32-bit microcontroller platform to evoke SSVEP and four red LEDs flashing at random intervals to generate P300 events. The system can also record the P300 event timestamps that can be used in classification, to improve the accuracy and reliability. The hybrid stimulus was tested for real-time classification accuracy by controlling a LEGO robot to move in four directions.

Multiresolution analysis over graphs for a motor imagery based online BCI game.

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain-computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human-machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes.

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing.

OBJECTIVE: While Parkinson's disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing. METHOD: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli. RESULTS: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli. CONCLUSION: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients.

Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface.

The steady state visual evoked protocol has recently become a popular paradigm in brain-computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inherently isolated in nature, such as selecting numbers from a keypad to be dialled or letters from an alphabet to be spelled. However motivation exists for users to employ proportional control methods in intrinsically analogue tasks such as the movement of a mouse pointer. This paper introduces an online BCI in which control of a mouse pointer is directly proportional to a user's intent. Performance is measured over a series of pointer movement tasks and compared to the traditional discrete output approach. Analogue control allowed subjects to move the pointer faster to the cued target location compared to discrete output but suffers more undesired movements overall. Best performance is achieved when combining the threshold to movement of traditional discrete techniques with the range of movement offered by proportional control.

Two-stage biometric authentication method using thought activity brain waves.

Brain waves are proposed as a biometric for verification of the identities of individuals in a small group. The approach is based on a novel two-stage biometric authentication method that minimizes both false accept error (FAE) and false reject error (FRE). These brain waves (or electroencephalogram (EEG) signals) are recorded while the user performs either one or several thought activities. As different individuals have different thought processes, this idea would be appropriate for individual authentication. In this study, autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences, inter-hemispheric channel linear complexity and non-linear complexity (approximate entropy) values were used as EEG features by the two-stage authentication method with a modified four fold cross validation procedure. The results indicated that perfect accuracy was obtained, i.e. the FRE and FAE were both zero when the proposed method was tested on five subjects using certain thought activities. This initial study has shown that the combination of the two-stage authentication method with EEG features from thought activities has good potential as a biometric as it is highly resistant to fraud. However, this is only a pilot type of study and further extensive research with more subjects would be necessary to establish the suitability of the proposed method for biometric applications.

Biometrics from brain electrical activity: a machine learning approach.

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the Visual Evoked Potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis.

Enhanced detection of visual-evoked potentials in brain-computer interface using genetic algorithm and cyclostationary analysis.

We propose a novel framework to reduce background electroencephalogram (EEG) artifacts from multitrial visual-evoked potentials (VEPs) signals for use in brain-computer interface (BCI) design. An algorithm based on cyclostationary (CS) analysis is introduced to locate the suitable frequency ranges that contain the stimulus-related VEP components. CS technique does not require VEP recordings to be phase locked and exploits the intertrial similarities of the VEP components in the frequency domain. The obtained cyclic frequency spectrum enables detection of VEP frequency band. Next, bandpass or lowpass filtering is performed to reduce the EEG artifacts using these identified frequency ranges. This is followed by overlapping band EEG artifact reduction using genetic algorithm and independent component analysis (G-ICA) which uses mutual information (MI) criterion to separate EEG artifacts from VEP. The CS and GA methods need to be applied only to the training data; for the test data, the knowledge of the cyclic frequency bands and unmixing matrix would be sufficient for enhanced VEP detection. Hence, the framework could be used for online VEP detection. This framework was tested with various datasets and it showed satisfactory results with very few trials. Since the framework is general, it could be applied to the enhancement of evoked potential signals for any application.