EEG applications for sport and performance

Soft Bioelectronics for Heart Monitoring

Cardiovascular diseases (CVDs) are a predominant global health concern, accounting for over 17.9 million deaths in 2019, representing approximately 32% of all global fatalities. In North America and Europe, over a million adults undergo cardiac surgeries annually. Despite the benefits, such surgeries pose risks and require precise postsurgery monitoring. However, during the postdischarge period, where monitoring infrastructures are limited, continuous monitoring of vital signals is hindered. In this area, the introduction of implantable electronics is altering medical practices by enabling real-time and out-of-hospital monitoring of physiological signals and biological information postsurgery. The multimodal implantable bioelectronic platforms have the capability of continuous heart sensing and stimulation, in both postsurgery and out-of-hospital settings. Furthermore, with the emergence of machine learning algorithms into healthcare devices, next-generation implantables will benefit artificial intelligence (AI) and connectivity with skin-interfaced electronics to provide more precise and user-specific results. This Review outlines recent advancements in implantable bioelectronics and their utilization in cardiovascular health monitoring, highlighting their transformative deployment in sensing and stimulation to the heart toward reaching truly personalized healthcare platforms compatible with the Sustainable Development Goal 3.4 of the WHO 2030 observatory roadmap. This Review also discusses the challenges and future prospects of these devices.

Electrophysiological differences in inhibitory control processing between collegiate level soccer players and non-athletes in the absence of performance differences

Inhibitory control, the ability to manage conflicting responses and suppress inappropriate actions, is crucial for team sports athletes, including soccer players. While previous studies have shown that soccer players possess superior inhibitory control, the underlying mechanisms responsible for this advantage remain unclear. Thus, this research aimed to investigate the neural processes involved in conflict resolution and response inhibition, comparing collegiate level soccer players with non-athletes. Participants completed a novel go/no-go task that involved conflict resolution and response inhibition, while their electroencephalograms were recorded. Despite no significant difference in behavioral performance between the two groups, soccer players exhibited notable N2 and frontal midline theta modulations in response to conflict resolution and inhibition, which were comparatively weaker in non-athletes. Our findings suggest that expertise in team sports may enhance neural sensitivity to subtle yet significant information, even without a discernible behavioral advantage.

Online continual decoding of streaming EEG signal with a balanced and informative memory buffer

Electroencephalography (EEG) based Brain Computer Interface (BCI) systems play a significant role in facilitating how individuals with neurological impairments effectively interact with their environment. In real world applications of BCI system for clinical assistance and rehabilitation training, the EEG classifier often needs to learn on sequentially arriving subjects in an online manner. As patterns of EEG signals can be significantly different for different subjects, the EEG classifier can easily erase knowledge of learnt subjects after learning on later ones as it performs decoding in online streaming scenario, namely catastrophic forgetting. In this work, we tackle this problem with a memory-based approach, which considers the following conditions: (1) subjects arrive sequentially in an online manner, with no large scale dataset available for joint training beforehand, (2) data volume from the different subjects could be imbalanced, (3) decoding difficulty of the sequential streaming signal vary, (4) continual classification for a long time is required. This online sequential EEG decoding problem is more challenging than classic cross subject EEG decoding as there is no large-scale training data from the different subjects available beforehand. The proposed model keeps a small balanced memory buffer during sequential learning, with memory data dynamically selected based on joint consideration of data volume and informativeness. Furthermore, for the more general scenarios where subject identity is unknown to the EEG decoder, aka. subject agnostic scenario, we propose a kernel based subject shift detection method that identifies underlying subject changes on the fly in a computationally efficient manner. We develop challenging benchmarks of streaming EEG data from sequentially arriving subjects with both balanced and imbalanced data volumes, and performed extensive experiments with a detailed ablation study on the proposed model. The results show the effectiveness of our proposed approach, enabling the decoder to maintain performance on all previously seen subjects over a long period of sequential decoding. The model demonstrates the potential for real-world applications.

Electrophysiological impact of mental fatigue on brain activity during a bike task: A wavelet analysis approach

This study explored how mental fatigue affects brain activity during a low-intensity bike task utilising a continuous wavelet transformation in electroencephalography (EEG) analysis. The aim was to examine changes in brain activity potentially linked to central motor commands and to investigate their relationship with ratings of perceived exertion (RPE). In this study, sixteen participants (age: 21 ± 6 y, 7 females, 9 males) underwent one familiarization and two experimental trials in a randomised, blinded, cross-over study design. Participants executed a low-intensity bike task (9 min; 45 rpm; intensity (W): 10 % below aerobic threshold) after performing a mentally fatiguing (individualized 60-min Stroop task) or a control (documentary) task. Physiological (heart rate, EEG) and subjective measures (self-reported feeling of mental fatigue, RPE, cognitive load, motivation) were assessed prior, during and after the bike task. Post-Stroop, self-reported feeling of mental fatigue was higher in the intervention group (EXP) (74 ± 16) than in the control group (CON) (37 ± 17; p < 0.001). No significant differences in RPE during the bike task were observed between conditions. EEG analysis revealed significant differences (p < 0.05) in beta frequency (13-30 Hz) during the bike task, with EXP exhibiting more desynchronization during the pedal push phase and synchronization during the pedal release phase. These results suggest that mental fatigue, confirmed by both subjective and neurophysiological markers, did not significantly impact RPE during the bike task, possibly due to the use of the CR100 scale or absence of a performance outcome. However, EEG data did reveal significant beta band alterations during the task, indicating increased neural effort under mental fatigue. These findings reveal, for the first time, how motor-related brain activity at the motor cortex is impacted during a low-intensity bike task when mentally fatigued.

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

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

State of the art and future directions for measuring event-related potentials during cycling exercise: a systematic review

Intro

Electroencephalography (EEG) is a technique for measuring brain activity that is widely used in neuroscience research. Event-related potentials (ERPs) in the EEG make it possible to study sensory and cognitive processes in the brain. Previous reports have shown that aerobic exercise can have an impact on components of ERPs such as amplitude and latency. However, they focused on the measurement of ERPs after exercise.

Objectives

The aim of this systematic review was to investigate the feasibility of measuring ERPs during cycling, and to assess the impact of cycling on ERPs during cycling.

Methods

We followed the PRISMA guidelines for new systematic reviews. To be eligible, studies had to include healthy adults and measure ERPs during cycling. All articles were found using Google Scholar and by searching references. Data extracted from the studies included: objectives of ERP studies, ERP paradigm, EEG system, study population data, exercise characteristics (duration, intensity, pedaling cadence), and ERP and behavioral outcomes. The Cochrane Risk of Bias 2 tool was used to assess study bias.

Results

Twenty studies were selected. The effect of cycling on ERPs was mainly based on a comparison of P3 wave amplitude between cycling and resting states, using an attentional task. The ERP paradigm most often used was the auditory oddball task. Exercise characteristics and study methods varied considerably.

Discussion

It is possible to measure ERPs during cycling under conditions that are likely to introduce more artifacts, including a 3-h athletic exercise session and cycling outdoors. Secondly, no assessment of the effect of cycling on ERPs was possible, because the methods differed too widely between studies. In addition, the theories proposed to explain the results sometimes seemed to contradict each other. Although most studies reported significant results, the direction of the effects was inconsistent. Finally, we suggest some areas for improvement for future studies on the subject.

Differences in visuospatial cognition among table tennis players of different skill levels: an event-related potential study

This study aimed to examine the influence of sport skill levels on behavioural and neuroelectric performance in visuospatial attention and memory visuospatial tasks were administered to 54 participants, including 18 elite and 18 amateur table tennis players and 18 nonathletes, while event-related potentials were recorded. In all the visuospatial attention and memory conditions, table tennis players displayed faster reaction times than nonathletes, regardless of skill level, although there was no difference in accuracy between groups. In addition, regardless of task conditions, both player groups had a greater P3 amplitude than nonathletes, and elite players exhibited a greater P3 amplitude than amateurs players. The results of this study indicate that table tennis players, irrespective of their skill level, exhibit enhanced visuospatial capabilities. Notably, athletes at the elite level appear to benefit from an augmented allocation of attentional resources when engaging in visuospatial tasks.

Sex Differences in Athletic Performance Response to the Imagery and Mental Toughness of Elite Middle- and Long-Distance Runners

This study aimed to determine whether there is a difference between the levels of imagery and mental toughness in the context of sports performance in male and female athletes. A total of 344 track and field athletes, 205 male (59.6%, 23.3 ± 4.0 years) and 139 female (40.4%, 22.9 ± 4.0 years), voluntarily participated in the study. Imagery Inventory and Mental Toughness Inventory in Sport were used as data collection tools in the study. In the evaluation of athletic performance, athletes were asked about their ranks in the years 2020, 2021, and 2022 and were categorized according to the scoring tables specified by the International Association of Athletics Federation. A MANOVA analysis was used to determine whether there was a difference between low (-2% to +5%), medium (+6 to +11%), and high (+12 to +17%) performers among male and female athletes, and a post hoc analysis was used to determine the source of the difference. According to the present findings, there was no significant difference between the imagery and mental toughness levels of athletes with high, medium, and low performance among male athletes. On the contrary, a significant difference was detected between the imagery and mental toughness levels of female athletes with medium and high performances, showing that athletes in the high-performance range had higher levels of imagery (Eta2 = 8) and mental toughness (Eta2 = 10) than athletes in the medium- and low-performance ranges. The findings of this study show that imagery and emotional intelligence are important factors for sports performance. In this context, coaches and sports psychologists can include these parameters in their training programs to achieve the optimal performance range.

The use of the eye-fixation-related potential to investigate visual perception in professional domains with high attentional demand: a literature review

INTRODUCTION

Visual attention is a cognitive skill related to visual perception and neural activity, and also moderated by expertise, in time-constrained professional domains (e.g., aviation, driving, sport, surgery). However, the contribution of both perceptual and neural processes on performance has been studied separately in the literature.

DEVELOPMENT

We defend an integration of visual and neural signals to offer a more complete picture of the visual attention displayed by professionals of different skill levels when performing free-viewing tasks. Specifically, we propose to zoom the analysis in data related to the quiet eye and P300 component jointly, as a novel signal processing approach to evaluate professionals' visual attention.

CONCLUSION

This review highlights the advantages of using portable eye trackers and electroencephalogram systems altogether, as a promising technique for a better understanding of early cognitive components related to attentional processes. Altogether, the eye-fixation-related potentials method may provide a better understanding of the cognitive mechanisms employed by the participants in natural settings, revealing what visual information is of interest for participants and distinguishing the neural bases of visual attention between targets and non-targets whenever they perceive a stimulus during free viewing experiments.

fNIRS is capable of distinguishing laterality of lower body contractions

The use of functional near-infrared spectroscopy (fNIRS) for brain imaging during human movement continues to increase. This technology measures brain activity non-invasively using near-infrared light, is highly portable, and robust to motion artifact. However, the spatial resolution of fNIRS is lower than that of other imaging modalities. It is unclear whether fNIRS has sufficient spatial resolution to differentiate nearby areas of the cortex, such as the leg areas of the motor cortex. Therefore, the purpose of this study was to determine fNIRS' ability to discern laterality of lower body contractions. Activity in the primary motor cortex was recorded in forty participants (mean = 23.4 years, SD = 4.5, female = 23, male = 17) while performing unilateral lower body contractions. Contractions were performed at 30% of maximal force against a handheld dynamometer. These contractions included knee extension, knee flexion, dorsiflexion, and plantar flexion of the left and right legs. fNIRS signals were recorded and stored for offline processing and analysis. Channels of fNIRS data were grouped into regions of interest, with five tolerance conditions ranging from strict to lenient. Four of five tolerance conditions resulted in significant differences in cortical activation between hemispheres. During right leg contractions, the left hemisphere was more active than the right hemisphere. Similarly, during left leg contractions, the right hemisphere was more active than the left hemisphere. These results suggest that fNIRS has sufficient spatial resolution to distinguish laterality of lower body contractions. This makes fNIRS an attractive technology in research and clinical applications in which laterality of brain activity is required during lower body activity.

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, andKappaindex 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.

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.

Wireless EEG: A survey of systems and studies

The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturization. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 48 wireless EEG devices along with a number of important-sometimes difficult-to-obtain-features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorized by application and analyzed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary about pitfalls and caveats regarding this increasingly accessible line of research.

Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design

OBJECTIVE

We demonstrate and discuss the use of mobile electroencephalogram (EEG) for neuroergonomics. Both technical state of the art as well as measures and cognitive concepts are systematically addressed.

BACKGROUND

Modern work is increasingly characterized by information processing. Therefore, the examination of mental states, mental load, or cognitive processing during work is becoming increasingly important for ergonomics.

RESULTS

Mobile EEG allows to measure mental states and processes under real live conditions. It can be used for various research questions in cognitive neuroergonomics. Besides measures in the frequency domain that have a long tradition in the investigation of mental fatigue, task load, and task engagement, new approaches-like blink-evoked potentials-render event-related analyses of the EEG possible also during unrestricted behavior.

CONCLUSION

Mobile EEG has become a valuable tool for evaluating mental states and mental processes on a highly objective level during work. The main advantage of this technique is that working environments don't have to be changed while systematically measuring brain functions at work. Moreover, the workflow is unaffected by such neuroergonomic approaches.

The simultaneous changes in motor performance and EEG patterns in beta band during learning dart throwing skill in dominant and non-dominant hand

Background: Although changes in performance during the learning of various sports skills have been studied, however, how these changes at the brain level is still unknown. The aim of this study was to investigate simultaneous changes in motor performance and EEG patterns in beta band during learning dart throwing skill in dominant and non-dominant hand. Methodology: The samples consisted of 14 non-athlete students with an average age of 23 ± 2.5, which were divided into two group dominant hand (7) and non-dominant hand (7). Repeated measures ANOVA were used to measure data at the execution level and changes in EEG activity. Results: The results of this study at the performance level showed a significant reduction in the absolute error of dart throwing and at the same time at the brain level increased EEG activity in frontal and parietal-posterior regions along with decreased central area activity in acquisition and retention stages in both groups (P<.05). Also, there was a significant difference between the activity of EEG pattern in the dominant and non-dominant hand groups except for two channels AF3 and PO4 (P<.05). Conclusion: In general, the results of this study showed that along with relatively constant changes in performance during dart skill learning, relatively constant changes in EEG activity pattern occur, so that the concept of motor learning is also visible at the brain level. Also, the results of this study supported the existence of the different motor program for dominant and non-dominant hand control in the conditions of bilateral transfer control.

Study on exercise muscle fatigue based on sEMG and ECG data fusion and temporal convolutional network

BACKGROUND

Muscle fatigue is a crucial indicator to determine whether training is in place and to protect trainers.

PURPOSE

To make full use of morphological information of surface EMG and ECG signals in the time domain, a new idea and method for the fatigue assessment of exercise muscles based on data fusion is proposed in this paper.

METHODS

sEMG and ECG time series with the same length were obtained by signal preprocessing and sequence normalization, feature extraction of sequence tenses was realized by a deep learning network based on sequential convolution and signal fusion model of muscle fatigue evaluation was established by D-S evidence theory.

EXPERIMENT

Thirty volunteers were recruited and divided into three groups. ECG signals and sEMG signals at the biceps brachii of the right upper limb were monitored in a 20-minute exercise cycle.

RESULTS

The prediction result of TCN based on time domain signal is better than the commonly used KNN and SVM recognition algorithm, and the recognition accuracy of relaxed, excessive and fatigue by D-S fusion was 89%, 86%, 88.5%. The accuracy was 0.9055, 0.9494 and 0.9269, respectively. The recall rates of the three conditions were 0.9303, 0.9570 and 0.9435. The F-score of the three conditions was 0.8911, 0.8764 and 0.8837, respectively.

CONCLUSION

Based on time series and time series convolutional network, sEMG and ECG fusion of motor muscle recognition method can better distinguish different state information and has certain practical value in the fields of muscle evaluation, clinical diagnosis, wearable devices and so on.

Does high-definition transcranial direct current stimulation change brain electrical activity in professional female basketball players during free-throw shooting?

Differentiated brain activation in high-performance athletes supports neuronal mechanisms relevant to sports performance. Preparation for the motor action involves cortical and sub-cortical regions that can be non-invasively modulated by electrical current stimulation. This study aimed to investigate the effect of high-definition transcranial direct current stimulation (HD-tDCS) on electrical brain activity in professional female basketball players during free-throw shooting. Successful free-throw shooting (n = 2,361) from seven professional female basketball players was analyzed during two experimental conditions (HD-tDCS cathodic and sham) separated by 72 h. Three spectral bio-markers, Power Ratio Index (PRI), Delta Alpha Ratio (DAR), and Theta Beta Ratio (TBR) were measured (electroencephalography [EEG] Brain Products). Multi-channel HD-tDCS was applied for 20 min, considering current location and intensity for cathodic stimulation: FCC1h, AFF5h, AFF1h (-0.5 mA each), and FCC5h (ground). The within EEG analyses (pre and post HD-tDCS) of frontal channels (Fp1, Fp2, F3, F4, FC1, FC3) for 1 second epoch pre-shooting, showed increases in PRI (p < 0.001) and DAR (p < 0.001) for HD-tDCS cathodic condition, and in TBR for both conditions (cathodic, p = 0.01; sham, p = 0.002). Sub-group analysis divided the sample into less (n = 3; LSG) and more (n = 4; MSG) stable free-throw-shooting performers and revealed that increases in pre to post HD-tDCS in PRI only occurred for the LSG. These results suggest that the effect of HD-tDCS may induce changes in slow frontal frequency brain activities and that this alteration seems to be greater for players demonstrating a less stable free-throw shooting performance.

Topological Data Analysis as a New Tool for EEG Processing

Electroencephalography (EEG) is a widely used cerebral activity measuring device for both clinical and everyday life applications. In addition to denoising and potential classification, a crucial step in EEG processing is to extract relevant features. Topological data analysis (TDA) as an emerging tool enables to analyse and understand data from a different angle than traditionally used methods. As a higher dimensional analogy of graph analysis, TDA can model rich interactions beyond pairwise relations. It also distinguishes different dynamics of EEG time series. TDA remains largely unknown to the EEG processing community while it fits well the heterogeneous nature of EEG signals. This short review aims to give a quick introduction to TDA and how it can be applied to EEG analysis in various applications including brain-computer interfaces (BCIs). After introducing the objective of the article, the main concepts and ideas of TDA are explained. Next, how to implement it for EEG processing is detailed, and lastly the article discusses the benefits and limitations of the method.

A Sham-Controlled Study of Neurofeedback for Pain Management

Background

Neurofeedback (NFB) attempts to alter the brain’s electrophysiological activity and has shown potential as a pain management technique. Existing studies, however, often lack appropriate control groups or fail to assess whether electrophysiological activity has been successfully regulated. The current study is a randomized controlled trial comparing changes in brain activity and pain during NFB with those of a sham-control group.

Methods

An experimental pain paradigm in healthy participants was used to provide optimal control of pain sensation. Twenty four healthy participants were blind randomized to receive either 10 × NFB (with real EEG feedback) or 10 × sham (with false EEG feedback) sessions during noxious cold stimulation. Prior to actual NFB training, training protocols were individually determined for each participant based on a comparison of an initial 32-channel qEEG assessment administered at both baseline and during an experimental pain task. Each individual protocol was based on the electrode site and frequency band that showed the greatest change in amplitude during pain, with alpha or theta up-regulation at various electrode sites (especially Pz) the most common protocols chosen. During the NFB sessions themselves, pain was assessed at multiple times during each session on a 0–10 rating scale, and ANOVA was used to examine changes in pain ratings and EEG amplitude both across and during sessions for both NFB and sham groups.

Results

For pain, ANOVA trend analysis found a significant general linear decrease in pain across the 10 sessions (p = 0.015). However, no significant main or interaction effects of group were observed suggesting decreases in pain occurred independently of NFB. For EEG, there was a significant During Session X Group interaction (p = 0.004), which indicated that EEG amplitude at the training site was significantly closer to the target amplitude for the NFB compared to the sham group during painful stimulation, but this was only the case at the beginning of the cold task.

Conclusion

While these results must be interpreted within the context of an experimental pain model, they underline the importance of including an appropriate comparison group to avoid attributing naturally occurring changes to therapeutic effects.

Exercise under heat stress: thermoregulation, hydration, performance implications, and mitigation strategies

A rise in body core temperature and loss of body water via sweating are natural consequences of prolonged exercise in the heat. This review provides a comprehensive and integrative overview of how the human body responds to exercise under heat stress and the countermeasures that can be adopted to enhance aerobic performance under such environmental conditions. The fundamental concepts and physiological processes associated with thermoregulation and fluid balance are initially described, followed by a summary of methods to determine thermal strain and hydration status. An outline is provided on how exercise-heat stress disrupts these homeostatic processes, leading to hyperthermia, hypohydration, sodium disturbances, and in some cases exertional heat illness. The impact of heat stress on human performance is also examined, including the underlying physiological mechanisms that mediate the impairment of exercise performance. Similarly, the influence of hydration status on performance in the heat and how systemic and peripheral hemodynamic adjustments contribute to fatigue development is elucidated. This review also discusses strategies to mitigate the effects of hyperthermia and hypohydration on exercise performance in the heat by examining the benefits of heat acclimation, cooling strategies, and hyperhydration. Finally, contemporary controversies are summarized and future research directions are provided.

Quantitative EEG in sports: performance level estimation of professional female soccer players

Purpose

Measuring the peak performance of athletes remains a challenge in movement science and sports psychology. Non-invasive quantitative electroencephalography (QEEG) recordings can be used to analyze various factors in sports psychology.

Method

In this context, sports-related psychological factors were used to estimate the performance of Thai professional female soccer players before a competition. The QEEG recordings of thirty-two players were recorded three times: twice before a competition (once a week) and a week after a competition. Four factors of sports psychology were estimated and observed: anxiety, perceptual response to an acute bout of brain activity, assertiveness, and brain central fatigue. A brain topographic map (absolute power) and brain connectivity (coherence and amplitude asymmetry) data were used to analyze sports-related psychological factors. These factors were measurable based on the brain activity of the athletes and could be used to evaluate their performance during competitions by using QEEG values.

Results

Sports-related psychological performance was estimated by Pearson's correlation coefficients, which revealed that a quick perceptual response to an acute bout of brain activity could predict an athlete's performance during competition (r = .584, p = .000). Additionally, Spearman's correlation coefficients were used to estimate athletes performance. The results revealed a strong relationship ( r s =.634, p = .000), which was derived from the summation of anxiety and perceptual response to an acute bout of brain activity.

Conclusion

Consequently, the results of the present study can provide information to help staff coaches to choose the best performing players, representing an alternative method for accurately selecting key players in the competitive sports community.

The Impact of Vigorous Cycling Exercise on Visual Attention: A Study With the BR8 Wireless Dry EEG System

Many studies have reported that exercise can influence cognitive performance. But advancing our understanding of the interrelations between psychology and physiology in sports neuroscience requires the study of real-time brain dynamics during exercise in the field. Electroencephalography (EEG) is one of the most powerful brain imaging technologies. However, the limited portability and long preparation time of traditional wet-sensor systems largely limits their use to laboratory settings. Wireless dry-sensor systems are emerging with much greater potential for practical application in sports. Hence, in this paper, we use the BR8 wireless dry-sensor EEG system to measure P300 brain dynamics while cycling at various intensities. The preparation time was mostly less than 2 min as BR8 system’s dry sensors were able to attain the required skin-sensor interface impedance, enabling its operation without any skin preparation or application of conductive gel. Ten participants performed four sessions of a 3 min rapid serial visual presentation (RSVP) task while resting and while cycling. These four sessions were pre-CE (RSVP only), low-CE (RSVP in 40–50% of max heart rate), vigorous-CE (RSVP in 71–85% of max heart rate) and post-CE (RSVP only). The recorded brain signals demonstrate that the P300 amplitudes, observed at the Pz channel, for the target and non-target responses were significantly different in all four sessions. The results also show decreased reaction times to the visual attention task during vigorous exercise, enriching our understanding of the ways in which exercise can enhance cognitive performance. Even though only a single channel was evaluated in this study, the quality and reliability of the measurement using these dry sensor-based EEG systems is clearly demonstrated by our results. Further, the smooth implementation of the experiment with a dry system and the success of the data analysis demonstrate that wireless dry EEG devices can open avenues for real-time measurement of cognitive functions in athletes outside the laboratory.

Brain activity during real-time walking and with walking interventions after stroke: a systematic review

Investigations of real-time brain activations during walking have become increasingly important to aid in recovery of walking after a stroke. Individual brain activation patterns can be a valuable biomarker of neuroplasticity during the rehabilitation process and can result in improved personalized medicine for rehabilitation. The purpose of this systematic review is to explore the brain activation characteristics during walking post-stroke by determining: (1) if different components of gait (i.e., initiation/acceleration, steady-state, complex) result in different brain activations, (2) whether brain activations differ from healthy individuals. Six databases were searched resulting in 22 studies. Initiation/acceleration showed bilateral activation in frontal areas; steady-state and complex walking showed broad activations with the majority exploring and finding increases in frontal regions and some studies also showing increases in parietal activation. Asymmetrical activations were often related to performance asymmetry and were more common in studies with slower gait speed. Hyperactivations and asymmetrical activations commonly decreased with walking interventions and as walking performance improved. Hyperactivations often persisted in individuals who had experienced severe strokes. Only a third of the studies included comparisons to a healthy group: individuals post-stroke employed greater brain activation compared to young adults, while comparisons to older adults were less clear and limited. Current literature suggests some indicators of walking recovery however future studies investigating more brain regions and comparisons with healthy age-matched adults are needed to further understand the effect of stroke on walking-related brain activation.

Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the expert classification of the ICs by visual inspection. Brain dynamics in two different groups of denoised EEG signals, reconstructed after having removed the artifactual ICs identified by either visual inspection or the automated methods, was assessed by calculating microstate topographies, microstate metrics (duration, occurrence, and coverage), and directional predominance (based on transition probabilities). No statistically significant differences between the expert and the automated classification of the artifactual ICs were found (p > 0.05). Cronbach’s α values assessed the high test–retest reliability of microstate parameters for EEG datasets denoised by the automated procedure. The total EEG signal variance explained by the sets of global microstate templates was about 80% for all denoised EEG datasets, with no significant differences between groups. For the differently denoised EEG datasets in the two recording conditions, we found that the global microstate templates and the sequences of global microstates were very similar (p < 0.01). Descriptive statistics and Cronbach’s α of microstate metrics highlighted no significant differences and excellent consistency between groups (p > 0.5). These results confirm the ability of the optimized fingerprint method and the ARCI approach to effectively remove physiological artifacts from EEG recordings while preserving global brain dynamics. They also suggest that microstate analysis could represent a novel approach for assessing the ability of an EEG denoising method to remove artifacts without altering brain dynamics.

Carbohydrate Mouth Rinse Mitigates Mental Fatigue Effects on Maximal Incremental Test Performance, but Not in Cortical Alterations

Detrimental mental fatigue effects on exercise performance have been documented in constant workload and time trial exercises, but effects on a maximal incremental test (MIT) remain poorly investigated. Mental fatigue-reduced exercise performance is related to an increased effort sensation, likely due to a reduced prefrontal cortex (PFC) activation and inhibited spontaneous behavior. Interestingly, only a few studies verified if centrally active compounds may mitigate such effects. For example, carbohydrate (CHO) mouth rinse potentiates exercise performance and reduces effort sensation, likely through its effects on PFC activation. However, it is unknown if this centrally mediated effect of CHO mouth rinse may mitigate mental fatigue-reduced exercise performance. After a proof-of-principle study, showing a mental fatigue-reduced MIT performance, we observed that CHO mouth rinse mitigated MIT performance reductions in mentally fatigued cyclists, regardless of PFC alterations. When compared to placebo, mentally fatigued cyclists improved MIT performance by 2.24–2.33% when rinsing their mouth with CHO during MIT. However, PFC and motor cortex activation during MIT in both CHO and placebo mouth rinses were greater than in mental fatigue. Results showed that CHO mouth rinse mitigated the mental fatigue-reduced MIT performance, but challenged the role of CHO mouth rinse on PFC and motor cortex activation.

New Directions in Exercise Prescription: Is There a Role for Brain-Derived Parameters Obtained by Functional Near-Infrared Spectroscopy?

In the literature, it is well established that regular physical exercise is a powerful strategy to promote brain health and to improve cognitive performance. However, exact knowledge about which exercise prescription would be optimal in the setting of exercise–cognition science is lacking. While there is a strong theoretical rationale for using indicators of internal load (e.g., heart rate) in exercise prescription, the most suitable parameters have yet to be determined. In this perspective article, we discuss the role of brain-derived parameters (e.g., brain activity) as valuable indicators of internal load which can be beneficial for individualizing the exercise prescription in exercise–cognition research. Therefore, we focus on the application of functional near-infrared spectroscopy (fNIRS), since this neuroimaging modality provides specific advantages, making it well suited for monitoring cortical hemodynamics as a proxy of brain activity during physical exercise.

Neurodiagnostics in Sports: Investigating the Athlete's Brain to Augment Performance and Sport-Specific Skills

Enhancing performance levels of athletes during training and competition is a desired goal in sports. Quantifying training success is typically accompanied by performance diagnostics including the assessment of sports-relevant behavioral and physiological parameters. Even though optimal brain processing is a key factor for augmented motor performance and skill learning, neurodiagnostics is typically not implemented in performance diagnostics of athletes. We propose, that neurodiagnostics via non-invasive brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) will offer novel perspectives to quantify training-induced neuroplasticity and its relation to motor behavior. A better understanding of such a brain-behavior relationship during the execution of sport-specific movements might help to guide training processes and to optimize training outcomes. Furthermore, targeted non-invasive brain stimulation such as transcranial direct current stimulation (tDCS) might help to further enhance training outcomes by modulating brain areas that show training-induced neuroplasticity. However, we strongly suggest that ethical aspects in the use of non-invasive brain stimulation during training and/or competition need to be addressed before neuromodulation can be considered as a performance enhancer in sports.

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. To trace physiological underpinnings of this relationship, we investigated EEG and behavioral performance of 36 healthy adults recorded at rest and during visual attention tasks: visual search and gun shooting. All measures were repeated two months later to determine stability of the results. Correlation analyses revealed that within the range of 2–45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. Participants with lower global beta-2 resting-state power (gB2rest) showed weaker frontoparietal connectivity and greater capacity for its modifications, as indicated by changes in phase correlations of the EEG signals. At the same time shorter reaction times and improved shooting accuracy were found, in both test and retest, in participants with low gB2rest compared to higher gB2rest values. We posit that weak frontoparietal connectivity permits flexible network reconfigurations required for improved performance in everyday tasks.

The Effect of an Integrated Neurofeedback and Biofeedback Training Intervention on Ice Hockey Shooting Performance

The purpose of this study was to investigate the effectiveness of a sensorimotor rhythm (SMR) neurofeedback training (NFT) and biofeedback training (BFT) intervention on ice hockey shooting performance. Specifically, the purpose was to examine (a) whether an NFT/BFT program could improve ice hockey shooting performance, (b) whether the implementation of an SMR-NFT intervention leads to neurological adaptations during performance, and (c) whether such neurological changes account for improvement in shooting performance. Using a longitudinal stratified random control design, results demonstrated that while both SMR-NFT/BFT and control groups improved performance, the rate of improvement for the SMR-NFT/BFT group was significantly higher than the control. Participants in the SMR-NFT/BFT group demonstrated the ability to significantly increase SMR power from pre- to postintervention in the lab. However, no significant changes in SMR power were found during shooting performance. This result may be suggestive of differing cortical activity present during motor-skill preparation.

Differences in motivation during the bench press movement with progressive loads using EEG analysis

Considering our preliminary research with EEG analysis of the bench press in experienced powerlifters, we hypothesized that there would be significant differences in motivation between novice and elite powerlifters. Therefore the main objective of this study was to identify patterns of frontal alpha asymmetry (FAA) of the prime movers by alpha frequency band analysis (named as alpha motivation values) for each 35–100% one-repetition maximum (1RM) during the flat bench press. Ten novice powerlifters with no more than 2.5 years of resistance training experience and ten elite powerlifters with at least 7.5 years of training experience participated in the study. All participants were required to squat, bench press, and deadlift 100, 125, and 150% of their body mass, respectively. The athletes constituted a homogeneous group with respect to age (mean 22.3 ± 0.5 years). The EEG recordings were conducted using automatic headcups with 19 electrodes that were placed according to the International 10-20 Electrode Placement System. Signals from 8–12 Hz considering points F3 and F4 were analyzed. Furthermore, electromyographic (EMG) signals from the trapezius muscle were recorded. Before testing, moods and emotions of subjects were assessed to eliminate subjects with intense emotions. The results showed brain activity before, during and after cognitive and motor performance using electroencephalography (EEG). However, considering the still existing problems of movement artefacts during EEG measurements, eligible sports and exercises are limited to those that are relatively motionless during execution. Further studies are needed to confirm these preliminary results.

Imaging the human hippocampus with optically-pumped magnetoencephalography

Optically-pumped (OP) magnetometers allow magnetoencephalography (MEG) to be performed while a participant's head is unconstrained. To fully leverage this new technology, and in particular its capacity for mobility, the activity of deep brain structures which facilitate explorative behaviours such as navigation, must be detectable using OP-MEG. One such crucial brain region is the hippocampus. Here we had three healthy adult participants perform a hippocampal-dependent task - the imagination of novel scene imagery - while being scanned using OP-MEG. A conjunction analysis across these three participants revealed a significant change in theta power in the medial temporal lobe. The peak of this activated cluster was located in the anterior hippocampus. We repeated the experiment with the same participants in a conventional SQUID-MEG scanner and found similar engagement of the medial temporal lobe, also with a peak in the anterior hippocampus. These OP-MEG findings indicate exciting new opportunities for investigating the neural correlates of a range of crucial cognitive functions in naturalistic contexts including spatial navigation, episodic memory and social interactions.

Hyperscanning of Interactive Juggling: Expertise Influence on Source Level Functional Connectivity

Hyperscanning studies, wherein brain activity is recorded from multiple participants simultaneously, offer an opportunity to investigate interpersonal dynamics during interactive tasks at the neurophysiological level. In this study, we employed a dyadic juggling paradigm and electroencephalography (EEG) hyperscanning to evaluate functional connectivity between EEG sources within and between jugglers’ brains during individual and interactive juggling. We applied graph theoretical measures to identify significant differences in functional connectivity between the individual and interactive juggling conditions. Connectivity was measured in multiple juggler pairs with various skill levels where dyads were either skill-level matched or skill-level unmatched. We observed that global efficiency was reduced during paired juggling for less skilled jugglers and increased for more skilled jugglers. When jugglers were skill-level matched, additional reductions were found in the mean clustering coefficient and small-world topology during interactive juggling. A significant difference in hemispheric brain lateralization was detected between skill-level matched and skill-level unmatched jugglers during interactive juggling: matched jugglers had an increased right hemisphere lateralization while unmatched jugglers had an increased left hemisphere lateralization. These results reveal multiple differences in functional brain networks during individual and interactive juggling and suggest that similarities and disparities in individual skills can impact inter-brain dynamics in the performance and learning of motor tasks.

A new algorithm for blink correction adaptive to inter- and intra-subject variability

Electroencephalographic (EEG) signals are constantly superimposed with biological artifacts. In particular, spontaneous blinks represent a recurrent event that cannot be easily avoided. The main goal of this paper is to present a new algorithm for blink correction (ABC) that is adaptive to inter- and intra-subject variability. The whole process of designing a Brain-Computer Interface (BCI)-based EEG experiment is highlighted. From sample size determination to classification, a mixture of the standardized low-resolution electromagnetic tomography (sLORETA) for source localization and time restriction, followed by Riemannian geometry classifiers is featured. Comparison between ABC and the commonly-used Independent Component Analysis (ICA) for blinks removal shows a net amelioration with ABC. With the same pipeline using uncorrected data as a reference, ABC improves classification by 5.38% in average, whereas ICA deteriorates by -2.67%. Furthermore, while ABC accurately reconstructs blink-free data from simulated data, ICA yields a potential difference up to 200% from the original blink-free signal and an increased variance of 30.42%. Finally, ABC's major advantages are ease of visualization and understanding, low computation load favoring simple real-time implementation, and lack of spatial filtering, which allows for more flexibility during the classification step.

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Psycho-physiological analysis has gained greater attention in the last few decades in various fields including multimodal systems. Researchers use psychophysiological feedback devices such as skin conductance (SC), Electroencephalography (EEG) and Electrocardiography (ECG) to detect the affective states of the users during task performance. Psycho-physiological feedback has been successful in detection of the cognitive states of users in human-computer interaction (HCI). Recently, in game studies, psycho-physiological feedback has been used to capture the user experience and the effect of interaction on human psychology. This paper reviews several psycho-physiological, cognitive, and affective assessment studies and focuses on the use of psychophysiological signals in estimating the user’s cognitive and emotional states in multimodal systems. In this paper, we review the measurement techniques and methods that have been used to record psycho-physiological signals as well as the cognitive and emotional states in a variety of conditions. The aim of this review is to conduct a detailed study to identify, describe and analyze the key psycho-physiological parameters that relate to different mental and emotional states in order to provide an insight into key approaches. Furthermore, the advantages and limitations of these approaches are also highlighted in this paper. The findings state that the classification accuracy of >90% has been achieved in classifying emotions with EEG signals. A strong correlation between self-reported data, HCI experience, and psychophysiological data has been observed in a wide range of domains including games, human-robot interaction, mobile interaction, and simulations. An increase in β and γ -band activity have been observed in high intense games and simulations.

Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

EEG recordings are generally affected by interference from physiological and non-physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement. We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal. Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications.

Contact Pressure and Flexibility of Multipin Dry EEG Electrodes

In state-of-the-art electroencephalography (EEG) Silver/Silver-Chloride electrodes are applied together with electrolyte gels or pastes. Their application requires extensive preparation, trained medical staff and limits measurement time and mobility. We recently proposed a novel multichannel cap system for dry EEG electrodes for mobile and out-of-the-lab EEG acquisition. During the tests with these novel polymer-based multipin dry electrodes, we observed that the quality of the recording depends on the applied normal force and resulting contact pressure. Consequently, in this paper we systematically investigate the influence of electrode-skin contact pressure and electrode substrate flexibility on interfacial impedance and perceived wearing comfort in a study on 12 volunteers. The normal force applied to the electrode was varied between the minimum required force to achieve impedances and a maximum of 4 N, using a new force measurement applicator. We found that for a polymer shore hardness A98, with increasing normal force, the impedance decreases from and to and at frontal hairless and temporal hairy positions, respectively. Similar results were obtained for shore A90, A80, and A70. The best compromise of low and stable impedances as well as a good wearing comfort was determined for applied normal forces between 2 and 3 N using electrodes with shore A98 or A90. Our results provide the basis for improved EEG cap designs with optimal wearing comfort and recording quality for dry multipin electrodes, which will enable new fields of application for EEG.

The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability

One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with no-clinical aims, providing a powerful tool to neuroscientific research. However, these systems still suffer from a critical limitation, consisting in the use of wet electrodes, that are uncomfortable and require expertise to install and time from the user. In this context, dozens of different concepts of EEG dry electrodes have been recently developed, and there is the common opinion that they are reaching traditional wet electrodes quality standards. However, although many papers have tried to validate them in terms of signal quality and usability, a comprehensive comparison of different dry electrode types from multiple points of view is still missing. The present work proposes a comparison of three different dry electrode types, selected among the main solutions at present, against wet electrodes, taking into account several aspects, both in terms of signal quality and usability. In particular, the three types consisted in gold-coated single pin, multiple pins and solid-gel electrodes. The results confirmed the great standards achieved by dry electrode industry, since it was possible to obtain results comparable to wet electrodes in terms of signals spectra and mental states classification, but at the same time drastically reducing the time of montage and enhancing the comfort. In particular, multiple-pins and solid-gel electrodes overcome gold-coated single-pin-based ones in terms of comfort.

Evaluation of the social phobia scale and social interaction anxiety scale as assessments of performance and interaction anxiety

The DSM-5 classification of performance anxiety as distinct from social anxiety underlines the need for specific measurement instruments. The Social Phobia Scale (SPS) and Social Interaction Anxiety Scale (SIAS) are widely used assessments of performance and general interaction anxiety, yet few studies have examined their criterion validity using actual performance and interaction stressors. The current study is the first to assess the scales' convergent and discriminant validity using multimodal anxiety indices including psychophysiological reactivity, which is a key characterizing feature of performance anxiety. Ninety-three individuals completed the SPS and SIAS and participated in performance and interaction challenges. Anxiety response was assessed with criterion measures of self-reported anxiety, observer-rated anxiety, heart rate and skin conductance. Both scales were moderately correlated with self-report and observer-rated anxiety (r's = 0.21-0.62) and weakly correlated with physiological measures (r's = 0.07-0.35), generally supporting convergent criterion validity. Discriminant criterion validity was not supported, however, with the SIAS and SPS showing similar correlations with each criterion measure for both social challenges. These findings suggest the SIAS and SPS are valid instruments for the assessment of social anxiety but may not be suitable for the specific measurement of performance and interaction anxiety.

The neurobiology of placebo effects in sports: EEG frontal alpha asymmetry increases in response to a placebo ergogenic aid

The performance enhancing (ergogenic) placebo effect is elicited by an inert treatment and caused by positive affective appraisal of effort perception. Frontal alpha asymmetry (FAA) is a neurobiological correlate of positive affect. This study investigates, whether receiving an ergogenic placebo increases FAA and whether scores on the behavioral inhibition and activation system (BIS/BAS) scales affect this increase in FAA. Nineteen competitive male cyclists (37.26 ± 9.82 years) performed two maximum effort time trials. The first served as baseline for the second intervention time trial, where athletes received a placebo ergogenic aid or no treatment. We recorded FAA using EEG throughout all time trials and assessed BIS/BAS by questionnaire. There was a significant difference in change from baseline to intervention time trial in FAA during cycling in response to the placebo ergogenic aid compared to the control group. BIS, the BAS subscale Drive and the BAS-BIS difference score significantly co-varied with the change in FAA from baseline to intervention time trial in response to the placebo ergogenic aid. Administering a placebo ergogenic aid significantly influenced FAA during maximum effort cycling. Those athletes with a more pronounced goal seeking persistence and an overall dominance of the BAS over the BIS showed a significantly greater increase in FAA in response to a placebo ergogenic aid. A more pronounced BIS, however, seems to antagonize the increase in FAA associated with the ergogenic placebo response.

Oscillatory brain activity during acute exercise: Tonic and transient neural response to an oddball task

Intense physical exercise exerts measurable changes at various physiological levels that are well documented in the literature. However, despite the key role of the brain in processing inputs from internal organ systems and the external environment to coordinate and optimize behavior, little is known about brain dynamics during exercise. The present study investigates tonic and transient oscillatory brain activity in a group of participants performing an oddball task during a single bout of aerobic exercise. Twenty young males (19-32 years) were recruited for two experimental sessions on separate days. EEG activity was recorded during a session of cycling at 80% (moderate-to-high intensity) of VO_2max (maximum rate of oxygen consumption) while participants responded to infrequent targets (red square and big blue circle) presented among frequent nontargets (small blue circle). This was compared to a (baseline) light intensity session (30% VO_2max ) to control any potential effect of dual tasking (i.e., pedaling and performing the oddball task). A cluster-based nonparametric permutations test revealed an increase in power across the entire frequency spectrum during the moderate-to-high intensity exercise compared to light intensity. Furthermore, the more salient target (red square) elicited a lower increase in (stimulus-evoked) theta power in the 80% VO_2max than in the light intensity condition. Alpha and lower beta power decreased less in the standard trials (small blue circle) during the moderate-to-high exercise condition than in the light exercise condition. The present study unveils, for the first time, a complex brain activity pattern during vigorous exercise while attending to task-relevant stimuli.

Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging in Exercise⁻Cognition Science: A Systematic, Methodology-Focused Review

For cognitive processes to function well, it is essential that the brain is optimally supplied with oxygen and blood. In recent years, evidence has emerged suggesting that cerebral oxygenation and hemodynamics can be modified with physical activity. To better understand the relationship between cerebral oxygenation/hemodynamics, physical activity, and cognition, the application of state-of-the art neuroimaging tools is essential. Functional near-infrared spectroscopy (fNIRS) is such a neuroimaging tool especially suitable to investigate the effects of physical activity/exercises on cerebral oxygenation and hemodynamics due to its capability to quantify changes in the concentration of oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) non-invasively in the human brain. However, currently there is no clear standardized procedure regarding the application, data processing, and data analysis of fNIRS, and there is a large heterogeneity regarding how fNIRS is applied in the field of exercise⁻cognition science. Therefore, this review aims to summarize the current methodological knowledge about fNIRS application in studies measuring the cortical hemodynamic responses during cognitive testing (i) prior and after different physical activities interventions, and (ii) in cross-sectional studies accounting for the physical fitness level of their participants. Based on the review of the methodology of 35 as relevant considered publications, we outline recommendations for future fNIRS studies in the field of exercise⁻cognition science.