Brain and muscle activity during fatiguing maximum-speed knee movement.

Although the underlying mechanisms behind upper limb (e.g., finger) motor slowing during movements performed at the maximum voluntary rate have been explored, the same cannot be said for the lower limb. This is especially relevant considering the lower limb's larger joints and different functional patterns. Despite the similar motor control base, previously found differences in movement patterns and segment inertia may lead to distinct central and peripheral manifestations of fatigue in larger joint movement. Therefore, we aimed to explore these manifestations in a fatiguing knee maximum movement rate task by measuring brain and muscle activity, as well as brain-muscle coupling using corticomuscular coherence, during this task. A significant decrease in knee movement rate up to half the task duration was observed. After an early peak, brain activity showed a generalized decrease during the first half of the task, followed by a plateau, whereas knee flexor muscle activity showed a continuous decline. A similar decline was also seen in corticomuscular coherence but for both flexor and extensor muscles. The electrophysiological manifestations associated with knee motor slowing therefore showed some common and some distinct aspects compared with smaller joint tasks. Both central and peripheral manifestations of fatigue were observed; the changes seen in both EEG and electromyographic (EMG) variables suggest that multiple mechanisms were involved in exercise regulation and fatigue development.NEW & NOTEWORTHY The loss of knee movement rate with acute fatigue induced by high-speed movement is associated with both central and peripheral electrophysiological changes, such as a decrease in EEG power, increased agonist-antagonist cocontraction, and impaired brain-muscle coupling. These findings had not previously been reported for the knee joint, which shows functional and physiological differences compared with the existing findings for smaller upper limb joints.

Modulating Individual Alpha Frequency through Short-Term Neurofeedback for Cognitive Enhancement in Healthy Young Adults.

Human alpha oscillation (7-13 Hz) has been extensively studied over the years for its connection with cognition. The individual alpha frequency (IAF), defined as the frequency that provides the highest power in the alpha band, shows a positive correlation with cognitive processes. The modulation of alpha activities has been accomplished through various approaches aimed at improving cognitive performance. However, very few studies focused on the direct modulation of IAF by shifting the peak frequency, and the understanding of IAF modulation remains highly limited. In this study, IAFs of healthy young adults were up-regulated through short-term neurofeedback training using haptic feedback. The results suggest that IAFs have good trainability and are up-regulated, also that IAFs are correlated with the enhanced cognitive performance in mental rotation and n-back tests compared to sham-neurofeedback control. This study demonstrates the feasibility of self-regulating IAF for cognition enhancement and provides potential therapeutic benefits for cognitive-impaired patients.

Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis.

OBJECTIVE: Multi-frequency-modulated visual stimulation scheme has been shown effective for the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) recently, especially in increasing the visual target number with less stimulus frequencies and mitigating the visual fatigue. However, the existing calibration-free recognition algorithms based on the traditional canonical correlation analysis (CCA) cannot provide the merited performance. APPROACH: To improve the recognition performance, this study proposes a phase difference constrained CCA (pdCCA), which assumes that the multi-frequency-modulated SSVEPs share a common spatial filter over different frequencies and have a specified phase difference. Specifically, during the CCA computation, the phase differences of the spatially filtered SSVEPs are constrained using the temporal concatenation of the sine-cosine reference signals with the pre-defined initial phases. MAIN RESULTS: We evaluate the performance of the proposed pdCCA-based method on three representative multi-frequency-modulated visual stimulation paradigms (i.e., based on the multi-frequency sequential coding, the dual-frequency, and the amplitude modulation). The evaluation results on four SSVEP datasets (Dataset Ia, Ib, II, and III) show that the pdCCA-based method can significantly outperform the current CCA method in terms of recognition accuracy. It improves the accuracy by 22.09% in Dataset Ia, 20.86% in Dataset Ib, 8.61% in Dataset II, and 25.85% in Dataset III. SIGNIFICANCE: The pdCCA-based method, which actively controls the phase difference of the multi-frequency-modulated SSVEPs after spatial filtering, is a new calibration-free method for multi-frequency-modulated SSVEP-based BCIs.

Stimulus-Stimulus Transfer Based on Time-Frequency-Joint Representation in SSVEP-Based BCIs.

OBJECTIVE: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) require extensive and costly calibration to achieve high performance. Using transfer learning to re-use existing calibration data from old stimuli is a promising strategy, but finding commonalities in the SSVEP signals across different stimuli remains a challenge. METHOD: This study presents a new perspective, namely time-frequency-joint representation, in which SSVEP signals corresponding to different stimuli can be synchronized, and thus can emphasize common components. According to this time-frequency-joint representation, an adaptive decomposition technique based on the multi-channel adaptive Fourier decomposition (MAFD) is proposed to adaptively decompose SSVEP signals of different stimuli simultaneously. Then, common components can be identified and transferred across stimuli. RESULTS: A simulation study on public SSVEP datasets demonstrates that the proposed stimulus-stimulus transfer method has the ability to extract and transfer these common components across stimuli. By using calibration data from eight source stimuli, the proposed stimulus-stimulus transfer method can generate SSVEP templates of other 32 target stimuli. It boosts the ITR of the stimulus-stimulus transfer based recognition method from 95.966 bits/min to 123.684 bits/min. CONCLUSION: By extracting and transfer common components across stimuli in the proposed time-frequency-joint representation, the proposed stimulus-stimulus transfer method produces good classification performance without requiring calibration data of target stimuli. SIGNIFICANCE: This study provides a synchronization standpoint to analyze and model SSVEP signals. In addition, the proposed stimulus-stimulus method shortens the calibration time and thus improve comfort, which could facilitate real-world applications of SSVEP-based BCIs.

Online Adaptation Boosts SSVEP-Based BCI Performance.

OBJECTIVE: A user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) prefers no calibration for its target recognition algorithm, however, the existing calibration-free schemes perform still far behind their calibration-based counterparts. To tackle this issue, learning online from the subject's unlabeled data is investigated as a potential approach to boost the performance of the calibration-free SSVEP-based BCIs. METHODS: An online adaptation scheme is developed to tune the spatial filters using the online unlabeled data from previous trials, and then developing the online adaptive canonical correlation analysis (OACCA) method. RESULTS: A simulation study on two public SSVEP datasets (Dataset I and II) with a total of 105 subjects demonstrated that the proposed online adaptation scheme can boost the CCA's averaged information transfer rate (ITR) from 94.60 to 158.87 bits/min in Dataset I and from 85.80 to 123.91 bits/min in Dataset II. Furthermore, in our online experiment it boosted the CCA's ITR from 55.81 bits/min to 95.73 bits/min. More importantly, this online adaptation scheme can be easily combined with any spatial filtering-based algorithms to achieve online learning. CONCLUSION: By online adaptation, the proposed OACCA performed much better than the calibration-free CCA, and comparable to the calibration-based algorithms. SIGNIFICANCE: This work provides a general way for the SSVEP-based BCIs to learn online from unlabeled data and thus avoid calibration.

The Influence of an Alpha Band Neurofeedback Training in Heart Rate Variability in Athletes.

Neurofeedback training is a technique which has seen a widespread use in clinical applications, but has only given its first steps in the sport environment. Therefore, there is still little information about the effects that this technique might have on parameters, which are relevant for athletes' health and performance, such as heart rate variability, which has been linked to physiological recovery. In the sport domain, no studies have tried to understand the effects of neurofeedback training on heart rate variability, even though some studies have compared the effects of doing neurofeedback or heart rate biofeedback training on performance. The main goal of the present study was to understand if alpha-band neurofeedback training could lead to increases in heart rate variability. 30 male student-athletes, divided into two groups, (21.2 ± 2.62 year 2/week protocol and 22.6 ± 1.1 year 3/week protocol) participated in the study, of which three subjects were excluded. Both groups performed a pre-test, a trial session and 12 neurofeedback sessions, which consisted of 25 trials of 60 s of a neurofeedback task, with 5 s rest in-between trials. The total neurofeedback session time for each subject was 300 min in both groups. Throughout the experiment, electroencephalography and heart rate variability signals were recorded. Only the three sessions/week group revealed significant improvements in mean heart rate variability at the end of the 12 neurofeedback sessions (p = 0.05); however, significant interaction was not found when compared with both groups. It is possible to conclude that neurofeedback training of individual alpha band may induce changes in heart rate variability in physically active athletes.

The Influence of Noise in the Neurofeedback Training Sessions in Student Athletes.

Considering that athletes constantly practice and compete in noisy environments, the aim was to investigate if performing neurofeedback training in these conditions would yield better results in performance than in silent ones. A total of forty-five student athletes aged from 18 to 35 years old and divided equally into three groups participated in the experiment (mean ± SD for age: 22.02 ± 3.05 years). The total neurofeedback session time for each subject was 300 min and were performed twice a week. The environment in which the neurofeedback sessions were conducted did not seem to have a significant impact on the training's success in terms of alpha relative amplitude changes (0.04 ± 0.08 for silent room versus 0.07 ± 0.28 for noisy room, p = 0.740). However, the group exposed to intermittent noise appears to have favourable results in all performance assessments (p = 0.005 for working memory and p = 0.003 for reaction time). The results of the study suggested that performing neurofeedback training in an environment with intermittent noise can be interesting to athletes. Nevertheless, it is imperative to perform a replicated crossover design.

An Exploratory Study of Training Intensity in EEG Neurofeedback.

Neurofeedback training has shown benefits in clinical treatment and behavioral performance enhancement. Despite the wide range of applications, no consensus has been reached about the optimal training schedule. In this work, an EEG neurofeedback practical experiment was conducted aimed at investigating the effects of training intensity on the enhancement of the amplitude in the individual upper alpha band. We designed INTENSIVE and SPARSE training modalities, which differed regarding three essential aspects of training intensity: the number of sessions, the duration of a session, and the interval between sessions. Nine participants in the INTENSIVE group completed 4 sessions with 37.5 minutes each during consecutive days, while nine participants in the SPARSE group performed 6 sessions of 25 minutes spread over approximately 3 weeks. As a result, regarding the short-term effects, the upper alpha band amplitude change within sessions did not significantly differ between the two groups. Nonetheless, only the INTENSIVE group showed a significant increase in the upper alpha band amplitude. However, for the sustained effects across sessions, none of the groups showed significant changes in the upper alpha band amplitude across the whole course of training. The findings suggest that the progression within session is favored by the intensive design. Therefore, based on these findings, it is proposed that training intensity influences EEG self-regulation within sessions. Further investigations are needed to isolate different aspects of training intensity and effectively confirm if one modality globally outperforms the other.

Session Frequency Matters in Neurofeedback Training of Athletes.

Neurofeedback training has been an increasingly used technique and is taking its first steps in sport. Being at an embryonic stage, it is difficult to find consensus regarding the applied methodology to achieve the best results. This study focused on understanding one of the major methodological issues-the training session frequency. The aim of the investigation was to understand if there are differences between performing two sessions or three sessions per week in enhancement of alpha activity and improvement of cognition; and in case there are differences, infer the best protocol. Forty-five athletes were randomly assigned to the three-session-training-per-week group, the two-session-training-per-week group and a control group. The results showed that neurofeedback training with three sessions per week was more effective in increase of alpha amplitude during neurofeedback training than two sessions per week. Furthermore, only the three-session-per-week group showed significant enhancement in N-back and oddball performance after training. The findings suggested more condensed training protocol lead to better outcomes, providing guidance on neurofeedback protocol design in order to optimize training efficacy.

Generative Art with Swarm Landscapes.

We present a generative swarm art project that creates 3D animations by running a Particle Swarm Optimization algorithm over synthetic landscapes produced by an objective function. Different kinds of functions are explored, including mathematical expressions, Perlin noise-based terrain, and several image-based procedures. A method for displaying the particle swarm exploring the search space in aesthetically pleasing ways is described. Several experiments are detailed and analyzed and a number of interesting visual artifacts are highlighted.

Inter- and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-Based BCIs.

OBJECTIVE: Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver a high information transfer rate (ITR) usually require subject's calibration data to learn the class- and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration. This study presents a transfer learning scheme to substantially reduce the calibration effort. METHODS: Inspired by the parameter-based and instance-based transfer learning techniques, we propose a subject transfer based canonical correlation analysis (stCCA) method which utilizes the knowledge within subject and between subjects, thus requiring few calibration data from a new subject. RESULTS: The evaluation study on two SSVEP datasets (from Tsinghua and UCSD) shows that the stCCA method performs well with only a small amount of calibration data, providing an ITR at 198.18±59.12 (bits/min) with 9 calibration trials in the Tsinghua dataset and 111.04±57.24 (bits/min) with 3 trials in the UCSD dataset. Such performances are comparable to those from using the multi-stimulus CCA (msCCA) and the ensemble task-related component analysis (eTRCA) methods with the minimally required calibration data (i.e., at least 40 trials in the Tsinghua dataset and at least 12 trials in the UCSD dataset), respectively. CONCLUSION: Inter- and intra-subject transfer helps the recognition method achieve high ITR with extremely little calibration effort. SIGNIFICANCE: The proposed approach saves much calibration effort without sacrificing the ITR, which would be significant for practical SSVEP-based BCIs.

Dark Proteome Database: Studies on Disorder.

There is a misconception that intrinsic disorder in proteins is equivalent to darkness. The present study aims to establish, in the scope of the Swiss-Prot and Dark Proteome databases, the relationship between disorder and darkness. Three distinct predictors were used to calculate the disorder of Swiss-Prot proteins. The analysis of the results obtained with the used predictors and visualization paradigms resulted in the same conclusion that was reached before: disorder is mostly unrelated to darkness.

Changes of EEG phase synchronization and EOG signals along the use of steady state visually evoked potential-based brain computer interface.

OBJECTIVE: The steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI) has demonstrated relatively high performance with little user training, and thus becomes a popular BCI paradigm. However, due to the performance deterioration over time, its robustness and reliability appear not sufficient to allow a non-expert to use outside laboratory. It would be thus helpful to study what happens behind the decreasing tendency of the BCI performance. APPROACH: This paper explores the changes of brain networks and electrooculography (EOG) signals to investigate the cognitive capability changes along the use of the SSVEP-based BCI. The EOG signals are characterized by the blink amplitudes and the speeds of saccades, and the brain networks are estimated by the instantaneous phase synchronizations of electroencephalography signals. MAIN RESULTS: Experimental results revealed that the characteristics derived from EOG and brain networks have similar trends which contain two stages. At the beginning, the blink amplitudes and the saccade speeds start to reduce. Meanwhile, the global synchronizations of the brain networks are formed quickly. These observations implies that the cognitive decline along the use of the SSVEP-based BCI. Then, the EOG and the brain networks related characteristics demonstrate a slow recovery or relatively stable trend. SIGNIFICANCE: This study could be helpful for a better understanding about the depreciation of the BCI performance as well as its relationship with the brain networks and the EOG along the use of the SSVEP-based BCI.

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist).

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Spatial Filtering in SSVEP-Based BCIs: Unified Framework and New Improvements.

OBJECTIVE: In the steady-state visual evoked potential (SSVEP)-based brain computer interfaces (BCIs), spatial filtering, which combines the multi-channel electroencephalography (EEG) signals in order to reduce the non-SSVEP-related component and thus enhance the signal-to-noise ratio (SNR), plays an important role in target recognition. Recently, various spatial filtering algorithms have been developed employing different prior knowledge and characteristics of SSVEPs, however how these algorithms interconnect and differ is not yet fully explored, leading to difficulties in further understanding, utilizing and improving them. METHODS: We propose a unified framework under which the spatial filtering algorithms can be formulated as generalized eigenvalue problems (GEPs) with four different elements: data, temporal filter, orthogonal projection and spatial filter. Based on the framework, we design new spatial filtering algorithms for improvements through the choice of different elements. RESULTS: The similarities, differences and relationships among nineteen mainstream spatial filtering algorithms are revealed under the proposed framework. Particularly, it is found that they originate from the canonical correlation analysis (CCA), principal component analysis (PCA), and multi-set CCA, respectively. Furthermore, three new spatial filtering algorithms are developed with enhanced performance validated on two public SSVEP datasets with 45 subjects. CONCLUSION: The proposed framework provides insights into the underlying relationships among different spatial filtering algorithms and helps the design of new spatial filtering algorithms. SIGNIFICANCE: This is a systematic study to explore, compare and improve the existing spatial filtering algorithms, which would be significant for further understanding and future development of high performance SSVEP-based BCIs.

Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs.

OBJECTIVE: Latest target recognition methods that are equipped with learning from the subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. APPROACH: A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. MAIN RESULTS: Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. SIGNIFICANCE: A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs.

Neurofeedback Training for Cognitive and Motor Function Rehabilitation in Chronic Stroke: Two Case Reports.

Stroke is a debilitating neurological condition which usually results in the abnormal electrical brain activity and the impairment of sensation, motor, or cognition functions. In this context, neurofeedback training, i.e., a non-invasive and relatively low cost technique that contributes to neuroplasticity and behavioral performance, might be promising for stroke rehabilitation. We intended to explore neurofeedback training on a 63-year-old male patient and a 77-year-old female patient with chronic stroke. Both of them had suffered from an ischemic stroke for rather long period (more than 3 years) and could not gain further improvement by traditional therapy. The neurofeedback training was designed to enhance alpha activity by 15 sessions distributed over 2 months, for the purpose of overall cognitive improvement and hopefully also motor function improvement for the female patient. We found that the two patients showed alpha enhancement during NFT compared to eyes open baseline within most sessions. Furthermore, both patients reduced their anxiety and depression level. The male patient showed an evolution in speech pattern in terms of naming, sentences completion and verbal fluency, while the female patient improved functionality of the march. These results suggested that alpha neurofeedback training could provide a spectrum of improvements, providing new hope for chronic stroke patients who could not gain further improvements through traditional therapies.

Dark Proteome Database: Studies on Dark Proteins.

The dark proteome, as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016), 43.2% of the eukarya universe and 49.2% of the virus universe are part of the dark proteome. In bacteria and archaea, the percentage of the dark proteome presence is significantly less, at 12.6% and 13.3% respectively. In this work, we present a necessary step to complete the dark proteome picture by introducing the map of the dark proteome in the human and in other model organisms of special importance to mankind. The most significant result is that around 40% to 50% of the proteome of these organisms are still in the dark, where the higher percentages belong to higher eukaryotes (mouse and human organisms). Due to the amount of darkness present in the human organism being more than 50%, deeper studies were made, including the identification of 'dark' genes that are responsible for the production of so-called dark proteins, as well as the identification of the 'dark' tissues where dark proteins are over represented, namely, the heart, cervical mucosa, and natural killer cells. This is a step forward in the direction of gaining a deeper knowledge of the human dark proteome.

Comparison of Visual and Auditory Modalities for Upper-Alpha EEG-Neurofeedback.

Electroencephalography (EEG) neurofeedback (NF) training has been shown to produce long-lasting effects on the improvement of cognitive function as well as the normalization of aberrant brain activity in disease. However, the impact of the sensory modality used as the NF reinforcement signal on training effectiveness has not been systematically investigated. In this work, an EEG-based NF-training system was developed targeting the individual upper-alpha (UA) band and using either a visual or an auditory reinforcement signal, so as to compare the effects of the two sensory modalities. Sixteen healthy volunteers were randomly assigned to the Visual or Auditory group, where a radius-varying sphere or a volume-varying sound, respectively, reflected the relative amplitude of UA measured at EEG electrode Cz. Each participant underwent a total of four NF sessions, of approximately 40 min each, on consecutive days. Both groups showed significant increases in UA at Cz within sessions, and also across sessions. Effects subsequent to NF training were also found beyond the target frequency UA and scalp location Cz, namely in the lower-alpha and theta bands and in posterior brain regions, respectively. Only small differences were found on the EEG between the Visual and Auditory groups, suggesting that auditory reinforcement signals may be as effective as the more commonly used visual signals. The use of auditory NF may potentiate training protocols conducted under mobile conditions, which are now possible due to the increasing availability of wireless EEG systems.

Steady state particle swarm.

This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak-Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.