Motor imagery ability scores are related to cortical activation during gait imagery.

Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the θ, α, and ß band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). Positive correlations were identified between VMIQ and avgERD of the middle cingulum in the ß band and with avgERD of the left insula, right precentral area, and right middle occipital region in the θ band. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.

Beta bursts question the ruling power for brain-computer interfaces.

Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

The neurophysiology of closed-loop auditory stimulation in sleep: A magnetoencephalography study.

Closed-loop auditory stimulation (CLAS) is a brain modulation technique in which sounds are timed to enhance or disrupt endogenous neurophysiological events. CLAS of slow oscillation up-states in sleep is becoming a popular tool to study and enhance sleep's functions, as it increases slow oscillations, evokes sleep spindles and enhances memory consolidation of certain tasks. However, few studies have examined the specific neurophysiological mechanisms involved in CLAS, in part because of practical limitations to available tools. To evaluate evidence for possible models of how sound stimulation during brain up-states alters brain activity, we simultaneously recorded electro- and magnetoencephalography in human participants who received auditory stimulation across sleep stages. We conducted a series of analyses that test different models of pathways through which CLAS of slow oscillations may affect widespread neural activity that have been suggested in literature, using spatial information, timing and phase relationships in the source-localized magnetoencephalography data. The results suggest that auditory information reaches ventral frontal lobe areas via non-lemniscal pathways. From there, a slow oscillation is created and propagated. We demonstrate that while the state of excitability of tissue in auditory cortex and frontal ventral regions shows some synchrony with the electroencephalography (EEG)-recorded up-states that are commonly used for CLAS, it is the state of ventral frontal regions that is most critical for slow oscillation generation. Our findings advance models of how CLAS leads to enhancement of slow oscillations, sleep spindles and associated cognitive benefits and offer insight into how the effectiveness of brain stimulation techniques can be improved.

Multi-channel and multi-harmonic analysis of Auditory Steady-State Response detection.

The Auditory Steady-State Response (ASSR) is a type of auditory evoked potential (AEP) generated in the auditory system that can be automatically detected by means of objective response detectors (ORDs). ASSRs are usually registered on the scalp using electroencephalography (EEG). ORD are univariate techniques, i.e. only uses one data channel. However, techniques involving more than one channel - multi-channel objective response detectors (MORDs) - have been showing higher detection rate (DR) when compared to ORD techniques. When ASSR is evoked by amplitude stimuli, the responses could be detected by analyzing the modulation frequencies and their harmonics. Despite this, ORD techniques are traditionally applied only in its first harmonic. This approach is known as one-sample test. The q-sample tests, however, considers harmonics beyond the first. Thus, this work proposes and evaluates the use of q-sample tests using a combination of multiple EEG channels and multiple harmonics of the stimulation frequencies and compare them with traditional one-sample tests. The database used consists of EEG channels from 24 volunteers with normal auditory threshold collected following a binaural stimulation protocol by amplitude modulated (AM) tone with modulating frequencies near 80 Hz. The best q-sample MORD result showed an increase in DR of 45.25% when compared with the best one-sample ORD test. Thus, it is recommended to use multiple channels and multiple harmonics, whenever available.

Impact of Listening to Indian Classical Music, or Ragas, on the Electroencephalogram: A Meta-Analysis.

Ancient Indian classical music (ICM) has long been lauded and recognized for influencing emotional responses by influencing the human body's resonance. A meta-analysis of prospective case studies published in the last ten years on the effect of ancient Indian music ragas on brain waves is investigated. This meta-analysis aimed to analyze published prospective studies investigating the effect of ancient Indian ragas on EEG in healthy subjects. The present study included prospective studies published since 2012. Studies were obtained by searching four databases, such as PsychINFO, PubMed, Google Scholar, and JSTOR, and searching related journals. Eligibility criteria included studies assessing the impact of listening to Indian classical music on the EEG. Primary outcomes were changes in the brain waves, frequency, and power and their relationship to activity-related arousal, attention, and mental tasks. The studies were analyzed according to the PRISMA guidelines. There were a total of five included studies with 71 participants in the age range of 19-30, and the conditions for the test groups were generally similar except for varying types of ragas used and time of day. Analysis of the data collected from 71 participants revealed that music interventions had statistically significant effects on increasing alpha activity and attention scores. Fractal analysis was sensitive enough to detect EEG brainwave changes while and after listening to the raga musical intervention. Ragas stimulate arousal in different areas of the brain, depending on the emotions they are designed to evoke. However, the synchronized studies together could not highlight a significant relationship between ragas and EEG fractal dimension values. Although the meta-analysis failed to reproduce the same results from the individual studies, potentially due to the small sample size and study variation, the meta-analysis opens doors to the potential of ragas to elicit distinct emotions and serve as robust predictors of emotional response. Future studies can explore the therapeutic potential of various ragas in the clinical setting, such as in the management of cognitive disorders and stress or in modulating heart rate variability and cognitive performance.

The efficacy of electroencephalography neurofeedback for enhancing episodic memory in healthy and clinical participants: A systematic qualitative review and meta-analysis.

Several studies have examined whether electroencephalography neurofeedback (EEG-NF), a self-regulatory technique where an individual receives real-time feedback on a pattern of brain activity that is theoretically linked to a target behaviour, can enhance episodic memory. The aim of this research was to i) provide a qualitative overview of the literature, and ii) conduct a meta-analysis of appropriately controlled studies to determine whether EEG-NF can enhance episodic memory. The literature search returned 46 studies, with 21 studies (44 effect sizes) meeting the inclusion criteria for the meta-analysis. The qualitative overview revealed that, across EEG-NF studies on both healthy and clinical populations, procedures and protocols vary considerably and many studies were insufficiently powered with inadequate design features. The meta-analysis, conducted on studies with an active control, revealed a small-size, significant positive effect of EEG-NF on episodic memory performance (g = 0.31, p = 0.003), moderated by memory modality and EEG-NF self-regulation success. These results are discussed with a view towards optimising EEG-NF training and subsequent benefits to episodic memory.

A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.

A Brain-Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain-computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.

SincMSNet: a Sinc filter convolutional neural network for EEG motor imagery classification.

Objective.Motor imagery (MI) is widely used in brain-computer interfaces (BCIs). However, the decode of MI-EEG using convolutional neural networks (CNNs) remains a challenge due to individual variability.Approach.We propose a fully end-to-end CNN called SincMSNet to address this issue. SincMSNet employs the Sinc filter to extract subject-specific frequency band information and utilizes mixed-depth convolution to extract multi-scale temporal information for each band. It then applies a spatial convolutional block to extract spatial features and uses a temporal log-variance block to obtain classification features. The model of SincMSNet is trained under the joint supervision of cross-entropy and center loss to achieve inter-class separable and intra-class compact representations of EEG signals.Main results.We evaluated the performance of SincMSNet on the BCIC-IV-2a (four-class) and OpenBMI (two-class) datasets. SincMSNet achieves impressive results, surpassing benchmark methods. In four-class and two-class inter-session analysis, it achieves average accuracies of 80.70% and 71.50% respectively. In four-class and two-class single-session analysis, it achieves average accuracies of 84.69% and 76.99% respectively. Additionally, visualizations of the learned band-pass filter bands by Sinc filters demonstrate the network's ability to extract subject-specific frequency band information from EEG.Significance.This study highlights the potential of SincMSNet in improving the performance of MI-EEG decoding and designing more robust MI-BCIs. The source code for SincMSNet can be found at:https://github.com/Want2Vanish/SincMSNet.

Longitudinal Evidence for Attenuated Local-Global Deviance Detection as a Precursor of Working Memory Decline.

From the perspective of predictive coding, normal aging is accompanied by decreased weighting of sensory inputs and increased reliance on predictions, resulting in the attenuation of prediction errors in older age. Recent electroencephalography (EEG) research further revealed that the age-related shift from sensorium to predictions is hierarchy-selective, as older brains show little reduction in lower-level but significant suppression in higher-level prediction errors. Moreover, the disrupted propagation of prediction errors from the lower-level to the higher-level seems to be linked to deficient maintenance of information in working memory. However, it is unclear whether the hierarchical predictive processing continues to decline with advancing age as working memory. Here, we longitudinally followed a sample of 78 participants from three age groups (including seniors, adults, and adolescents) over three years' time. Seniors exhibited largely preserved local processing [consisting of comparable mismatch negativity (MMN), delayed P3a, and comparable reorienting negativity (RON)] but significantly compromised global processing (consisting of suppressed frontocentral negativity and suppressed P3b) in the auditory local-global paradigm. These electrophysiological responses did not change with the passing of time, unlike working memory which deteriorated with advancing age. Correlation analysis further showed that these electrophysiological responses signaling prediction errors are indicative of concurrent working memory. Moreover, there was a correlation between earlier predictive processing and later working memory but not between earlier working memory and later predictive processing. The temporal asymmetry suggested that the hierarchy-selective attenuation of prediction errors is likely a precursor of working memory decline.

Mismatch negativity as a marker of auditory pattern separation.

To what extent does incidental encoding of auditory stimuli influence subsequent episodic memory for the same stimuli? We examined whether the mismatch negativity (MMN), an event-related potential generated by auditory change detection, is correlated with participants' ability to discriminate those stimuli (i.e. targets) from highly similar lures and from dissimilar foils. We measured the MMN in 30 young adults (18-32 years, 18 females) using a passive auditory oddball task with standard and deviant 5-tone sequences differing in pitch contour. After exposure, all participants completed an incidental memory test for old targets, lures, and foils. As expected, participants at test exhibited high sensitivity in recognizing target items relative to foils and lower sensitivity in recognizing target items relative to lures. Notably, we found a significant correlation between MMN amplitude and lure discrimination, but not foil discrimination. Our investigation shows that our capacity to discriminate sensory inputs at encoding, as measured by the MMN, translates into precision in memory for those inputs.

The effect of TENS on sleep: A pilot study.

BACKGROUND: Insomnia is the second most common neuropsychiatric disorder, but the current treatments are not very effective. There is therefore an urgent need to develop better treatments. Transcutaneous electrical nerve stimulation (TENS) may be a promising means of treating insomnia. OBJECTIVE: This work aims to explore whether and how TENS modulate sleep and the effect of stimulation waveforms on sleep. METHODS: Forty-five healthy subjects participated in this study. Electroencephalography (EEG) data were recorded before and after four mode low-frequency (1 Hz) TENS with different waveforms, which were formed by superimposing sine waves of different high frequencies (60-210 Hz) and low frequencies (1-6 Hz). The four waveform modes are formed by combining sine waves of varying frequencies. Mode 1 (M1) consists of a combination of high frequencies (60-110 Hz) and low frequencies (1-6 Hz). Mode 2 (M2) is made up of high frequencies (60-210 Hz) and low frequencies (1-6 Hz). Mode 3 (M3) consists of high frequencies (110-160 Hz) and low frequencies (1-6 Hz), while mode 4 (M4) is composed of high frequencies (160-210 Hz) and low frequencies (1-6 Hz). For M1, M3 and M4, the high frequency portions of the stimulus waveforms account for 50%, while for M2, the high frequency portion of the waveform accounts for 65%. For each mode, the current intensities ranged from 4 mA to 7 mA, with values for each participant adjusted according to individual tolerance. During stimulation, the subjects were stimulated at the greater occipital nerve by the four mode TENS. RESULTS: M1, M3, and M4 slowed down the frequency of neural activity, broadened the distribution of theta waves, and caused a decrease in activity in wakefulness-related regions and an increase in activity in sleep-related regions. However, M2 has the opposite modulation effect. CONCLUSION: These results indicated that low-frequency TENS (1 Hz) may facilitate sleep in a waveform-specific manner. Our findings provide new insights into the mechanisms of sleep modulation by TENS and the design of effective insomnia treatments.

Are Motor Imagery Ability scores related to cortical activity during gait imagery?

Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography (hdEEG) in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the ß band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). VMIQ was positively correlated with avgERD of frontal and cingulate areas, whereas IA SCORE was positively correlated with avgERD of left inferior frontal and superior temporal regions. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.

An investigation of auditory processing in relapsing-remitting multiple sclerosis.

Multiple sclerosis (MS) is one of the most common neurological diseases in North America and it is frequently associated with sensory processing difficulties, cognitive deficits, and psychiatric illness. While many studies have examined cognitive deficits in MS measured by behavioural responses and neuroimaging techniques, only a few studies have examined neurophysiological measures of auditory functioning in MS, such as the mismatch negativity (MMN). The MMN is an event-related potential that indicates automatic auditory change detection. This study examined whether MMN endpoints measured by electroencephalography (EEG) differ in individuals with relapsing-remitting MS compared to healthy controls and whether the symptomatology of MS, including symptoms of depression and fatigue, are related to MMN measures. A multi-feature MMN paradigm, which includes five distinct deviant tones, was used to assess auditory cortex function in MS. There were no significant differences in MMN amplitudes or latencies between the MS and control group (p < 0.05) and corresponding effect sizes were small. However, there was a correlation between reduced MMN amplitudes in response to an intensity deviant and physician-reported disability. The intensity MMN may be more sensitive to deterioration in this population. Ultimately, this study provides a comprehensive profile of early auditory processing abilities in MS and suggests that a reduction in the MMN response may be representative of disease severity in MS.

Dealing with correlations in the multichannel EEG using bipolar derivations and Monte Carlo simulations: application to the detection of auditory steady-state responses.

The multichannel objective response detection (MORD) techniques are statistical methods, which use information from more than one electroencephalography (EEG) channel, to infer the presence of evoked potential. However, the correlation level between the channels can lead to a decrease in MORD performance, such as an increase in the false positive (FP) rate and/or a decrease in the detection rate (DR). The present study aims to propose a method to deal with the correlations in the multichannel EEG. The method consists of making an adjustment in the Monte Carlo simulation, considering the information between channels. The MORD techniques with and without the new method were applied to an auditory steady-state response (ASSR) database, composed of the EEG multichannel of eleven volunteers during multifrequency stimulation. The proposed method kept the FP rate at values equal to or less than the significance level of the test and led to an increase of 8.51% in the DR in relation to non-application of the method. Results of this study indicate that the proposed method is an alternative to deal with the effect of the correlation between channels in situations where MORD techniques are applied.

Context independent reductions in external processing during self-generated episodic social cognition.

Ongoing cognition supports behavioral flexibility by facilitating behavior in the moment, and through the consideration of future actions. These different modes of cognition are hypothesized to vary with the correlation between brain activity and external input, since evoked responses are reduced when cognition switches to topics unrelated to the current task. This study examined whether these reduced evoked responses change as a consequence of the task environment in which the experience emerges. We combined electroencephalography (EEG) recording with multidimensional experience sampling (MDES) to assess the electrophysiological correlates of ongoing thought in task contexts which vary on their need to maintain continuous representations of task information for satisfactory performance. We focused on an event-related potential (ERP) known as the parietal P3 that had a greater amplitude in our tasks relying on greater external attention. A principal component analysis (PCA) of the MDES data revealed four patterns of ongoing thought: off-task episodic social cognition, deliberate on-task thought, imagery, and emotion. Participants reported more off-task episodic social cognition and mental imagery under low external demands and more deliberate on-task thought under high external task demands. Importantly, the occurrence of off-task episodic social cognition was linked to similar reductions in the amplitude of the P3 regardless of external task. These data suggest the amplitude of the P3 may often be a general feature of external task-related content and suggest attentional decoupling from sensory inputs are necessary for certain types of perceptually-decoupled, self-generated thoughts.

Neurologic music therapy combined with EEG-tDCS for upper motor extremity performance in patients with corticobasal syndrome: Study protocol for a novel approach.

BACKGROUND: Corticobasal syndrome (CBS) is an atypical parkinsonian disorder that involves degeneration of brain regions associated with motor coordination and sensory processing. Combining transcranial direct current stimulation (tDCS) with rehabilitation training has been shown to improve upper-limb performance in other disease models. Here, we describe the protocol investigating whether tDCS with neurologic music therapy (NMT) (patterned sensory enhancement and therapeutic instrumental music performance) enhances functional arm/hand performance in individuals with CBS. METHODS: Study participants are randomly assigned to six 30-min sessions (twice per week for 3 weeks) of NMT + either sham tDCS or active tDCS. We aim to stimulate the frontoparietal cortex, which is associated with movement execution/coordination and sensory processing. The hemisphere contralateral to the more affected arm is stimulated (total stimulation current of 2 mA from 5 dime-sized electrodes). Individualized NMT sessions designed to exercise the upper limb are provided. Participants undergo gross/fine motor, cognitive and emotional assessments at baseline and follow-up (one month after the final session). To investigate the immediate effects of tDCS and NMT training, gross /fine motor, affective level, and kinematic parameter measurements using motion sensors are collected before and after each session. Electroencephalography is used to collect electrical neurophysiological responses before, during, and after tDCS+NMT sessions. The study participants, neurologic music therapist and outcome assessor are blinded to whether participants are in the sham or active tDCS group. CONCLUSION: This noninvasive and patient-centered clinical trial for CBS may provide insight into rehabilitation options that are sorely lacking in this population.

For Which Children with ADHD is TBR Neurofeedback Effective? Comorbidity as a Moderator.

We examined psychiatric comorbidities moderation of a 2-site double-blind randomized clinical trial of theta/beta-ratio (TBR) neurofeedback (NF) for attention deficit hyperactivity disorder (ADHD). Seven-to-ten-year-olds with ADHD received either NF (n = 84) or Control (n = 58) for 38 treatments. Outcome was change in parent-/teacher-rated inattention from baseline to end-of-treatment (acute effect), and 13-month-follow-up. Seventy percent had at least one comorbidity: oppositional defiant disorder (ODD) (50%), specific phobias (27%), generalized anxiety (23%), separation anxiety (16%). Comorbidities were grouped into anxiety alone (20%), ODD alone (23%), neither (30%), or both (27%). Comorbidity (p = 0.043) moderated acute effect; those with anxiety-alone responded better to Control than to TBR NF (d = - 0.79, CI - 1.55- - 0.04), and the other groups showed a slightly better response to TBR NF than to Control (d = 0.22 ~ 0.31, CI - 0.3-0.98). At 13-months, ODD-alone group responded better to NF than Control (d = 0.74, CI 0.05-1.43). TBR NF is not indicated for ADHD with comorbid anxiety but may benefit ADHD with ODD.Clinical Trials Identifier: NCT02251743, date of registration: 09/17/2014.

Deep Convolutional Neural Network for EEG-Based Motor Decoding.

Brain-machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI. This study presents a deep convolutional neural network (CNN) for EEG-based motor decoding. Both upper-limb and lower-limb motor imagery were detected from this end-to-end learning with four datasets. An average classification accuracy of 93.36 ± 1.68% was yielded on the four datasets. We compared the proposed approach with two other models, i.e., multilayer perceptron and the state-of-the-art framework with common spatial patterns and support vector machine. We observed that the performance of the CNN-based framework was significantly better than the other two models. Feature visualization was further conducted to evaluate the discriminative channels employed for the decoding. We showed the feasibility of the proposed architecture to decode motor imagery from raw EEG data without manually designed features. With the advances in the fields of computer vision and speech recognition, deep learning can not only boost the EEG decoding performance but also help us gain more insight from the data, which may further broaden the knowledge of neuroscience for brain mapping.

Competing at the Cybathlon championship for people with disabilities: long-term motor imagery brain-computer interface training of a cybathlete who has tetraplegia.

BACKGROUND: The brain-computer interface (BCI) race at the Cybathlon championship, for people with disabilities, challenges teams (BCI researchers, developers and pilots with spinal cord injury) to control an avatar on a virtual racetrack without movement. Here we describe the training regime and results of the Ulster University BCI Team pilot who has tetraplegia and was trained to use an electroencephalography (EEG)-based BCI intermittently over 10 years, to compete in three Cybathlon events. METHODS: A multi-class, multiple binary classifier framework was used to decode three kinesthetically imagined movements (motor imagery of left arm, right arm, and feet), and relaxed state. Three game paradigms were used for training i.e., NeuroSensi, Triad, and Cybathlon Race: BrainDriver. An evaluation of the pilot's performance is presented for two Cybathlon competition training periods-spanning 20 sessions over 5 weeks prior to the 2019 competition, and 25 sessions over 5 weeks in the run up to the 2020 competition. RESULTS: Having participated in BCI training in 2009 and competed in Cybathlon 2016, the experienced pilot achieved high two-class accuracy on all class pairs when training began in 2019 (decoding accuracy > 90%, resulting in efficient NeuroSensi and Triad game control). The BrainDriver performance (i.e., Cybathlon race completion time) improved significantly during the training period, leading up to the competition day, ranging from 274-156 s (255 ± 24 s to 191 ± 14 s mean ± std), over 17 days (10 sessions) in 2019, and from 230-168 s (214 ± 14 s to 181 ± 4 s), over 18 days (13 sessions) in 2020. However, on both competition occasions, towards the race date, the performance deteriorated significantly. CONCLUSIONS: The training regime and framework applied were highly effective in achieving competitive race completion times. The BCI framework did not cope with significant deviation in electroencephalography (EEG) observed in the sessions occurring shortly before and during the race day. Changes in cognitive state as a result of stress, arousal level, and fatigue, associated with the competition challenge and performance pressure, were likely contributing factors to the non-stationary effects that resulted in the BCI and pilot achieving suboptimal performance on race day. Trial registration not registered.

Effect of functional and effective brain connectivity in identifying vowels from articulation imagery procedures.

Articulation imagery, a form of mental imagery, refers to the activity of imagining or speaking to oneself mentally without an articulation movement. It is an effective domain of research in speech impaired neural disorders, as speech imagination has high similarity to real voice communication. This work employs electroencephalography (EEG) signals acquired from articulation and articulation imagery in identifying the vowel being imagined during different tasks. EEG signals from chosen electrodes are decomposed using the empirical mode decomposition (EMD) method into a series of intrinsic mode functions. Brain connectivity estimators and entropy measures have been computed to analyze the functional cooperation and causal dependence between different cortical regions as well as the regularity in the signals. Using machine learning techniques such as multiclass support vector machine (MSVM) and random forest (RF), the vowels have been classified. Three different training and testing protocols (Articulation-AR, Articulation imagery-AI and Articulation vs Articulation imagery-AR vs AI) were employed for identifying the vowel being imagined of articulating. An overall classification accuracy of 80% was obtained for articulation imagery protocol which was found to be higher than the other two protocols. Also, MSVM techniques outperformed the RF technique in terms of the classification accuracy. The effect of brain connectivity estimators and machine learning techniques seems to be reliable in identifying the vowel from the subjects' thought and thereby assisting the people with speech impairment.