Prefrontal Modulation of Visual Processing and Sustained Attention in Aging, a tDCS-EEG Coregistration Approach.

The ability to sustain attention is integral to healthy cognition in aging. The right PFC (rPFC) is critical for maintaining high levels of attentional focus. Whether plasticity of this region can be harnessed to support sustained attention in older adults is unknown. We used transcranial direct current stimulation to increase cortical excitability of the rPFC, while monitoring behavioral and electrophysiological markers of sustained attention in older adults with suboptimal sustained attention capacity. During rPFC transcranial direct current stimulation, fewer lapses of attention occurred and electroencephalography signals of frontal engagement and early visual attention were enhanced. To further verify these results, we repeated the experiment in an independent cohort of cognitively typical older adults using a different sustained attention paradigm. Again, prefrontal stimulation was associated with fewer attentional lapses. These experiments suggest the rPFC can be manipulated in later years to increase top-down modulation over early sensory processing and improve sustained attention performance. This holds valuable information for the development of neurorehabilitation protocols to ameliorate age-related deficits in this capacity.

Domain-specific and domain-general processes underlying metacognitive judgments.

Metacognition and self-awareness are commonly assumed to operate as global capacities. However, there have been few attempts to test this assumption across multiple cognitive domains and metacognitive evaluations. Here, we assessed the covariance between "online" metacognitive processes, as measured by decision confidence judgments in the domains of perception and memory, and error awareness in the domain of attention to action. Previous research investigating metacognition across task domains have not matched stimulus characteristics across tasks raising the possibility that any differences in metacognitive accuracy may be influenced by local task properties. The current experiment measured metacognition in perceptual, memorial and attention tasks that were closely matched for stimulus characteristics. We found that metacognitive accuracy across the three tasks was dissociated suggesting that domain specific networks support an individual's capacity for accurate metacognition. This finding was independent of objective performance, which was controlled using a staircase procedure. However, response times for metacognitive judgments and error awareness were associated suggesting that shared mechanisms determining how these meta-level evaluations unfold in time may underlie these different types of decision. In addition, the relationship between these laboratory measures of metacognition and reports of everyday functioning from participants and their significant others (informants) was investigated. We found that informant reports, but not self reports, predicted metacognitive accuracy on the perceptual task and participants who underreported cognitive difficulties relative to their informants also showed poorer metacognitive accuracy on the perceptual task. These results are discussed in the context of models of metacognitive regulation and neuropsychological evidence for dissociable metacognitive systems. The potential for the refinement of metacognitive assessment in clinical populations is also discussed.

Opportunities and obstacles in non-invasive brain stimulation.

Non-invasive brain stimulation (NIBS) is a complex and multifaceted approach to modulating brain activity and holds the potential for broad accessibility. This work discusses the mechanisms of the four distinct approaches to modulating brain activity non-invasively: electrical currents, magnetic fields, light, and ultrasound. We examine the dual stochastic and deterministic nature of brain activity and its implications for NIBS, highlighting the challenges posed by inter-individual variability, nebulous dose-response relationships, potential biases and neuroanatomical heterogeneity. Looking forward, we propose five areas of opportunity for future research: closed-loop stimulation, consistent stimulation of the intended target region, reducing bias, multimodal approaches, and strategies to address low sample sizes.

EEG data space adaptation to reduce intersession nonstationarity in brain-computer interface.

A major challenge in EEG-based brain-computer interfaces (BCIs) is the intersession nonstationarity in the EEG data that often leads to deteriorated BCI performances. To address this issue, this letter proposes a novel data space adaptation technique, EEG data space adaptation (EEG-DSA), to linearly transform the EEG data from the target space (evaluation session), such that the distribution difference to the source space (training session) is minimized. Using the Kullback-Leibler (KL) divergence criterion, we propose two versions of the EEG-DSA algorithm: the supervised version, when labeled data are available in the evaluation session, and the unsupervised version, when labeled data are not available. The performance of the proposed EEG-DSA algorithm is evaluated on the publicly available BCI Competition IV data set IIa and a data set recorded from 16 subjects performing motor imagery tasks on different days. The results show that the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the results without adaptation in terms of classification accuracy. The results also show that for subjects with poor BCI performances when no adaptation is applied, the proposed EEG-DSA algorithm in both the supervised and unsupervised versions significantly outperforms the unsupervised bias adaptation algorithm (PMean).

Bayesian learning from multi-way EEG feedback for robot navigation and target identification.

Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously while users merely observe assistive robots performing tasks. Using reactive brain signals, existing studies have addressed robot navigation tasks with a very limited number of potential target locations. Moreover, they use only binary, error-vs-correct classification of robot actions, leaving more detailed information unutilised. In this study a virtual robot had to navigate towards, and identify, target locations in both small and large grids, wherein any location could be the target. For the first time, we apply a system utilising detailed EEG information: 4-way classification of movements is performed, including specific information regarding when the target is reached. Additionally, we classify whether targets are correctly identified. Our proposed Bayesian strategy infers the most likely target location from the brain's responses. The experimental results show that our novel use of detailed information facilitates a more efficient and robust system than the state-of-the-art. Furthermore, unlike state-of-the-art approaches, we show scalability of our proposed approach: By tuning parameters appropriately, our strategy correctly identifies 98% of targets, even in large search spaces.

Electrophysiological Correlates of Response Time in a Vigilant Attention Task.

Early detection of a deficit in vigilant attention can allow for user notification or intervention. In this paper, Electrophysiological correlates of vigilant attention from a random-dot motion task were explored. Using only frontal (Fz) and parietal (Pz) EEG channels, spectral features of response time were determined. Notably, significant differ-ences in high-beta, gamma and alpha frequency bands were found between fast and slow reaction times. These results are interpreted in line with the relevant literature on arousal, off-task thought and active visuospatial attentional suppression. The presence of response-locked time-domain features was analysed. However, motor-related features obfuscated these features.

A Transfer Learning Algorithm to Reduce Brain-Computer Interface Calibration Time for Long-Term Users.

Current motor imagery-based brain-computer interface (BCI) systems require a long calibration time at the beginning of each session before they can be used with adequate levels of classification accuracy. In particular, this issue can be a significant burden for long term BCI users. This article proposes a novel transfer learning algorithm, called r-KLwDSA, to reduce the BCI calibration time for long-term users. The proposed r-KLwDSA algorithm aligns the user's EEG data collected in previous sessions to the few EEG trials collected in the current session, using a novel linear alignment method. Thereafter, the aligned EEG trials from the previous sessions and the few EEG trials from the current sessions are fused through a weighting mechanism before they are used for calibrating the BCI model. To validate the proposed algorithm, a large dataset containing the EEG data from 11 stroke patients, each performing 18 BCI sessions, was used. The proposed framework demonstrated a significant improvement in the classification accuracy, of over 4% compared to the session-specific algorithm, when there were as few as two trials per class available from the current session. The proposed algorithm was particularly successful in improving the BCI accuracy of the sessions that had initial session-specific accuracy below 60%, with an average improvement of around 10% in the accuracy, leading to more stroke patients having meaningful BCI rehabilitation.

Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

Background. A number of recent randomized controlled trials reported the efficacy of brain-computer interface (BCI) for upper-limb stroke rehabilitation compared with other therapies. Despite the encouraging results reported, there is a significant variance in the reported outcomes. This paper aims to investigate the effectiveness of different BCI designs on poststroke upper-limb rehabilitation. Methods. The effect sizes of pooled and individual studies were assessed by computing Hedge's g values with a 95% confidence interval. Subgroup analyses were also performed to examine the impact of different BCI designs on the treatment effect. Results. The study included 12 clinical trials involving 298 patients. The analysis showed that the BCI yielded significant superior short-term and long-term efficacy in improving the upper-limb motor function compared to the control therapies (Hedge's g = 0.73 and 0.33, respectively). Based on our subgroup analyses, the BCI studies that used the intention of movement had a higher effect size compared to those used motor imagery (Hedge's g = 1.21 and 0.55, respectively). The BCI studies using band power features had a significantly higher effect size than those using filter bank common spatial patterns features (Hedge's g = 1.25 and - 0.23, respectively). Finally, the studies that used functional electrical stimulation as the BCI feedback had the highest effect size compared to other devices (Hedge's g = 1.2). Conclusion. This meta-analysis confirmed the effectiveness of BCI for upper-limb rehabilitation. Our findings support the use of band power features, the intention of movement, and the functional electrical stimulation in future BCI designs for poststroke upper-limb rehabilitation.

Exploring the ability of stroke survivors in using the contralesional hemisphere to control a brain-computer interface.

Brain-computer interfaces (BCIs) have recently been shown to be clinically effective as a novel method of stroke rehabilitation. In many BCI-based studies, the activation of the ipsilesional hemisphere was considered a key factor required for motor recovery after stroke. However, emerging evidence suggests that the contralesional hemisphere also plays a role in motor function rehabilitation. The objective of this study is to investigate the effectiveness of the BCI in detecting motor imagery of the affected hand from contralesional hemisphere. We analyzed a large EEG dataset from 136 stroke patients who performed motor imagery of their stroke-impaired hand. BCI features were extracted from channels covering either the ipsilesional, contralesional or bilateral hemisphere, and the offline BCI accuracy was computed using 10 [Formula: see text] 10-fold cross-validations. Our results showed that most stroke patients can operate the BCI using either their contralesional or ipsilesional hemisphere. Those with the ipsilesional BCI accuracy of less than 60% had significantly higher motor impairments than those with the ipsilesional BCI accuracy above 80%. Interestingly, those with the ipsilesional BCI accuracy of less than 60% achieved a significantly higher contralesional BCI accuracy, whereas those with the ipsilesional BCI accuracy more than 80% had significantly poorer contralesional BCI accuracy. This study suggests that contralesional BCI may be a useful approach for those with a high motor impairment who cannot accurately generate signals from ipsilesional hemisphere to effectively operate BCI.

Impaired metacognition and reduced neural signals of decision confidence in adults with traumatic brain injury.

OBJECTIVE: Metacognition reflects our capacity to monitor or evaluate other cognitive states as they unfold during task performance, for example, our level of confidence in the veracity of a memory. Impaired metacognition is seen in patients with traumatic brain injury (TBI) and substantially impacts their ability to manage functional difficulties during recovery. Recent evidence suggests that metacognitive representations reflect domain-specific processes (e.g., memory vs. perception) acting jointly with generic confidence signals mediated by widespread frontoparietal networks. The impact of neurological insult on metacognitive processes across different cognitive domains following TBI remains unknown. METHOD: To assess metacognitive accuracy, we measured decision confidence across both a perceptual and memory task in patients with TBI (n = 27) and controls (n = 28). During the metacognitive tasks, continuous electroencephalography was recorded, and event-related potentials (ERP) were analyzed. RESULTS: First, we observed a deficit in metacognitive efficiency across both tasks suggesting that patients show a loss of perceptual and memorial evidence available for confidence judgments despite equivalent accuracy levels to controls. Second, a late positive-going ERP waveform (500-700 ms) was greater in amplitude for high versus low-confidence judgements for controls across both task domains. By contrast, in patients with TBI, the same ERP waveform did not vary by confidence level suggesting a deficient or attenuated neural marker of decision confidence postinjury. CONCLUSIONS: These findings suggest that diffuse damage to putative frontoparietal regions in patients disrupts domain-general metacognitive accuracy and electrophysiological signals that accumulate evidence of decision confidence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders.

Most mental disorders, such as addictive diseases or schizophrenia, are characterized by impaired cognitive function and behavior control originating from disturbances within prefrontal neural networks. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox whose suitability for diagnosis and therapy of mental disorders has not yet been explored. This study, therefore, aimed to develop a biocompatible and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We used a 3D-printing technology to rapidly prototype customized bioelectronic implants through robot-controlled deposition of soft silicones and a conductive platinum ink. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials in treatment-naïve animals, after alcohol administration and following neuromodulation through implant-driven electrical brain stimulation and cortical delivery of the anti-relapse medication naltrexone. Towards smart neuroprosthetic interfaces, we furthermore developed machine learning algorithms to autonomously classify treatment effects within the neural recordings. The neuroprosthesis successfully captured neural activity patterns reflecting intact stimulus processing and alcohol-induced neural depression. Moreover, implant-driven electrical and pharmacological stimulation enabled successful enhancement of neural activity. A machine learning approach based on stepwise linear discriminant analysis was able to deal with sparsity in the data and distinguished treatments with high accuracy. Our work demonstrates the feasibility of multimodal bioelectronic systems to monitor, modulate and identify healthy and affected brain states with potential use in a personalized and optimized therapy of neuropsychiatric disorders.

#EEGManyLabs: Investigating the replicability of influential EEG experiments.

There is growing awareness across the neuroscience community that the replicability of findings about the relationship between brain activity and cognitive phenomena can be improved by conducting studies with high statistical power that adhere to well-defined and standardised analysis pipelines. Inspired by recent efforts from the psychological sciences, and with the desire to examine some of the foundational findings using electroencephalography (EEG), we have launched #EEGManyLabs, a large-scale international collaborative replication effort. Since its discovery in the early 20th century, EEG has had a profound influence on our understanding of human cognition, but there is limited evidence on the replicability of some of the most highly cited discoveries. After a systematic search and selection process, we have identified 27 of the most influential and continually cited studies in the field. We plan to directly test the replicability of key findings from 20 of these studies in teams of at least three independent laboratories. The design and protocol of each replication effort will be submitted as a Registered Report and peer-reviewed prior to data collection. Prediction markets, open to all EEG researchers, will be used as a forecasting tool to examine which findings the community expects to replicate. This project will update our confidence in some of the most influential EEG findings and generate a large open access database that can be used to inform future research practices. Finally, through this international effort, we hope to create a cultural shift towards inclusive, high-powered multi-laboratory collaborations.

Improving EEG-based error detection using relative peak features.

A brain-computer interface (BCI) potentially enables a severely disabled person to communicate using brain signals. Automatic detection of error-related potentials (ErrPs) in electroencephalograph (EEG) could improve BCI performance by allowing to correct the erroneous action made by the machine. However, the current low accuracy in detecting ErrPs, particularly in some users, can reduce its potential benefits. The paper addresses this problem by proposing a novel relative peak feature (RPF) selection method to improve performance and accuracy for recognising an ErrP in the EEG. Using data collected from 29 participants with a mean age of 24.14 years the relative peak features yielded an average across all classifiers of 81.63% accuracy in detecting the erroneous events and an average 78.87 % accuracy in detecting the correct events, using KNN, SVM and LDA classifiers. In comparison to the temporal feature selection, there was a gain in performance in all classifiers of 17.85% for error accuracy and a reduction of -6.16% for correct accuracy Specifically; our proposed RPF used significantly reduced the number of features by 91.7% when compared with the state of the art temporal features.In the future, this work will improve the human-robot interaction by improving the accuracy of detecting errors that enable the BCI to correct any mistakes.

Four-Way Classification of EEG Responses To Virtual Robot Navigation.

Studies have shown the possibility of using brain signals that are automatically generated while observing a navigation task as feedback for semi-autonomous control of a robot. This allows the robot to learn quasi-optimal routes to intended targets. We have combined the subclassification of two different types of navigational errors, with the subclassification of two different types of correct navigational actions, to create a 4-way classification strategy, providing detailed information about the type of action the robot performed. We used a 2-stage stepwise linear discriminant analysis approach, and tested this using brain signals from 8 and 14 participants observing two robot navigation tasks. Classification results were significantly above the chance level, with mean overall accuracy of 44.3% and 36.0% for the two datasets. As a proof of concept, we have shown that it is possible to perform fine-grained, 4-way classification of robot navigational actions, based on the electroencephalogram responses of participants who only had to observe the task. This study provides the next step towards comprehensive implicit brain-machine communication, and towards an efficient semi-autonomous brain-computer interface.

Biomarkers and neuromodulation techniques in substance use disorders.

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.

Correction to: Biomarkers and neuromodulation techniques in substance use disorders.

[This corrects the article DOI: 10.1186/s42234-020-0040-0.].

"You Have Reached Your Destination": A Single Trial EEG Classification Study.

Studies have established that it is possible to differentiate between the brain's responses to observing correct and incorrect movements in navigation tasks. Furthermore, these classifications can be used as feedback for a learning-based BCI, to allow real or virtual robots to find quasi-optimal routes to a target. However, when navigating it is important not only to know we are moving in the right direction toward a target, but also to know when we have reached it. We asked participants to observe a virtual robot performing a 1-dimensional navigation task. We recorded EEG and then performed neurophysiological analysis on the responses to two classes of correct movements: those that moved closer to the target but did not reach it, and those that did reach the target. Further, we used a stepwise linear classifier on time-domain features to differentiate the classes on a single-trial basis. A second data set was also used to further test this single-trial classification. We found that the amplitude of the P300 was significantly greater in cases where the movement reached the target. Interestingly, we were able to classify the EEG signals evoked when observing the two classes of correct movements against each other with mean overall accuracy of 66.5 and 68.0% for the two data sets, with greater than chance levels of accuracy achieved for all participants. As a proof of concept, we have shown that it is possible to classify the EEG responses in observing these different correct movements against each other using single-trial EEG. This could be used as part of a learning-based BCI and opens a new door toward a more autonomous BCI navigation system.

Weighted Transfer Learning of Dynamic Time Warped Data for Motor Imagery based Brain Computer Interfaces.

A large amount of calibration data is typically needed to train an electroencephalogram (EEG)-based brain-computer interfaces (BCI) due to the non-stationary nature of EEG data. This paper proposes a novel weighted transfer learning algorithm using a dynamic time warping (DTW) based alignment method to alleviate this need by using data from other subjects. DTW-based alignment is first applied to reduce the temporal variations between a specific subject data and the transfer learning data from other subjects. Next, similarity is measured using Kullback Leibler divergence (KL) between the DTW aligned data and the specific subject data. The other subjects' data are then weighted based on their KL similarity to each trials of the specific subject data. This weighted data from other subjects are then used to train the BCI model of the specific subject. An experiment was performed on publicly available BCI Competition IV dataset 2a. The proposed algorithm yielded an average improvement of 9% compared to a subject-specific BCI model trained with 4 trials, and the results yielded an average improvement of 10% compared to naive transfer learning. Overall, the proposed DTW-aligned KL weighted transfer learning algorithm show promise to alleviate the need of large amount of calibration data by using only a short calibration data.

Dynamic time warping-based transfer learning for improving common spatial patterns in brain-computer interface.

OBJECTIVE: Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning. APPROACH: The proposed framework combines the subject-specific covariance matrix ([Formula: see text]) estimated using the available trials from the new subject, with a novel DTW-based transferred covariance matrix ([Formula: see text]) estimated using previous subjects' trials. In the proposed [Formula: see text], the available labelled trials from the previous subjects are temporally aligned to the average of the available trials of the new subject from the same class using DTW. This alignment aims to reduce temporal variations and non-stationarities between previous subjects' trials and the available trials from the new subjects. Moreover, to tackle the problem of regularization parameter selection when only a few trials are available for training, an online method is proposed, where the best regularization parameter is selected based on the confidence scores of the trained classifier on the upcoming first few labelled testing trials. MAIN RESULTS: The proposed framework is evaluated on two datasets against two baseline algorithms. The obtained results reveal that DTW-RCSP significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training. SIGNIFICANCE: Impressively, our results show that successful BCI interactions could be achieved with a calibration session as small as only one trial per class.

Using Resting State Heart Rate Variability and Skin Conductance Response to Detect Depression in Adults.

Depression is the leading cause of disability worldwide, yet rates of missed- and mis-diagnoses are alarmingly high. The introduction of objective biomarkers, to aid diagnosis, informed by depression's physiological pathology may alleviate some of the burden on strained mental health services. Three minutes of eyes-closed resting state heart rate and skin conductance response (SCR) data were acquired from 27 participants (16 healthy controls, 11 with major depressive disorder (MDD)). Various classifiers were trained on state-of-the-art and novel features. We are aware of no previous studies analysing the utility of multimodal vs. individual modalities for classification. We found no improvement using multimodal classifiers over using heart rate variability (HRV) alone, which achieved 81% test accuracy. The best multimodal and SCR only classifiers were only slightly less accurate at 78%. Despite not improving depression detection, SCR features did show stronger correlation with suicidal ideation than HRV. SD1/SD22 is a novel HRV feature proposed in this paper, similar to the commonly used ratio SD1/SD2 but with more marked separation between classes, having the largest Rank Biserial Correlation of all examined features (p-value = 0.002, RBC = -0.73). We recommend further studies in this area.