Decoding working-memory load duringn-back task performance from high channel fNIRS data.

Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.

On decoding of rapid motor imagery in a diverse population using a high-density NIRS device.

Introduction: Functional near-infrared spectroscopy (fNIRS) aims to infer cognitive states such as the type of movement imagined by a study participant in a given trial using an optical method that can differentiate between oxygenation states of blood in the brain and thereby indirectly between neuronal activity levels. We present findings from an fNIRS study that aimed to test the applicability of a high-density (>3000 channels) NIRS device for use in short-duration (2 s) left/right hand motor imagery decoding in a diverse, but not explicitly balanced, subject population. A side aim was to assess relationships between data quality, self-reported demographic characteristics, and brain-computer interface (BCI) performance, with no subjects rejected from recruitment or analysis. Methods: BCI performance was quantified using several published methods, including subject-specific and subject-independent approaches, along with a high-density fNIRS decoder previously validated in a separate study. Results: We found that decoding of motor imagery on this population proved extremely challenging across all tested methods. Overall accuracy of the best-performing method (the high-density decoder) was 59.1 +/- 6.7% after excluding subjects where almost no optode-scalp contact was made over motor cortex and 54.7 +/- 7.6% when all recorded sessions were included. Deeper investigation revealed that signal quality, hemodynamic responses, and BCI performance were all strongly impacted by the hair phenotypical and demographic factors under investigation, with over half of variance in signal quality explained by demographic factors alone. Discussion: Our results contribute to the literature reporting on challenges in using current-generation NIRS devices on subjects with long, dense, dark, and less pliable hair types along with the resulting potential for bias. Our findings confirm the need for increased focus on these populations, accurate reporting of data rejection choices across subject intake, curation, and final analysis in general, and signal a need for NIRS optode designs better optimized for the general population to facilitate more robust and inclusive research outcomes.

The Lab Streaming Layer for Synchronized Multimodal Recording.

Accurately recording the interactions of humans or other organisms with their environment or other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common LAN. Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features ensure precise, continuous data recording, even in the face of interruptions. The LSL ecosystem has grown to support over 150 data acquisition device classes as of Feb 2024, and establishes interoperability with and among client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording and it is now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis packages, and brain-computer interfaces. Outside of basic science, research, and development, LSL has been used as a resilient and transparent backend in scenarios ranging from art installations to stage performances, interactive experiences, and commercial deployments. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes using multiple data streams at a common timebase while capturing time details for every data frame.

How Sensitive Are EEG Results to Preprocessing Methods: A Benchmarking Study.

Although several guidelines for best practices in EEG preprocessing have been released, even studies that strictly adhere to those guidelines contain considerable variation in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to variations in preprocessing methods and parameters. To address this issue, we analyze the effect of preprocessing methods on downstream EEG analysis using several simple signal and event-related measures. Signal measures include recording-level channel amplitudes, study-level channel amplitude dispersion, and recording spectral characteristics. Event-related methods include ERPs and ERSPs and their correlations across methods for a diverse set of stimulus events. Our analysis also assesses differences in residual signals both in the time and spectral domains after blink artifacts have been removed. Using fully automated pipelines, we evaluate these measures across 17 EEG studies for two ICA-based preprocessing approaches (LARG, MARA) plus two variations of Artifact Subspace Reconstruction (ASR). Although the general structure of the results is similar across these preprocessing methods, there are significant differences, particularly in the low-frequency spectral features and in the residuals left by blinks. These results argue for detailed reporting of processing details as suggested by most guidelines, but also for using a federation of automated processing pipelines and comparison tools to quantify effects of processing choices as part of the research reporting.

Automated EEG mega-analysis II: Cognitive aspects of event related features.

We present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings containing approximately 7.8 million event instances acquired under several different experimental paradigms. Such large-scale analysis is predicated on consistent data organization and event annotation as well as an effective automated preprocessing pipeline to transform raw EEG into a form suitable for comparative analysis. A key component of this analysis is the annotation of study-specific event codes using a common vocabulary to describe relevant event features. We demonstrate that Hierarchical Event Descriptors (HED tags) capture statistically significant cognitive aspects of EEG events common across multiple recordings, subjects, studies, paradigms, headset configurations, and experimental sites. We use representational similarity analysis (RSA) to show that EEG responses annotated with the same cognitive aspect are significantly more similar than those that do not share that cognitive aspect. These RSA similarity results are supported by visualizations that exploit the non-linear similarities of these associations. We apply temporal overlap regression, reducing confounds caused by adjacent event instances, to extract time and time-frequency EEG features (regressed ERPs and ERSPs) that are comparable across studies and replicate findings from prior, individual studies. Likewise, we use second-level linear regression to separate effects of different cognitive aspects on these features across all studies. This work demonstrates that EEG mega-analysis (pooling of raw data across studies) can enable investigations of brain dynamics in a more generalized fashion than single studies afford. A companion paper complements this event-based analysis by addressing commonality of the time and frequency statistical properties of EEG across studies at the channel and dipole level.

Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies.

Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if such analyses in the EEG field are possible or equally fruitful. Here we present the results of a large-scale EEG mega-analysis using 18 studies from six sites representing several different experimental paradigms. We demonstrate that when meta-data are consistent across studies, both channel-level and source-level EEG mega-analysis are possible and can provide insights unavailable in single studies. The analysis uses a fully-automated processing pipeline to reduce line noise, interpolate noisy channels, perform robust referencing, remove eye-activity, and further identify outlier signals. We define several robust measures based on channel amplitude and dispersion to assess the comparability of data across studies and observe the effect of various processing steps on these measures. Using ICA-based dipolar sources, we also observe consistent differences in overall frequency baseline amplitudes across brain areas. For example, we observe higher alpha in posterior vs anterior regions and higher beta in temporal regions. We also detect consistent differences in the slope of the aperiodic portion of the EEG spectrum across brain areas. In a companion paper, we apply mega-analysis to assess commonalities in event-related EEG features across studies. The continuous raw and preprocessed data used in this analysis are available through the DataCatalog at https://cancta.net.

Acquisition of dental skills in preclinical technique courses: influence of spatial and manual abilities.

Sixty years of research have not added up to a concordant evaluation of the influence of spatial and manual abilities on dental skill acquisition. We used Ackerman's theory of ability determinants of skill acquisition to explain the influence of spatial visualization and manual dexterity on the task performance of dental students in two consecutive preclinical technique courses. We measured spatial and manual abilities of applicants to Hamburg Dental School by means of a multiple choice test on Technical Aptitude and a wire-bending test, respectively. Preclinical dental technique tasks were categorized as consistent-simple and inconsistent-complex based on their contents. For analysis, we used robust regression to circumvent typical limitations in dental studies like small sample size and non-normal residual distributions. We found that manual, but not spatial ability exhibited a moderate influence on the performance in consistent-simple tasks during dental skill acquisition in preclinical dentistry. Both abilities revealed a moderate relation with the performance in inconsistent-complex tasks. These findings support the hypotheses which we had postulated on the basis of Ackerman's work. Therefore, spatial as well as manual ability are required for the acquisition of dental skills in preclinical technique courses. These results support the view that both abilities should be addressed in dental admission procedures in addition to cognitive measures.

Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG.

GOAL: We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification. METHODS: The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system. RESULTS: Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ±0.09) and LCMV (0.72 ±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 ±0.16) but significantly better for LCMV (0.82 ±0.12) . CONCLUSION: We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG. SIGNIFICANCE: This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.

The PREP pipeline: standardized preprocessing for large-scale EEG analysis.

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

Prediction of practical performance in preclinical laboratory courses - the return of wire bending for admission of dental students in Hamburg.

Although some recent studies concluded that dexterity is not a reliable predictor of performance in preclinical laboratory courses in dentistry, they could not disprove earlier findings which confirmed the worth of manual dexterity tests in dental admission. We developed a wire bending test (HAM-Man) which was administered during dental freshmen's first week in 2008, 2009, and 2010. The purpose of our study was to evaluate if the HAM-Man is a useful selection criterion additional to the high school grade point average (GPA) in dental admission. Regression analysis revealed that GPA only accounted for a maximum of 9% of students' performance in preclinical laboratory courses, in six out of eight models the explained variance was below 2%. The HAM-Man incrementally explained up to 20.5% of preclinical practical performance over GPA. In line with findings from earlier studies the HAM-Man test of manual dexterity showed satisfactory incremental validity. While GPA has a focus on cognitive abilities, the HAM-Man reflects learning of unfamiliar psychomotor skills, spatial relationships, and dental techniques needed in preclinical laboratory courses. The wire bending test HAM-Man is a valuable additional selection instrument for applicants of dental schools.

Audio podcasts in practical courses in biochemistry - cost-efficient e-learning in a well-proven format from radio broadcasting.

INTRODUCTION: Audio podcasts are an e-learning format that may help to motivate students to deal with the contents of medical education more intensely. We adopted a well-proven format from radio broadcasting, the radio documentary, to direct the listeners' attention to information about practical courses in biochemistry over a period of 20 minutes at most. Information, original sounds, and a specific atmosphere allow listeners to perceive the contents intensely. METHOD: In order to organise the production of the podcast as cost-efficient and least time-consuming as possible, a student, a teacher, a clinician, and a technical assistant compile the core themes of their respective text blocks in an editorial conference first. After that, the speakers can elaborate on and record their blocks independently. Coordination is widely handled by the student. At two points of time, the podcasts were evaluated by the medical students by means of a questionnaire. RESULTS: With little cost and time expenses, eight podcasts were produced. They have been used by the students extensively and have also been evaluated very positively by non-student listeners. For long-term usage, a regular reference to the podcast offer is required in the courses. CONCLUSION: Involving students, successful podcasts can be produced to support classroom teaching with little expenses and contribute to the external presentation of the medical faculty.

The Hamburg selection procedure for dental students - introduction of the HAM-Nat as subject-specific test for study aptitude.

INTRODUCTION: The present study examines the question whether the selection of dental students should be based solely on average school-leaving grades (GPA) or whether it could be improved by using a subject-specific aptitude test. METHODS: The HAM-Nat Natural Sciences Test was piloted with freshmen during their first study week in 2006 and 2007. In 2009 and 2010 it was used in the dental student selection process. The sample size in the regression models varies between 32 and 55 students. RESULTS: Used as a supplement to the German GPA, the HAM-Nat test explained up to 12% of the variance in preclinical examination performance. We confirmed the prognostic validity of GPA reported in earlier studies in some, but not all of the individual preclinical examination results. CONCLUSION: The HAM-Nat test is a reliable selection tool for dental students. Use of the HAM-Nat yielded a significant improvement in prediction of preclinical academic success in dentistry.

Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG.

This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.

EyeCatch: data-mining over half a million EEG independent components to construct a fully-automated eye-component detector.

Independent component analysis (ICA) can find distinct sources of electroencephalographic (EEG) activity, both brain-based and artifactual, and has become a common pre-preprocessing step in analysis of EEG data. Distinction between brain and non-brain independent components (ICs) accounting for, e.g., eye or muscle activities is an important step in the analysis. Here we present a fully automated method to identify eye-movement related EEG components by analyzing the spatial distribution of their scalp projections (scalp maps). The EyeCatch method compares each input scalp map to a database of eye-related IC scalp maps obtained by data-mining over half a million IC scalp maps obtained from 80,006 EEG datasets associated with a diverse set of EEG studies and paradigms. To our knowledge this is the largest sample of IC scalp maps that has ever been analyzed. Our result show comparable performance to a previous state-of-art semi-automated method, CORRMAP, while eliminating the need for human intervention.

BCILAB: a platform for brain-computer interface development.

OBJECTIVE: The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human-computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. APPROACH: Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. MAIN RESULTS: To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. SIGNIFICANCE: The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.

EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing.

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments.

A Dry EEG-System for Scientific Research and Brain-Computer Interfaces.

Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain-computer interfacing (BCI), EEG recently has become a tool to enhance human-machine interaction. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, event-related potentials (ERP) were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differences between the wet and dry electrode systems. We conclude that the tested dry electrodes were capable to detect EEG signals with good quality and that these signals can be used for research or BCI applications. Easy to handle electrodes may help to foster the use of EEG among a wider range of potential users.

Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general.

Cognitive monitoring is an approach utilizing realtime brain signal decoding (RBSD) for gaining information on the ongoing cognitive user state. In recent decades this approach has brought valuable insight into the cognition of an interacting human. Automated RBSD can be used to set up a brain-computer interface (BCI) providing a novel input modality for technical systems solely based on brain activity. In BCIs the user usually sends voluntary and directed commands to control the connected computer system or to communicate through it. In this paper we propose an extension of this approach by fusing BCI technology with cognitive monitoring, providing valuable information about the users' intentions, situational interpretations and emotional states to the technical system. We call this approach passive BCI. In the following we give an overview of studies which utilize passive BCI, as well as other novel types of applications resulting from BCI technology. We especially focus on applications for healthy users, and the specific requirements and demands of this user group. Since the presented approach of combining cognitive monitoring with BCI technology is very similar to the concept of BCIs itself we propose a unifying categorization of BCI-based applications, including the novel approach of passive BCI.

Estimation of task workload from EEG data: new and current tools and perspectives.

We report, as part of the EMBC meeting Cognitive State Assessment (CSA) competition 2011, an empirical comparison using robust cross-validation of the performance of eleven computational approaches to real-time electroencephalography (EEG) based mental workload monitoring on Multi-Attribute Task Battery data from eight subjects. We propose a new approach, Overcomplete Spectral Regression, that combines several potentially advantageous attributes and empirically demonstrate its superior performance on these data compared to the ten other CSA methods tested. We discuss results from computational, neuroscience and experimentation points of view.