Concurrent fNIRS and EEG for brain function investigation: A systematic, methodology-focused review

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research

Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals

Brain–computer interface (BCI) is a powerful system for communicating between the brain and outside world. Traditional BCI systems work based on electroencephalogram (EEG) signals only. Recently, researchers have used a combination of EEG signals with other signals to improve the performance of BCI systems. Among these signals, the combination of EEG with functional near-infrared spectroscopy (fNIRS) has achieved favourable results. In most studies, only EEGs or fNIRs have been considered as chain-like sequences, and do not consider complex correlations between adjacent signals, neither in time nor channel location. In this study, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features. The proposed model incorporates the spatial relationship between EEG and fNIRS signals. This could be implemented by transforming the sequences of these chain-like signals into hierarchical three-rank tensors. The tests show that the proposed model has a precision of 99.6%

A Newcomer\u27s Guide to Functional Near Infrared Spectroscopy Experiments

This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Towards simultaneous electroencephalography and functional near-infrared spectroscopy for improving diagnostic accuracy in prolonged disorders of consciousness: a healthy cohort study

Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behaviour from spontaneous behaviour. As many such behaviours are minimal and inconsistent, behavioural assessments are susceptible to diagnostic errors. Advanced neuroimaging tools such as functional magnetic resonance imaging and electroencephalography (EEG) can bypass behavioural responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. As each individual neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.), this thesis studies on healthy individuals a burgeoning technique of non-invasive electrical and optical neuroimaging—simultaneous EEG and functional near-infrared spectroscopy (fNIRS)—that can be applied at the bedside. Measuring reliable covert behaviours is correlated with participant engagement, instrumental sensitivity and the accurate localisation of responses, aspects which are further addressed over three studies. Experiment 1 quantifies the typical EEG changes in response to covert commands in the absence and presence of an object. This is investigated to determine whether a goal-directed task can yield greater EEG control accuracy over simple monotonous imagined single-joint actions. Experiment 2 characterises frequency domain NIRS changes in response to overt and covert hand movements. A method for reconstructing haemodynamics using the less frequently investigated phase parameter is outlined and the impact of noise contaminated NIRS measurements are discussed. Furthermore, classification performances between frequency-domain and continuous-wave-like signals are compared. Experiment 3 lastly applies these techniques to determine the potential of simultaneous EEG-fNIRS classification. Here a sparse channel montage that would ultimately favour clinical utility is used to demonstrate whether such a hybrid method containing rich spatial and temporal information can improve the classification of covert responses in comparison to unimodal classification of signals. The findings and discussions presented within this thesis identify a direction for future research in order to more accurately translate the brain state of patients with a prolonged disorder of consciousness

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State

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