Monitor Brain Activity Research Articles

Retraction Note: EEG signal based brain activity monitoring using adaptive wavelet transform and activity learning neural vector (ALNV) classification technique

Retraction Note: EEG signal based brain activity monitoring using adaptive wavelet transform and activity learning neural vector (ALNV) classification technique

The absence of dominant alpha-oscillatory EEG activity during emergence from delta-dominant anesthesia predicts neurocognitive impairment- results from a prospective observational trial

Study objectivePostoperative neurocognitive disorders (PND) are common complications after surgery under general anesthesia. In our aging society the incidence of PND will increase. Hence, interdisciplinary efforts should be taken to minimize the occurrence of PND. Electroencephalographic (EEG) monitoring of brain activity during anesthesia or emergence from anesthesia is a promising tool to identify patients at risk. We therefore investigated whether we could identify specific EEG signatures during emergence of anesthesia that are associated with the occurrence of PND. Design and patientsWe performed a prospective observational investigation on 116 patients to evaluate the EEG features during emergence from general anesthesia dominated by slow delta waves in patients with and without delirium in the postoperative care unit (PACU-D) as assessed by the CAM-ICU and the RASS. Main resultsDuring emergence both the frontal and global EEG of patients with PACU-D were significantly different from patients without PACU-D. PACU-D patients had lower relative alpha power and reduced fronto-parietal alpha coherence. ConclusionsWith our analysis we show differences in EEG features associated with anesthesia emergence in patients with and without PACU-D. Frontal and global EEG alpha-band features could help to identify patients with PACU-D.Clinical trial number: NCT03287401

PhyMask: Robust Sensing of Brain Activity and Physiological Signals During Sleep with an All-textile Eye Mask

Clinical-grade wearable sleep monitoring is a challenging problem since it requires concurrently monitoring brain activity, eye movement, muscle activity, cardio-respiratory features, and gross body movements. This requires multiple sensors to be worn at different locations as well as uncomfortable adhesives and discrete electronic components to be placed on the head. As a result, existing wearables either compromise comfort or compromise accuracy in tracking sleep variables. We propose PhyMask, an all-textile sleep monitoring solution that is practical and comfortable for continuous use and that acquires all signals of interest to sleep solely using comfortable textile sensors placed on the head. We show that PhyMask can be used to accurately measure all the signals required for precise sleep stage tracking and to extract advanced sleep markers such as spindles and K-complexes robustly in the real-world setting. We validate PhyMask against polysomnography (PSG) and show that it significantly outperforms two commercially-available sleep tracking wearables—Fitbit and Oura Ring.

Skin‐Like Transparent Sensor Sheet for Remote Healthcare Using Electroencephalography and Photoplethysmography

AbstractThe growing demand for efficient home healthcare applications for brain disorder diagnostics and treatment has inspired the development of wearable devices for monitoring brain activity. However, flexible probes that have improved biocompatibility for wearable devices are markedly affected by noise due to contact interface issues. In this study, a stretchable (≤1500% strain) and transparent (over 85% transmittance) biocompatible electrode that steadily adheres to skin is developed to fabricate an imperceptible sheet‐type device that wirelessly records electroencephalograms (EEGs). The multifunctional characteristic of the electrode results from the double‐network structure in an elastomer/conductive additive blend on a metal‐nanowire‐based track, which contributed to EEG monitoring with ultralow noise (≈0.14 µV noise floor) and sleep‐stage classification. Furthermore, the optical transparency enabled camera‐based photoplethysmography to detect pulse waves and blood oxygen saturation without interfering with the light path, which is a crucial factor in realizing a remote healthcare system.

Luminescence Thermometry for Brain Activity Monitoring: A Perspective.

Minimally invasive monitoring of brain activity is essential not only to gain understanding on the working principles of the brain, but also for the development of new diagnostic tools. In this perspective we describe how brain thermometry could be an alternative to conventional methods (e.g., magnetic resonance or nuclear medicine) for the acquisition of thermal images of the brain with enough spatial and temperature resolution to track brain activity in minimally perturbed animals. We focus on the latest advances in transcranial luminescence thermometry introducing a critical discussion on its advantages and shortcomings. We also anticipate the main challenges that the application of luminescent nanoparticles for brain thermometry will face in next years. With this work we aim to promote the development of near infrared luminescence for brain activity monitoring, which could also benefit other research areas dealing with the brain and its illnesses.

Ultrasoft Porous 3D Conductive Dry Electrodes for Electrophysiological Sensing and Myoelectric Control.

Biopotential electrodes have found broad applications in health monitoring, human-machine interactions, and rehabilitation. Here, we report the fabrication and applications of ultrasoft breathable dry electrodes that can address several challenges for their long-term wearable applications - skin compatibility, wearability, and long-term stability. The proposed electrodes rely on porous and conductive silver nanowire based nanocomposites as the robust mechanical and electrical interface. The highly conductive and conformable structure eliminates the necessity of conductive gel while establishing a sufficiently low electrode-skin impedance for high-fidelity electrophysiological sensing. The introduction of gas-permeable structures via a simple and scalable method based on sacrificial templates improves breathability and skin compatibility for applications requiring long-term skin contact. Such conformable and breathable dry electrodes allow for efficient and unobtrusive monitoring of heart, muscle, and brain activities. In addition, based on the muscle activities captured by the electrodes and a musculoskeletal model, electromyogram-based neural-machine interfaces were realized, illustrating the great potential for prosthesis control, neurorehabilitation, and virtual reality.

Recent Advances of Artificial Intelligence Tools in Attention-Deficit Hyperactivity Disorder (ADHD)

Abstract: Attention deficit hyperactive disorder or ADHD is a common disorder among children, and if not identified early, it may affect the child’s later life. Pharmacotherapy in ADHD has been linked to the emergence of other emotional disorders. Children who get pharmacological treatment are more likely to continue taking these medications until adulthood, increasing their risk of acquiring other psychological problems. As a result, the majority of ADHD patients are eventually prescribed numerous medicines to manage emotional difficulties as well. Thus, AI tools are seen to be a boon for ADHD patients and clinicians. There have been emerging approaches in using artificial intelligence tools to diagnose and treat ADHD in recent years. Different algorithms and medical devices are used for greater accuracy and precision. The various neural networks detect complex signals in the human brain and analyze them. As it is a neurodevelopmental disorder, AI gives the best tools for proper diagnosis and treatment. Virtual and physical branches of AI are a great help to the patient. This review article focuses on the use of various AI models and tools that employ ADHD symptoms, MRI scans, and EEG signals, using electroencephalogram sensors to monitor brain activity, to help physicians better manage this prevalent neurodevelopmental disorder.

333 First FDA Cleared Thin Film Electrode for Intracranial Recording and Monitoring of Brain Activity – Device Testing and Initial Clinical Use

INTRODUCTION: Subdural strip and grid (SDG) electrodes are routinely used for pre-surgical evaluation of patients with drug resistant epilepsy (DRE). Although these electrodes have been in the US market since their first FDA clearance in 1985, their fabrication, materials and properties have hardly changed. They are made of silicone, are thick (>0.5 mm), manufactured by hand, and do not optimally conform to brain convolutions. Technological solutions that overcome these limitations are needed. METHODS: Thin film electrode (0.08 mm) arrays were manufactured from polyimide with platinum contacts (multiple configurations, 3 mm contact diameter, spaced 10 mm apart). Electrochemical and electrical testing include electrolyte interface impedance, cyclic voltammetry and other impedance and isolation tests, according to the International Electrotechnical Commission 60601 for proving device safety and performance. Biocompatibility was tested using standard methods according to International Organization for Standardization 10993. Post-market data were collected employing a user feedback questionnaire using a rating scale (1 to 5, with 5 being the highest). RESULTS: Thin film electrodes passed all electrochemical, electrical and biocompatibility testing required for FDA clearance (obtained Nov 2019). Electrodes (n = 19) were used in clinical practice between Nov 23, 2020 and May 3, 2021 in the epilepsy monitoring units and for intraoperative monitoring (IOM) for recording brain activity in (a) DRE patients undergoing evaluation for potential surgical resection, and (b) patients undergoing tumor resection. Users (neurosurgeons; n = 6) provided ratings between 4 and 5 regarding the properties (thickness, flexibility, weight), features (ease of placement, conformability to the brain areas) and performance (qualitative signal recording quality) of the electrodes during surgical implantation. CONCLUSION: This is the first thin film electrode technology FDA cleared for use in clinical practice. The properties of these thin, flexible polyimide electrode arrays may overcome many limitations of the existing technology. Initial clinical use indicated that the technology is feasible for surgical implantation with greater flexibility compared to silicone based cortical electrodes.

Exploring construction workers’ brain connectivity during hazard recognition: a cognitive psychology perspective

Monitoring brain activity is a novel development for hazard recognition in the construction industry. However, very few empirical studies have investigated the causal connections within the brain. This study aimed to explore the brain connectivity of construction workers during hazard recognition. Electroencephalogram data were collected from construction workers to perform image-based hazard recognition tasks. The Granger causality-based adaptive directed transfer function was used to simulate directed and time-variant information flow across the observed brain activity from the perspective of cognitive psychology. The results suggested a top-down modulation of behavioral goals originating from the dorsal attention network during hazard relocation. The sensory cortex predominantly serves as the information outlet center and interacts extensively with the frontal and visual cortices, reflecting a top-down attention reorientation mechanism for processing threatening stimuli. Our findings of brain effective connectivity supplement new evidence underpinning parallel distributed processing theory for workplace hazard recognition.

Coagulation Bath‐Assisted 3D Printing of PEDOT:PSS with High Resolution and Strong Substrate Adhesion for Bioelectronic Devices

AbstractPoly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is a promising material because of its favorable electrical and mechanical properties, stability in ambient environments, and biocompatibility. It finds broad application in energy storage, flexible electronics, and bioelectronics. Additive manufacturing opens a plethora of new avenues to form and shape PEDOT:PSS, allowing for the rapid construction of customized geometries. However, there are difficulties in printing PEDOT:PSS while maintaining its attractive properties. A 3D printing method for PEDOT:PSS using a room‐temperature coagulation bath‐based direct ink writing technique is reported. This technique enables fabrication of PEDOT:PSS into parts that are of high resolution and high conductivity, while maintaining stable electrochemical properties. The coagulation bath can be further modified to improve the mechanical properties of the resultant printed part via a one‐step reaction. Furthermore, it is demonstrated that a simple post‐processing step allows the printed electrodes to strongly adhere to several substrates under aqueous conditions, broadening their use in bioelectronics. Employing 3D printing of PEDOT:PSS, a cortex‐wide neural interface is fabricated, and intracranial electrical stimulation and simultaneous optical monitoring of mice brain activity with wide field calcium imaging are demonstrated. This reported 3D‐printing technique eliminates the need for cumbersome experimental setups while offering desired material properties.

Optimization Study of High-definition Multi-channel Noninvasive Brain Stimulation Technique Based on Real-time Feedback of Brain Activity

Noninvasive brain stimulation methods are examples of evolving treatments for strokes and neurodegenerative diseases. Methods such as transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS) can be combined with exercise therapy and rehabilitation to enhance patient recovery. Professor Yun-Hee Kim from the NEURI Laboratory within the Samsung Medical Center and the Sungkyunkwan University School of Medicine in South Korea is working to develop personalized non-invasive neuromodulation technology that, due to its individual approach, will further enhance the effectiveness of treatment. Kim’s goal is to develop a personalized stimulation technology that can monitor brain activity in real-time with no adverse effects on the patient. As such, she is working on a combination study involving high-definition transcranial direct current stimulation (HD-tDCS) and functional near-infrared spectroscopy (fNIRS). It combines neuroplasticity and neuroimaging tools in an effort to maximize brain plasticity and seeks to overcome the existing limitations of tDCS and fNIRS. Kim and the team intend to complete the technology they are working on through technical optimization and by commercializing the product, the researchers hope that they can help the rehabilitation of patients and support their return to society. It is important to Kim and the team that their technology works with both software and hardware and can be readily used by non-experts.

Epileptic Seizure Detection Using Brain-Rhythmic Recurrence Biomarkers and ONASNet-Based Transfer Learning.

The electroencephalogram (EEG) tool has great potential for real-time monitoring of abnormal brain activities, such as preictal and ictal seizures. Developing an EEG-based detection system for patients with epilepsy is vital for clinical management and targeted therapy. This paper proposes a single-channel seizure detection system using brain-rhythmic recurrence biomarkers (BRRM) and an optimized model (ONASNet). BRRM is a direct mapping of the recurrence morphology of brain rhythms in phase space; it reflects the nonlinear dynamics of original EEG signals. The architecture of ONASNet is determined through a modified neural network searching strategy. Then, we exploited transfer learning to apply ONASNet to our EEG data. The combination of BRRM and ONASNet leverages the multiple channels of a neural network to extract features from different brain rhythms simultaneously. We evaluated the efficiency of BRRM-ONASNet on the real EEG recordings derived from Bonn University. In the experiments, different transfer-learning models (TLMs) are respectively constructed using ONASNet and seven well-known neural network structures (VGG16/VGG19/ResNet50/InceptionV3/DenseNet121/Xception/NASNet). Moreover, we compared those TLMs by model size, computing complexity, learning capability, and prediction latency. ONASNet outperforms other structures by strong learning capability, high stability, small model size, short latency, and less requirement of computing resources. Comparing BRRM-ONASNet with other existing methods, our work performs better than others with 100% accuracy under the identical dataset and same detection task. Contributions: The proposed method in this study, analyzing nonlinear features from phase-space representations using a deep neural network, provides new insights for EEG decoding. The successful application of this method in epileptic-seizure detection contributes to computationally medical assistance for epilepsy.

A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification.

Functional near-infrared spectroscopy (fNIRS), a non-invasive optical technique, is widely used to monitor brain activities for disease diagnosis and brain-computer interfaces (BCIs). Deep learning-based fNIRS classification faces three major barriers: limited datasets, confusing evaluation criteria, and domain barriers. We apply more appropriate evaluation methods to three open-access datasets to solve the first two barriers. For domain barriers, we propose a general and scalable vision fNIRS framework that converts multi-channel fNIRS signals into multi-channel virtual images using the Gramian angular difference field (GADF). We use the framework to train state-of-the-art visual models from computer vision (CV) within a few minutes, and the classification performance is competitive with the latest fNIRS models. In cross-validation experiments, visual models achieve the highest average classification results of 78.68% and 73.92% on mental arithmetic and word generation tasks, respectively. Although visual models are slightly lower than the fNIRS models on unilateral finger- and foot-tapping tasks, the F1-score and kappa coefficient indicate that these differences are insignificant in subject-independent experiments. Furthermore, we study fNIRS signal representations and the classification performance of sequence-to-image methods. We hope to introduce rich achievements from the CV domain to improve fNIRS classification research.

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

Increased Connectivity among Sensory and Motor Regions during Visual and Audiovisual Speech Perception.

In everyday conversation, we usually process the talker's face as well as the sound of the talker's voice. Access to visual speech information is particularly useful when the auditory signal is degraded. Here, we used fMRI to monitor brain activity while adult humans (n = 60) were presented with visual-only, auditory-only, and audiovisual words. The audiovisual words were presented in quiet and in several signal-to-noise ratios. As expected, audiovisual speech perception recruited both auditory and visual cortex, with some evidence for increased recruitment of premotor cortex in some conditions (including in substantial background noise). We then investigated neural connectivity using psychophysiological interaction analysis with seed regions in both primary auditory cortex and primary visual cortex. Connectivity between auditory and visual cortices was stronger in audiovisual conditions than in unimodal conditions, including a wide network of regions in posterior temporal cortex and prefrontal cortex. In addition to whole-brain analyses, we also conducted a region-of-interest analysis on the left posterior superior temporal sulcus (pSTS), implicated in many previous studies of audiovisual speech perception. We found evidence for both activity and effective connectivity in pSTS for visual-only and audiovisual speech, although these were not significant in whole-brain analyses. Together, our results suggest a prominent role for cross-region synchronization in understanding both visual-only and audiovisual speech that complements activity in integrative brain regions like pSTS.SIGNIFICANCE STATEMENT In everyday conversation, we usually process the talker's face as well as the sound of the talker's voice. Access to visual speech information is particularly useful when the auditory signal is hard to understand (e.g., background noise). Prior work has suggested that specialized regions of the brain may play a critical role in integrating information from visual and auditory speech. Here, we show a complementary mechanism relying on synchronized brain activity among sensory and motor regions may also play a critical role. These findings encourage reconceptualizing audiovisual integration in the context of coordinated network activity.

Numerical estimation of the B1 transmit field distortion in a copper EEG trace comparison with the thin-film based resistive trace "NeoNet".

This study investigates the effects of EEG traces in B1 transmit field distortion in a 3T MRI. EEG is a non-invasive method to monitor brain activities. Although EEG monitors brain activities with a high temporal resolution, it has trouble localizing the signal source. The EEG-fMRI is the multimodal imaging method, but care is needed to use EEG while in MRI as EEG traces create the signal distortion to the MRI. To tackle this problem, resistive traces are developed using thin-film technology to reduce the signal distortion during MRI. Numerical simulation was used to estimate the amount of B1 transmit field distortion of NeoNet and copper-based EEG nets (CuNet - with and without current limiting resistors) compared with the case without EEG net (NoNet). The reduced B1 transmit field distortion is estimated in the case of NeoNet compared to the CuNets. NeoNet is an MR-compatible high-density EEG net designed for pediatric subjects. The proposed NeoNet traces will facilitate/enable such EEG/fMRI pediatric studies with mitigated artifacts, which in turn will help to move the pediatric EEG/fMRI field forward.Clinical Relevance-This study estimates the benefit of the thin-film based EEG net with reduced B1 transmit artifact for the multimodal study of EEG-fMRI. The results are compared with commercial EEG trace made with copper metal with current limiting resistors. It is reported that about 470,000 children are suffering from Epilepsy. The MR-compatible resistive EEG traces se EEG-fMRI has potential to be a valuable tool to help understand pediatric Epilepsy and move the pediatric EEG-fMRI field forward.

Detecting Epileptic Seizures via Non-Uniform Multivariate Embedding of EEG Signals.

Efficient real-time detection of epileptic seizures remains a challenging task in clinical practice. In this study, we introduce a new thresholding method to monitor brain activities via a non-uniform multivariate (NUM) embedding of multi-channel electroencephalogram (EEG) signals. Specifically, we present a NUM embedding optimization problem to identify the best embedding parameters. We originate one feature, named non-uniform multivariate multiscale entropy (NUMME), which is extracted from the NUM embedded EEG data. Finally, the extracted feature, compared to an individualized threshold, is used for monitoring and detecting seizure onsets. Experimental results on the real CHB-MIT Scalp EEG database show that our approach achieves a comparable performance to the state-of-art methods. Moreover, it is important to note that we accomplish this without using any sophisticated machine learning algorithms.

Prediction of patient survival following postanoxic coma using EEG data and clinical features.

Electroencephalography (EEG) is an effective and non-invasive technique commonly used to monitor brain activity and assist in outcome prediction for comatose patients post cardiac arrest. EEG data may demonstrate patterns associated with poor neurological outcome for patients with hypoxic injury. Thus, both quantitative EEG (qEEG) and clinical data contain prognostic information for patient outcome. In this study we use machine learning (ML) techniques, random forest (RF) and support vector machine (SVM) to classify patient outcome post cardiac arrest using qEEG and clinical feature sets, individually and combined. Our ML experiments show RF and SVM perform better using the joint feature set. In addition, we extend our work by implementing a convolutional neural network (CNN) based on time-frequency images derived from EEG to compare with our qEEG ML models. The results demonstrate significant performance improvement in outcome prediction using non-feature based CNN compared to our feature based ML models. Implementation of ML and DL methods in clinical practice have the potential to improve reliability of traditional qualitative assessments for postanoxic coma patients.

Validating Poly(3,4-ethylene dioxythiophene) Polystyrene Sulfonate-Based Textile Electroencephalography Electrodes by a Textile-Based Head Phantom.

It is important to go through a validation process when developing new electroencephalography (EEG) electrodes, but it is impossible to keep the human mind constant, making the process difficult. It is also very difficult to identify noise and signals as the input signal is unknown. In this work, we have validated textile-based EEG electrodes constructed from a poly(3,4-ethylene dioxythiophene) polystyrene sulfonate:/polydimethylsiloxane coated cotton fabric using a textile-based head phantom. The performance of the textile-based electrode has also been compared against a commercial dry electrode. The textile electrodes collected a signal to a smaller skin-to-electrode impedance (−18.9%) and a higher signal-to-noise ratio (+3.45%) than Ag/AgCl dry electrodes. From an EEGLAB, it was observed that the inter-trial coherence and event-related spectral perturbation graphs of the textile-based electrodes were identical to the Ag/AgCl electrodes. Thus, these textile-based electrodes can be a potential alternative to monitor brain activity.

Non-invasive ultrasound stimulation on the retina and visual cortex for visual restoration

Nearly 30% of the U.S. population aged over 75 have age-related macular degeneration and retinitis pigmentosa. 10% of them may become legally blind. Currently, blindness cannot be cured and patients’ living quality can be compromised severely. Electrical visual prostheses are used as the best near-term strategy. However, they have challenges including invasive devices, complex implantation surgeries, and limited resolution. Non-invasive ultrasonic neuromodulation can be a promising technology for non-invasive visual prostheses. Our work showed that direct ultrasound stimulation on the retina can evoke neuron activities in either normal-sighted or retinal degenerated blind rats in vivo, indicating a promising future of ultrasound stimulation as a novel and non-invasive visual prosthesis for translational applications in blind patients. For patients with damaged optic nerves, retinal prostheses are not effective. Thus, we also investigated the feasibility of transcranial focused ultrasound for non-invasive stimulation of the visual cortex to develop a non-invasive cortical visual prosthesis. Besides stimulations, ultrasound has been used to non-invasively monitor brain activity by imaging cortical blood flow. Our study shows that ultrasound-induced visual responses can be monitored by ultrasound flow imaging of the brain, constructing an ultrasound stimulation-recording closed-loop system.