EEG Electrodes Research Articles

Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75–96.9% of channels) with a 1.65–5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2–15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain–computer interface (BCI).

Fully 3D-Printed Dry EEG Electrodes.

Electroencephalography (EEG) is used to detect brain activity by recording electrical signals across various points on the scalp. Recent technological advancement has allowed brain signals to be monitored continuously through the long-term usage of EEG wearables. However, current EEG electrodes are not able to cater to different anatomical features, lifestyles, and personal preferences, suggesting the need for customisable electrodes. Despite previous efforts to create customisable EEG electrodes through 3D printing, additional processing after printing is often needed to achieve the required electrical properties. Although fabricating EEG electrodes entirely through 3D printing with a conductive material would eliminate the need for further processing, fully 3D-printed EEG electrodes have not been seen in previous studies. In this study, we investigate the feasibility of using a low-cost setup and a conductive filament, Multi3D Electrifi, to 3D print EEG electrodes. Our results show that the contact impedance between the printed electrodes and an artificial phantom scalp is under 550 Ω, with phase change of smaller than -30∘, for all design configurations for frequencies ranging from 20 Hz to 10 kHz. In addition, the difference in contact impedance between electrodes with different numbers of pins is under 200 Ω for all test frequencies. Through a preliminary functional test that monitored the alpha signals (7-13 Hz) of a participant in eye-open and eye-closed states, we show that alpha activity can be identified using the printed electrodes. This work demonstrates that fully 3D-printed electrodes have the capability of acquiring relatively high-quality EEG signals.

Novel welch-transform based enhanced spectro-temporal analysis for cognitive microsleep detection using a single electrode EEG

The growing demand for semi-autonomous human–machine systems has led to an increased requirement for human fatigue detection. Direct and invasive approaches for microsleep detection include cognitive computing methods using Brain-Computer Interfaces (BCI). The contextual integration of multi-channel or heterogeneous signals for sleep staging remains a formidable challenge. In addition, the cost of acquiring many signals is significantly higher than that of acquiring a single signal. Consequently, researchers have recently attempted to utilize single-channel EEG over multi-channel acquisition systems for sleep staging. The Fast Fourier Transform (FFT) has been widely used in previous research findings for spectral analysis of complex time-series data streams. In contrast to the FFT, we utilize here, for the first time, the Welch Transform which can give higher stability in noise reduction for spectral analysis. Specifically, we provide a novel method to implement the short-time Welch transform (STWT) as an enhanced technique for the spectro-temporal analysis of single-electrode EEG signals. Further, our proposed model utilizes attention-based spatial and channel-wise inter-dependencies using a one-dimensional causal convolutional neural network (CNN) to extract contextual features automatically. Finally, we demonstrate an end-to-end proof of concept for our data extraction, adaptive data resampling, manual feature extraction, and deep-neural network-based modeling architecture. Comparative simulation results using the benchmark, maintenance of wake-fullness test (MWT) dataset for microsleep detection during automobile transportation, show that our proposed end-to-end system, utilizing novel STWT-based enhanced spectro-temporal analysis, outperforms current state-of-the-art methods, delivering 95% and 89% test accuracy for the case of temporal and spectral data inputs, respectively.

Functional Neuroimaging in Patients With Disorders of Consciousness: Caution Advised.

Functional Neuroimaging in Patients With Disorders of Consciousness: Caution Advised.

N°359 – Improving by removing: Early prognostication in postanoxic coma with reduced electrode set and EEG features in a logistic regression model

N°359 – Improving by removing: Early prognostication in postanoxic coma with reduced electrode set and EEG features in a logistic regression model

A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings.

Wearable electroencephalography (EEG) has the potential to improve everyday life through brain-computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed EEG systems that can still collect neural activity precisely. For example, researchers are using flexible EEG electrode arrays that can be attached around the ear (cEEGrids) to study neural activations in everyday life situations. However, the use of such concealed EEG approaches is limited by measurement challenges such as reduced signal amplitudes and high recording system costs. In this article, we compare the performance of a lower-cost open-source amplification system, the OpenBCI Cyton+Daisy boards, with a benchmark amplifier, the MBrainTrain Smarting Mobi. Our results show that the OpenBCI system is a viable alternative for concealed EEG research, with highly similar noise performance, but slightly lower timing precision. This system can be a great option for researchers with a smaller budget and can, therefore, contribute significantly to advancing concealed EEG research.

Emotion Recognition Using a Novel Granger Causality Quantifier and Combined Electrodes of EEG.

Electroencephalogram (EEG) connectivity patterns can reflect neural correlates of emotion. However, the necessity of evaluating bulky data for multi-channel measurements increases the computational cost of the EEG network. To date, several approaches have been presented to pick the optimal cerebral channels, mainly depending on available data. Consequently, the risk of low data stability and reliability has increased by reducing the number of channels. Alternatively, this study suggests an electrode combination approach in which the brain is divided into six areas. After extracting EEG frequency bands, an innovative Granger causality-based measure was introduced to quantify brain connectivity patterns. The feature was subsequently subjected to a classification module to recognize valence-arousal dimensional emotions. A Database for Emotion Analysis Using Physiological Signals (DEAP) was used as a benchmark database to evaluate the scheme. The experimental results revealed a maximum accuracy of 89.55%. Additionally, EEG-based connectivity in the beta-frequency band was able to effectively classify dimensional emotions. In sum, combined EEG electrodes can efficiently replicate 32-channel EEG information.

Development of Low-Contact-Impedance Dry Electrodes for Electroencephalogram Signal Acquisition.

Dry electroencephalogram (EEG) systems have a short set-up time and require limited skin preparation. However, they tend to require strong electrode-to-skin contact. In this study, dry EEG electrodes with low contact impedance (<150 kΩ) were fabricated by partially embedding a polyimide flexible printed circuit board (FPCB) in polydimethylsiloxane and then casting them in a sensor mold with six symmetrical legs or bumps. Silver-silver chloride paste was used at the exposed tip of each leg or bump that must touch the skin. The use of an FPCB enabled the fabricated electrodes to maintain steady impedance. Two types of dry electrodes were fabricated: flat-disk electrodes for skin with limited hair and multilegged electrodes for common use and for areas with thick hair. Impedance testing was conducted with and without a custom head cap according to the standard 10-20 electrode arrangement. The experimental results indicated that the fabricated electrodes exhibited impedance values between 65 and 120 kΩ. The brain wave patterns acquired with these electrodes were comparable to those acquired using conventional wet electrodes. The fabricated EEG electrodes passed the primary skin irritation tests based on the ISO 10993-10:2010 protocol and the cytotoxicity tests based on the ISO 10993-5:2009 protocol.

Detecting Microstate Transition in Human Brain via Eigenspace of Spatiotemporal Graph

In this letter, a novel approach for detecting the transition of electroencephalography (EEG) microstates of the human brain has been proposed. We have considered the EEG electrodes as nodes of a graph and the correlation between the electrodes' signals as the edge weights. Then, using spectral analysis of the graph, a method has been proposed for detecting the transition of the microstates. The proposed method is comprised of two steps. First, a spatiotemporal graph is constructed using the correlation between the Laplacian of the spatial graph at consecutive time instants. Then, using the principal angles of the eigenspace of the graph, the transition of the EEG microstate has been detected. Experimental results on two publicly available datasets show that the proposed method performs more accurately than state of the art. On the first dataset for the 15 out of 20 subjects, the spatiotemporal graph approach improved the accuracy of detecting the transition time. On the second dataset, it missed only two transitions as opposed to the spatial graph, which failed to detect overall ten transitions across all the subjects.

Resting-state EEG Predicts Cognitive Impairment in Parkinson’s Disease (P6-11.015)

<h3>Objective:</h3> To develop and evaluate an EEG-based assay to predict cognitive functions in Parkinson’s disease (PD) using resting-state EEG data. <h3>Background:</h3> Cognitive dysfunction is a prominent feature of PD. PD related cognitive impairment is usually diagnosed by neuropsychological testing; however, these tests are complex, cannot capture cognitive fluctuations in PD patients, and are not readily compatible with repeated measurements, real-time feedback, or closed-loop neuromodulation therapies. <h3>Design/Methods:</h3> We recorded a few minutes of resting-state EEG data from a few electrodes. Our approach optimized a data-driven, machine-learning technique called ‘Linear predictive coding EEG Algorithms for Parkinson’s disease’ (LEAD-PD) to predict cognitive measures from 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal Cognitive Assessment (MOCA), as well as with different cognitive domains from NIH Toolbox, using multiple cross-validation schemes and randomization tests. We also investigated the relationship between them with multiple regression models, compared our approach with traditional EEG-based features and measured robustness with data truncation tests. <h3>Results:</h3> We found a strong correlation (rho = 0.69, <i>p</i> value &lt; 0.001) between our proposed LEAD-PD cognitive index and the MOCA score using data from only 8 EEG electrodes. LEAD-PD index also strongly predicted other cognitive domains from the NIH toolbox (Dimensional Change Card Sort, Flanker Attention and Inhibitory Control, Picture Vocabulary Task, Pattern Completion Speed Processing, and the Picture Sequence Memory tests). Our approach was consistently more predictive than traditional EEG spectral analysis, yielded 80.44% accuracy (0.90 AUC, 79.52 % sensitivity, and 81.14% specificity) in detecting cognitive impairment, and extended to both PD and control participants. The relationship between our approach and the MOCA was nonlinear with an R² of 0.46 for quadratic regression models. <h3>Conclusions:</h3> LEAD-PD approach using resting-state EEG is computationally-efficient and capable of providing continuous real-time control and feedback, facilitating the diagnosis and monitoring of cognitive impairment in PD. <b>Disclosure:</b> An immediate family member of Dr. Uc has received personal compensation in the range of $500-$4,999 for serving as an officer or member of the Board of Directors for American Board of Pediatrics. Dr. Uc has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier. The institution of Dr. Uc has received research support from Department of Veterans Affairs. The institution of Dr. Uc has received research support from Department of Defense. The institution of Dr. Uc has received research support from Michael J. Fox Foundation. The institution of an immediate family member of Dr. Uc has received research support from NIH. Dr. Anjum has nothing to disclose. Dr. Dasgupta has nothing to disclose. Dr. Narayanan has nothing to disclose.

Origin, synchronization, and propagation of sleep slow waves in children

Study ObjectivesSleep slow wave activity, as measured using EEG delta power (<4 Hz), undergoes significant changes throughout development, mirroring changes in brain function and anatomy. Yet, age-dependent variations in the characteristics of individual slow waves have not been thoroughly investigated. Here we aimed at characterizing individual slow wave properties such as origin, synchronization, and cortical propagation at the transition between childhood and adulthood. MethodsWe analyzed overnight high-density (256 electrodes) EEG recordings of healthy typically developing children (N = 21, 10.3 ± 1.5 years old) and young healthy adults (N = 18, 31.1 ± 4.4 years old). All recordings were preprocessed to reduce artifacts, and NREM slow waves were detected and characterized using validated algorithms. The threshold for statistical significance was set at p = 0.05. ResultsThe slow waves of children were larger and steeper, but less widespread than those of adults. Moreover, they tended to mainly originate from and spread over more posterior brain areas. Relative to those of adults, the slow waves of children also displayed a tendency to more strongly involve and originate from the right than the left hemisphere. The separate analysis of slow waves characterized by high and low synchronization efficiency showed that these waves undergo partially distinct maturation patterns, consistent with their possible dependence on different generation and synchronization mechanisms. ConclusionsChanges in slow wave origin, synchronization, and propagation at the transition between childhood and adulthood are consistent with known modifications in cortico-cortical and subcortico-cortical brain connectivity. In this light, changes in slow-wave properties may provide a valuable yardstick to assess, track, and interpret physiological and pathological development.

Role of cerebellum in sleep-dependent memory processes.

The activities and role of the cerebellum in sleep have, until recently, been largely ignored by both the sleep and cerebellum fields. Human sleep studies often neglect the cerebellum because it is at a position in the skull that is inaccessible to EEG electrodes. Animal neurophysiology sleep studies have focussed mainly on the neocortex, thalamus and the hippocampus. However, recent neurophysiological studies have shown that not only does the cerebellum participate in the sleep cycle, but it may also be implicated in off-line memory consolidation. Here we review the literature on cerebellar activity during sleep and the role it plays in off-line motor learning, and introduce a hypothesis whereby the cerebellum continues to compute internal models during sleep that train the neocortex.

Electroencephalographic evaluation under standing sedation using sublingual detomidine hydrochloride in Egyptian Arabian foals for investigation of epilepsy.

A standardized protocol for electroencephalography (EEG) under standing sedation for the investigation of epilepsy in foals is needed. To evaluate a modified standardized EEG protocol under standing sedation using sublingual detomidine hydrochloride in Egyptian Arabian foals. Nineteen foals (controls, 9; juvenile idiopathic epilepsy [JIE], 10). Descriptive clinical study. Foals were classified as controls or epileptic based on history or witnessed seizures and neurological examination. Foals were sedated using sublingual detomidine hydrochloride at a dosage of 0.08 mg/kg to avoid stress associated with injectable sedation. Once foals appeared sedated with their heads low to the ground and with wide base stance (30 minutes), topical lidocaine hydrochloride was applied at the determined locations of EEG electrodes. Fifteen minutes were allowed for absorption and electrodes were placed, protected, and EEG recording performed. Level of sedation was considered excellent with no need of redosing. The EEG recording lasted from 27 to 51 minutes and provided interpretable data. Epileptic discharges (ED) were noted predominantly in the central-parietal region in 9 of 10 epileptic foals. Photic stimulation triggered ED in 7 of 10 epileptic foals and in none of the controls. Foals were not oversedated and recovered uneventfully. Sublingual detomidine hydrochloride is a safe, painless, simple, and effective method of sedation for EEG recording in foals. Sublingual sedation allowed the investigation of cerebral electrical activity during states of sleep and arousal, and during photic stimulation for the investigation of epilepsy in foals.

ICA’s bug: How ghost ICs emerge from effective rank deficiency caused by EEG electrode interpolation and incorrect re-referencing

Independent component analysis (ICA) has been widely used for electroencephalography (EEG) analyses. However, ICA performance relies on several crucial assumptions about the data. Here, we focus on the granularity of data rank, i.e., the number of linearly independent EEG channels. When the data are rank-full (i.e., all channels are independent), ICA produces as many independent components (ICs) as the number of input channels (rank-full decomposition). However, when the input data are rank-deficient, as is the case with bridged or interpolated electrodes, ICA produces the same number of ICs as the data rank (forced rank deficiency decomposition), introducing undesired ghost ICs and indicating a bug in ICA. We demonstrated that the ghost ICs have white noise properties, in both time and frequency domains, while maintaining surprisingly typical scalp topographies, and can therefore be easily missed by EEG researchers and affect findings in unknown ways. This problem occurs when the minimum eigenvalue λmin of the input data is smaller than a certain threshold, leading to matrix inversion failure as if the rank-deficient inversion was forced, even if the data rank is cleanly deficient by one. We defined this problem as the effective rank deficiency. Using sound file mixing simulations, we first demonstrated the effective rank deficiency problem and determined that the critical threshold for λmin is 10−7 in the given situation. Second, we used empirical EEG data to show how two preprocessing stages, re-referencing to average without including the initial reference and non-linear electrode interpolation, caused this forced rank deficiency problem. Finally, we showed that the effective rank deficiency problem can be solved by using the identified threshold (λmin = 10−7) and the correct re-referencing procedure described herein. The former ensures the achievement of effective rank-full decomposition by properly reducing the input data rank, and the latter allows avoidance of a widely practiced incorrect re-referencing approach. Based on the current literature, we discuss the ambiguous status of the initial reference electrode when re-referencing. We have made our data and code available to facilitate the implementation of our recommendations by the EEG community.

Aluminum Thin Film Nanostructure Traces in Pediatric EEG Net for MRI and CT Artifact Reduction.

Magnetic resonance imaging (MRI) and continuous electroencephalogram (EEG) monitoring are essential in the clinical management of neonatal seizures. EEG electrodes, however, can significantly degrade the image quality of both MRI and CT due to substantial metallic artifacts and distortions. Thus, we developed a novel thin film trace EEG net ("NeoNet") for improved MRI and CT image quality without compromising the EEG signal quality. The aluminum thin film traces were fabricated with an ultra-high-aspect ratio (up to 17,000:1, with dimensions 30 nm × 50.8 cm × 100 µm), resulting in a low density for reducing CT artifacts and a low conductivity for reducing MRI artifacts. We also used numerical simulation to investigate the effects of EEG nets on the B1 transmit field distortion in 3 T MRI. Specifically, the simulations predicted a 65% and 138% B1 transmit field distortion higher for the commercially available copper-based EEG net ("CuNet", with and without current limiting resistors, respectively) than with NeoNet. Additionally, two board-certified neuroradiologists, blinded to the presence or absence of NeoNet, compared the image quality of MRI images obtained in an adult and two children with and without the NeoNet device and found no significant difference in the degree of artifact or image distortion. Additionally, the use of NeoNet did not cause either: (i) CT scan artifacts or (ii) impact the quality of EEG recording. Finally, MRI safety testing confirmed a maximum temperature rise associated with the NeoNet device in a child head-phantom to be 0.84 °C after 30 min of high-power scanning, which is within the acceptance criteria for the temperature for 1 h of normal operating mode scanning as per the FDA guidelines. Therefore, the proposed NeoNet device has the potential to allow for concurrent EEG acquisition and MRI or CT scanning without significant image artifacts, facilitating clinical care and EEG/fMRI pediatric research.

Pinniped electroencephalography: Methodology and findings in California sea lions (Zalophus californianus).

This study was designed to identify abnormalities in the electroencephalograms (EEGs) recorded from stranded California sea lions (Zalophus californianus) with suspected domoic acid (DA) toxicosis. Recordings from animals presenting for non-neurological issues were also obtained to better understand the normal EEG (background activity and transient events) in this species, as, to date, studies have focused on examining natural sleep in pinnipeds. Most animals were sedated for electrode placement and EEG acquisition with some receiving antiepileptic medications or isoflurane during the procedure. A total of 103 recordings were read and scored from 0 (normal) to 3 (severely abnormal). Epileptiform discharges, consisting of spikes, sharp waves, slow waves, and/or spike waves, were present in all EEGs with scores of 1, 2, or 3. The distribution of these events over the scalp varied. While often generalized, others were lateralized over one hemisphere, bifrontal, bioccipital, and/or bitemporal, while some discharges were multifocal. Findings were different between sea lions and occasionally changed within the EEG on a given sea lion. No clinical seizures were observed during the recording but a few sea lions had findings consistent with electroencephalographic seizures. When available, supporting diagnostic results obtained from magnetic resonance imaging (MRI) and/or necropsy/histopathology were described, as well as the status of those sea lions that recovered and were released with satellite tags.

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring.

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.

BRAIN-COMPUTER INTERFACE FOR CONVERTING THOUGHTS TO SPEECH

A brain-computer interface (BCI) is a form of technology that develops a communication channel between a user and certain environmental objects using the user's brain impulses and to develop a non-invasive brain-computer interface that can "translate ideas into voice" (that converts brain signals into speech). The technique is primarily intended for individuals who can no longer interact via conventional channels (lock-in-syndrome patients). The brain signal produced for each word is recorded using EEG electrodes, and the ADC is used to convert the recorded brain signal to a voltage value. When the system produces the requisite voltage, the voice is delivered. Keywords: Brain, EEG electrodes, Thoughts, Speech, Non-Invasive, Signal processing, Word, ADC

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors.

The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.

Improving EEG Electrode Sensitivity with Graphene Nano Powder and Neural Network for Schizophrenia Diagnosis

Hallucinations and delusions are symptoms of schizophrenia. Due to persistent auditory and visual hallucinations, a person with schizophrenia cannot process reality clearly. Abnormal brain activity results from delusion and hallucination. During the capture of EEG signals, aberrant behavior is detected. The EEG electrodes do not well detect the brain's current distribution. Schizophrenia causes the EEG signal to be warped and less sensitive, which results in incorrect interpretation of brain activity. In this paper, an EEG electrode constructed of graphene nanopowder is suggested that is sensitive to the brain's weak electrical activity. The cold spray approach created graphene EEG electrodes, improving the material bonding and chemical characteristics. By obtaining EEG readings from schizophrenic patients, the sensitivity of the graphene electrode was assessed. The EEG signal was collected from the subject when taking part in cognitive tests like question sessions and numerical problems. Several neural networks (NN) algorithms can be used to identify hallucination and delusion aspects in EEG recordings. Further details regarding the hallucination and delusion aspects in the EEG signal were provided by the NN, showing a Graphene electrode. As compared to other NN models, the comparative study of several NN models revealed that the BFGS quasi-Newtonian backpropagation algorithm accurately recognized hallucination and delusion features.