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Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG.

Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG–fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI–EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI–EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation in the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI−EEG-nf. Our proof-of-concept results demonstrate the feasibility of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activities of the human brain. They suggest potential applications of rtfMRI–EEG-nf in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression. •We report the first implementation of simultaneous rtfMRI and EEG neurofeedback.•A novel integration of simultaneous rtfMRI and EEG data streams is described.•Subjects can self-regulate their left amygdala fMRI activation and frontal EEG asymmetry.•Hemodynamic and electrophysiological processes can be regulated simultaneously.•rtfMRI–EEG neurofeedback holds promise for improved treatment of mental disorders.

(2013) Neural Processes Underlying the“Same”-“Different” Judgment of Two Simultaneously Presented Objects- An EEG Study. The correct abbreviation of the third author's name in the Author Contributions statement is: DR. Citation: Zhang R, Hu Z, Roberson D, Zhang L, Li H, Liu Q (2014) Correction: Neural Processes Underlying the“Same”-“Different” Judgment of Two Simultaneously Presented Objects- An EEG Study.

This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.

Converging evidence suggests that human cognition and behavior emerge from functional brain networks interacting on local and global scales. We investigated two information-theoretic measures of functional brain segregation and integration-interaction complexity C (X), and integration I(X)-as applied to electroencephalographic (EEG) signals and how these measures are affected by choice of EEG reference. C (X) is a statistical measure of the system entropy accounted for by interactions among its elements, whereas I(X) indexes the overall deviation from statistical independence of the individual elements of a system. We recorded 72 channels of scalp EEG from human participants who sat in a wakeful resting state (interleaved counterbalanced eyes-open and eyes-closed blocks). C (X) and I(X) of the EEG signals were computed using four different EEG references: linked-mastoids (LM) reference, average (AVG) reference, a Laplacian (LAP) \"reference-free\" transformation, and an infinity (INF) reference estimated via the Reference Electrode Standardization Technique (REST). Fourier-based power spectral density (PSD), a standard measure of resting state activity, was computed for comparison and as a check of data integrity and quality. We also performed dipole source modeling in order to assess the accuracy of neural source C (X) and I(X) estimates obtained from scalp-level EEG signals. C (X) was largest for the LAP transformation, smallest for the LM reference, and at intermediate values for the AVG and INF references. I(X) was smallest for the LAP transformation, largest for the LM reference, and at intermediate values for the AVG and INF references. Furthermore, across all references, C (X) and I(X) reliably distinguished between resting-state conditions (larger values for eyes-open vs. eyes-closed). These findings occurred in the context of the overall expected pattern of resting state PSD. Dipole modeling showed that simulated scalp EEG-level C (X) and I(X) reflected changes in underlying neural source dependencies, but only for higher levels of integration and with highest accuracy for the LAP transformation. Our observations suggest that the Laplacian-transformation should be preferred for the computation of scalp-level C (X) and I(X) due to its positive impact on EEG signal quality and statistics, reduction of volume-conduction, and the higher accuracy this provides when estimating scalp-level EEG complexity and integration.

Since the replication crisis, standardization has become even more important in psychological science and neuroscience. As a result, many methods are being reconsidered, and researchers' degrees of freedom in these methods are being discussed as a potential source of inconsistencies across studies. With the aim of addressing these subjectivity issues, we have been working on a tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig. Two scripts are presented and explained step-by-step to perform basic, informed ERP and frequency-domain analyses, including data export to statistical programs and visual representations of the data. The open-source software EEGlab in MATLAB is used as the data handling platform, but scripts based on code provided by Mike Cohen (2014) are also included. This accompanying tutorial-like article explains and shows how the processing of our automated pipeline affects the data and addresses, especially beginners in EEG-analysis, as other (pre)-processing chains are mostly targeting rather informed users in specialized areas or only parts of a complete procedure. In this context, we compared our pipeline with a selection of existing approaches. The need for standardization and replication is evident, yet it is equally important to control the plausibility of the suggested solution by data exploration. Here, we provide the community with a tool to enhance the understanding and capability of EEG-analysis. We aim to contribute to comprehensive and reliable analyses for neuro-scientific research.

A very exhaustive clinical history was applied to the subjects, which accounted for a big number of possible medical conditions of family history of risk factors. [...]subjects with EEG deviations like the absence of the Alpha peak, which is considered normal if not accompanied by other factors, were kept in the sample. [...]this a sample which is representative of the normal population, and not a sample of the “supernormal” population. For stationary data it is well-known that the cross spectral matrices retain all the statistical properties of the original signals. Since the signals were assumed to be stationary, and the cross-spectral matrices were obtained by averaging 24 epochs of artifact free EEG activity, it is guaranteed that the FFT complex coefficients have an approximate complex Gaussian distribution at each frequency, and also they are independent between frequencies (Brillinger, 1974, 2001). [...]211 healthy subjects (105 males, 106 females) were selected to create the first Cuban Normative database.

Emotion regulation plays a key role in human behavior and overall well-being. Neurofeedback is a non-invasive self-brain training technique used for emotion regulation to enhance brain function and treatment of mental disorders through behavioral changes. Previous neurofeedback research often focused on using activity from a single brain region as measured by fMRI or power from one or two EEG electrodes. In a new study, we employed connectivity-based EEG neurofeedback through recalling positive autobiographical memories and simultaneous fMRI to upregulate positive emotion. In our novel approach, the feedback was determined by the coherence of EEG electrodes rather than the power of one or two electrodes. We compared the efficiency of this connectivity-based neurofeedback to traditional activity-based neurofeedback through multiple experiments. The results showed that connectivity-based neurofeedback effectively improved BOLD signal change and connectivity in key emotion regulation regions such as the amygdala, thalamus, and insula, and increased EEG frontal asymmetry, which is a biomarker for emotion regulation and treatment of mental disorders such as PTSD, anxiety, and depression and coherence among EEG channels. The psychometric evaluations conducted both before and after the neurofeedback experiments revealed that participants demonstrated improvements in enhancing positive emotions and reducing negative emotions when utilizing connectivity-based neurofeedback, as compared to traditional activity-based and sham neurofeedback approaches. These findings suggest that connectivity-based neurofeedback may be a superior method for regulating emotions and could be a useful alternative therapy for mental disorders, providing individuals with greater control over their brain and mental functions.

The integration of transcranial magnetic stimulation (TMS) with electroencephalography (EEG) has markedly enhanced our ability to probe cortical excitability and monitor the brain’s electrophysiological responses to external perturbations. In recent decades, this combination has become a widely used and important tool in both basic neuroscience and clinical research. However, persistent challenges remain, particularly the limited reliability of early TMS-evoked potentials (TEPs), contamination from stimulus-locked and induced artifacts (e.g., coil discharge, electrode polarization, cranial muscle activity), and reliance on non-individualized stimulation protocols. This review outlines the evolution of the TMS-EEG methodology in four key implementations: (i) EEG-blind TMS, where stimulation parameters are fixed without EEG-based adjustments; (ii) EEG-informed TMS, which leverages online EEG readouts to optimize stimulation settings prior to acquisition; (iii) EEG-triggered TMS, employing feedforward algorithms to align stimulation with ongoing neural oscillations; and (iv) closed loop TMS, where real-time feedback dynamically adapts stimulation parameters during the session. We examine the electrophysiological and technical foundations of each approach, highlighting their benefits and limitations. Emerging closed-loop systems represent a shift toward adaptive, data-driven neuromodulation, unlocking promising avenues for personalized brain stimulation. Further refinement of these approaches will be critical to improving their precision, reliability, and applicability in diverse clinical and research settings. Collectively, these developments demonstrate a field-wide progression toward increasingly precise and individualized brain stimulation strategies, enabled by real-time electrophysiological feedback and customizable stimulation protocols. •EEG-blind TMS: Operator agnostic to TMS effects on EEG, with fixed dose not tuned to EEG outcomes.•EEG-informed TMS: EEG readout guides manual tuning of TMS parameters for optimal stimulation dose.•EEG-triggered TMS: Feed-forward models decode EEG rhythms to predict and sync TMS with brain states.•Closed-loop TMS: Multi-input, multi-output platforms adapt TMS via EEG/EMG feedback in real time.