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Mixed-reality (MR) environments, in which virtual objects are overlaid on the real environment and shared with peers by wearing a transparent optical head-mounted display, are considered to be well suited for collaborative work. However, no studies have been conducted to provide neuroscientific evidence of its effectiveness. In contrast, inter-brain synchronization has been repeatedly observed in cooperative tasks and can be used as an index of the quality of cooperation. In this study, we used electroencephalography (EEG) to simultaneously measure the brain activity of pairs of participants, a technique known as hyperscanning, during a cooperative motor task to investigate whether inter-brain synchronization would be also observed in a shared MR environment. The participants were presented with virtual building blocks to grasp and build up an object cooperatively with a partner or individually. We found that inter-brain synchronization in the cooperative condition was stronger than that in the individual condition (F(1, 15) = 4.70, p < 0.05). In addition, there was a significant correlation between task performance and inter-brain synchronization in the cooperative condition (rs = 0.523, p < 0.05). Therefore, the shared MR environment was sufficiently effective to evoke inter-brain synchronization, which reflects the quality of cooperation. This study offers a promising neuroscientific method to objectively measure the effectiveness of MR technology.

In motor imagery (MI) brain–computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient’s demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain’s force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time–frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time–frequency–space domain features to improve the algorithm’s sensitivity. The results showed that the accuracy of the algorithm was 86.4 ± 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 ± 14.5%), Deep ConvNet (75.3 ± 12.3%), Shallow ConvNet (77.6 ± 11.8%), EEGNet (82.3 ± 13.8%), and SCNN-BiLSTM (69.1 ± 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance.

Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique’s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.

In order to discriminate situation awareness (SA) levels on the basis of SA-sensitive electroencephalography (EEG) features, the high-SA (HSA) group and low-SA (LSA) groups, which are representative of two SA levels, were classified according to the situation awareness global assessment technology (SAGAT) scores measured in the multi-attribute task battery (MATB) II tasks. Furthermore, three types of EEG features, namely, absolute power, relative power, and slow-wave/fast-wave (SW/FW), were explored using spectral analysis. In addition, repeated analysis of variance (ANOVA) was conducted in three brain regions (frontal, central, and parietal) × three brain lateralities (left, middle, and right) × two SA groups (LSA and HSA) to explore SA-sensitive EEG features. The statistical results indicate a significant difference between the two SA groups according to SAGAT scores; moreover, no significant difference was found for the absolute power of four waves (delta (δ), theta (θ), alpha (α), and beta (β)). In addition, the LSA group had a significantly lower β relative power than the HSA group in central and partial regions. Lastly, compared with the HSA group, the LSA group had higher θ/β and (θ + α)/(α + β) in all analyzed brain regions, higher α/β in the parietal region, and higher (θ + α)/β in all analyzed regions except for the left and right laterality in the frontal region. The above SA-sensitive EEG features were fed into principal component analysis (PCA) and the Bayes method to discriminate different SA groups, and the accuracies were 83.3% for the original validation and 70.8% for the cross-validation. The results provide a basis for real-time assessment and discrimination of SA by investigating EEG features, thus contributing to monitoring SA decrement that might lead to threats to flight safety.

Functional connectivity related to familiarity has recently been investigated in the context of various stimuli (e.g., words, faces, pictures, music, and video). However, the directed functional connectivity patterns with different attention indexes as a response to familiar/unfamiliar stimuli remain unclear. In the current study, we employed the Directed Transfer Function (DTF) to estimate the information flow between brain areas. This method was reported to be practically robust to volume conduction. Furthermore, the hierarchical clustering approach was utilized to group subjects based on the attention index, i.e., the alpha/theta ratio of fronto-central (frontal to central and central to frontal) features. Three major findings were revealed from this study. First, all subjects had different attention indexes when they watched familiar/unfamiliar videos. Then, subjects were sorted into three groups: low index (LI), middle index (MI), and high index (HI). Second, a competition between two states (familiar/unfamiliar) showed that the information flows of familiar stimuli were greater than unfamiliar stimuli, which involved significant effects in the frontal, temporal, and parietal areas. Third, comparison between groups (LI/MI/HI) demonstrated that the frontal and central regions were the primary sources that distributed information flows to almost the whole brain, particularly during familiar conditions. This result indicates that these two regions may play an important role in attentional processing.

Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain. Method: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification. Results: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME). Conclusions: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.

Disability is a physical or mental impairment. People with disability have more barriers to do certain activity than those without disability. Moreover, several conditions make them having difficulty to communicate with other people. Currently, researchers have helped people with disabilities by developing brain-computer interface (BCI) technology, which uses artifact on electroencephalograph (EEG) as a communication tool using blinks. Research on eye blinks has only focused on the threshold and peak amplitude, while the difference in how many blinks can be detected using peak amplitude has not been the focus yet. This study used primary data taken using a Muse headband on 15 subjects. This data was used as a dataset classified using bagging (random forest) and boosting (XGBoost) methods with python; 80% of the data was allocated for learning and 20% was for testing. The classified data was divided into ten times of testing, which were then averaged. The number of eye blinks’ classification results showed that the accuracy value using random forest was 77.55%, and the accuracy result with the XGBoost method was 90.39%. The result suggests that the experimental model is successful and can be used as a reference for making applications that help people to communicate by differentiating the number of eye blinks. This research focused on developing the number of eye blinks. However, in this study, only three blinking were used so that further research could increase these number.

Background:Anorexia nervosa (AN) is a serious and life-threatening psychiatric condition. With a paucity of approved treatments, there is a desperate need for novel treatment avenues to be explored. Here, we present (1) an overview of the ways through which Public Patient Involvement (PPI) has informed a trial of psilocybin-assisted therapy for AN and (2) a protocol for a pilot study of psilocybin-assisted therapy in AN currently underway at Imperial College London. The study aims to assess the feasibility, brain mechanisms and preliminary outcomes of treating anorexia nervosa with psilocybin.Methods:(1) PPI: Across two online focus groups, eleven individuals with lived experience of AN were presented with an overview of the protocol. Their feedback not only identified solutions to possible barriers for future participants, but also helped the research team to better understand the concept of “recovery”from the perspective of those with lived experience. (2) Protocol: Twenty female participants [21–65 years old, body mass index (BMI) 15 kg/m2or above] will receive three oral doses of psilocybin (up to 25 mg) over a 6-week period delivered in a therapeutic environment and enveloped by psychological preparation and integration. We will work with participant support networks (care teams and an identified support person) throughout and there will be an extended remote follow-up period of 12 months. Our two-fold primary outcomes are (1) psychopathology (Eating Disorder Examination) across the 6-month follow-up and (2) readiness and motivation to engage in recovery (Readiness and Motivation Questionnaire) across the 6-week trial period. Neurophysiological outcome measures will be: (1) functional magnetic resonance imaging (fMRI) brain changes from baseline to 6-week endpoint and (2) post-acute changes in electroencephalography (EEG) activity, including an electrophysiological marker of neuronal plasticity.Discussion:The results of this pilot study will not only shed light on the acceptability, brain mechanisms, and impression of the potential efficacy of psilocybin as an adjunct treatment for AN but will be essential in shaping a subsequent Randomised Control Trial (RCT) that would test this treatment against a suitable control condition.Clinical Trial Registration:identifier: NCT04505189.

As human’s simple tasks are being increasingly replaced by autonomous systems and robots, it is likely that the responsibility of handling more complex tasks will be more often placed on human workers. Thus, situations in which workplace tasks change before human workers become proficient at those tasks will arise more frequently due to rapid changes in business trends. Based on this background, the importance of preventing human error will become increasingly crucial. Existing studies on human error reveal how task errors are related to heart rate variability (HRV) indexes and electroencephalograph (EEG) indexes. However, in terms of preventing human error, analysis on their relationship with conditions before human error occurs (i.e., the human pre-error state) is still insufficient. This study aims at identifying biological indexes potentially useful for the detection of high-risk psychological states. As a result of correlation analysis between the number of errors in a Stroop task and the multiple HRV and EEG indexes obtained before and during the task, significant correlations were obtained with respect to several biological indexes. Specifically, we confirmed that conditions before the task are important for predicting the human error risk in high-cognitive-load tasks while conditions both before and during tasks are important in low-cognitive-load tasks.

The movement-related cortical potential (MRCP) is a brain signal that can be recorded using surface electroencephalography (EEG) and represents the cortical processes involved in movement preparation. The MRCP has been widely researched in simple, single-joint movements, however, these movements often lack ecological validity. Ecological validity refers to the generalizability of the findings to real-world situations, such as neurological rehabilitation. This scoping review aimed to synthesize the research evidence investigating the MRCP in ecologically valid movement tasks. A search of six electronic databases identified 102 studies that investigated the MRCP during multi-joint movements; 59 of these studies investigated ecologically valid movement tasks and were included in the review. The included studies investigated 15 different movement tasks that were applicable to everyday situations, but these were largely carried out in healthy populations. The synthesized findings suggest that the recording and analysis of MRCP signals is possible in ecologically valid movements, however the characteristics of the signal appear to vary across different movement tasks (i.e., those with greater complexity, increased cognitive load, or a secondary motor task) and different populations (i.e., expert performers, people with Parkinson’s Disease, and older adults). The scarcity of research in clinical populations highlights the need for further research in people with neurological and age-related conditions to progress our understanding of the MRCPs characteristics and to determine its potential as a measure of neurological recovery and intervention efficacy. MRCP-based neuromodulatory interventions applied during ecologically valid movements were only represented in one study in this review as these have been largely delivered during simple joint movements. No studies were identified that used ecologically valid movements to control BCI-driven external devices; this may reflect the technical challenges associated with accurately classifying functional movements from MRCPs. Future research investigating MRCP-based interventions should use movement tasks that are functionally relevant to everyday situations. This will facilitate the application of this knowledge into the rehabilitation setting.

Attention is a condition when someone concentrates on a specific task while ignoring other perceivable information. Numerous methods of attention level detection such as observation, self-assessment, and objective performance have been applied especially in supervised machine learning. But those methods tend to be delayed, sporadic, not at the moment in time, and based on participant cognitive ability. This study proposed a new labeling method for attention level detection by using quantitative evaluation formula based on blink rates and pupillometry. Comparison in error detection between self-assessment, observation, and objective performance has been done in this study. After that, this study investigated the effect of attention level based on self-assessment toward blink rates and pupillometry. The result shown blink rates in low attention is higher than high attention. On the other hand, pupillometry in low attention is smaller than high attention. The effect of attention levels toward pupillometry and blink rates are extracted into several algorithms. The result from experimental procedure shown quantitative evaluation formula has percentage error less than 15% compared with self-assessment. Overall, these results demonstrated that the proposed method can be used to be data labeling for other physiological signals such as electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS). After that, this quantitative formula was applied to EEG-ECG-NIRS for attention level detection. Two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS has been used to detect attention level during tasks load. Our result has shown the accuracy system 82.31%.

The COMT Val158Met polymorphism affects the availability of synaptic dopamine in the prefrontal cortex and has been widely studied as a genetic risk factor for psychosis. Schizotypy is associated with an increased risk of psychosis, with some studies implicating similar neurobiological mechanisms to schizophrenia. The present study sought to interrogate the link between the COMT Val158Met polymorphism and schizotypy using electroencephalogram (EEG) to identify neurophysiological mechanisms underpinning psychosis risk. Neurotypical (N = 91) adults were genotyped for the COMT Val158Met polymorphism, completed the Schizotypal Personality Questionnaire (SPQ), and had eyes open resting-state EEG recorded for 4 min. SPQ suspiciousness subscale scores were higher for individuals homozygous for Val/Val and Met/Met versus Val/Met genotypes. Delta, theta, alpha-2, beta-1, and beta-2 amplitudes were lower for Val/Val than Met/Met individuals. Lower theta amplitudes were correlated with higher total SPQ scores (P = 0.050), and multiple regression revealed that higher delta, and lower theta and beta-2 amplitudes (but not COMT genotype) best predicted total SPQ scores (P = 0.014). This study demonstrates the importance of COMT genotype in determining trait suspiciousness and EEG oscillatory activity. It also highlights relationships between dopaminergic alterations, EEG and schizotypy that are dissimilar to those observed in schizophrenia.

Giving a robot the ability to perceive emotion in its environment can improve human-robot interaction (HRI), thereby facilitating more human-like communication. To achieve emotion recognition in different built environments for adolescents, we propose a multi-modal emotion intensity perception method using an integration of electroencephalography (EEG) and eye movement information. Specifically, we first develop a new stimulus video selection method based on computation of normalized arousal and valence scores according to subjective feedback from participants. Then, we establish a valence perception sub-model and an arousal sub-model by collecting and analyzing emotional EEG and eye movement signals, respectively. We employ this dual recognition method to perceive emotional intensities synchronously in two dimensions. In the laboratory environment, the best recognition accuracies of the modality fusion for the arousal and valence dimensions are 72.8 and 69.3%. The experimental results validate the feasibility of the proposed multi-modal emotion recognition method for environment emotion intensity perception. This promising tool not only achieves more accurate emotion perception for HRI systems but also provides an alternative approach to quantitatively assess environmental psychology.

Epilepsy is a common neurological disorder characterized by naturally-occurring spontaneous recurrent seizures and comorbidities. Kindling has long been used to model epileptogenic mechanisms and to assess antiepileptic drugs. In particular, extended kindling can induce spontaneous recurrent seizures without gross brain lesions, as seen clinically. To date, the development of spontaneous recurrent seizures following extended kindling, and the effect of the antiepileptic drugs on these seizures are not well understood. In the present study we aim to develop a mouse model of extended hippocampal kindling for the first time. Once established, we plan to evaluate the effect of three different antiepileptic drugs on the development of the extended-hippocampal-kindled-induced spontaneous recurrent seizures. Male C57 black mice were used for chronic hippocampal stimulations or handling manipulations (twice daily for up to 70 days). Subsequently, animals underwent continuous video/EEG monitoring for seizure detection. Spontaneous recurrent seizures were consistently observed in extended kindled mice but no seizures were detected in the control animals. The aforementioned seizures were generalized events characterized by hippocampal ictal discharges and concurrent motor seizures. Incidence and severity of the seizures was relatively stable while monitored over a few months after termination of the hippocampal stimulation. Three antiepileptic drugs with distinct action mechanisms were tested: phenytoin, lorazepam and levetiracetam. They were applied via intra-peritoneal injections at anticonvulsive doses and their effects on the spontaneous recurrent seizures were analyzed 10–12 h post-injection. Phenytoin (25 mg/kg) and levetiracetam (400 mg/kg) abolished the spontaneous recurrent seizures. Lorazepam (1.5 mg/kg) decreased motor seizure severity but did not reduce the incidence and duration of corresponding hippocampal discharges, implicating its inhibitory effects on seizure spread. No gross brain lesions were observed in a set of extended hippocampal kindled mice submitted to histological evaluation. All these data suggests that our model could be considered as a novel mouse model of extended hippocampal kindling. Some limitations remain to be considered.

In this paper, we propose a new method for analyzing the electromagnetic field generated by neural activity in the brain. The purpose of the development of the new method is to improve computational precision compared to the boundary element method (BEM) that is the well-known and widely used one in this field. The proposed method has some fundamental advantages of computation for complicated anatomical structure that is difficult or hard to implement by using the BEM. Although the method needs an approximation that has not been refined yet, simulation results show that the method is a promising one for applying to such complicated analysis.

This study examines the feasibility of using electroencephalograms (EEGs) to rapidly detect the intent to open one’s hand in individuals with complete hand paralysis following a subcortical ischemic stroke. If detectable, this motor planning activity could be used in real time to trigger a motorized hand exoskeleton or an electrical stimulation device that opens/closes the hand. While EEG-triggered movement-assist devices could restore function, they may also promote recovery by reinforcing the use of remaining cortical circuits. EEGs were recorded while participants were cued to either relax or attempt to extend their fingers. Linear discriminant analysis was used to detect onset of finger extension from the EEGs in a leave-one-trial-out cross-validation process. In each testing trial, the classifier was applied in pseudo real time starting from an initial hand-relaxed phase, through movement planning, and into the initial attempted finger extension phase (finger extension phase estimated from typical time-to-movement-onset measured in the unaffected hand). The classifiers detected attempted finger-extension at a significantly higher rate during both motor planning and early attempted execution compared to rest. To reduce inappropriate triggering of a movement-assist device during rest, the classification threshold could be adjusted to require more certainty about one’s intent to move before triggering a device. Additionally, a device could be set to activate only after multiple time samples in a row were classified as finger extension events. These options resulted in some sessions with no false triggers while the person was resting, but moderate-to-high true trigger rates during attempted movements.

Anticipation is a mental process during which a person actively engages in a phase required for the sensory perception and execution of the optimal actions at the arrival of the relevant future events. Since this process occurs before the execution of an intended action, it may be used as a control signal for Brain Computer Interface (BCI) applications. Recognition of neural correlates of this process can enhance the performance of a BCI and in turn reduce mental workload of its users. To this end, it is vital to understand the neural correlates involved in this process and to design robust methods for its recognition in single trials. The analysis of these correlates may also contribute to the basic knowledge of the mechanisms underlying this behavior. The thesis provides three major contributions: (i) it reports methods for the robust recognition of anticipation related Electroencephalogram (EEG) potentials (ii) it provides insights into the selection of appropriate preprocessing steps required for enhancing the Signal-to-Noise Ratio (SNR) of anticipatory slow cortical potentials (SCPs) and (iii) it identifies scalp area specific oscillatory activity related to different aspects of anticipatory behavior. First, we focus on methods for the single trial recognition of anticipatory SCPs using the widely known classical contingent negative variation (CNV) paradigm. Using this paradigm, we demonstrate the feasibility of recognizing the anticipatory SCPs (CNV potentials) using features thatmodel its temporal pattern. We propose a Bayesian approach that exploits temporal evolution and redundancy to quickly classify (e.g., within half of the anticipatory period), without compromising classification accuracy. We then improve upon these recognition rates by using a source localization technique based on the biophysical model of the human head. We further validate the feasibility of recognizing CNV potentials in an online experiment, and report for the first time that, under controlled conditions, these potentials can be reproduced and recognized in realistic interaction scenarios (assistive technology web-browsing) with high accuracies. Second, the thesis provides insights into the selection of appropriate preprocessing stages required for improving the SNR of SCPs. The CNV potentials are characterized by low frequencies that are usually recorded with full-DC, and hence suffer from task-irrelevant high amplitude fluctuations and spatial noise. To account for this, we identified appropriate spectral and spatial filters to improve the SNR. We demonstrate the potential of these preprocessing stages by using fusing multiple electrode specific linear classifiers, which achieve recognition performances of 90±2% (area under curve of receiver operating characteristic), where the classifiers are trained using recordings from one day and tested on the recordings from several days apart. Finally, the thesis identifies different facets of anticipatory behavior. Apart from the widely known CNV potentials, it is not clear which other spectral bands could be related to anticipatory behavior. Using recordings from an experiment (i.e., the assistive technology web browser) where multiple warning stimuli predicted an imperative stimulus, we explored the phase and amplitude response of various oscillatory sub-bands for the identification of markers that could be associated with different aspects of anticipation. From this study we report that: (i) there are duration (4-10 seconds) specific changes in Electroencephalography (EEG) activity in the range 0-1 Hz in the central electrodes correlate with reaction time, (ii) there exist slow oscillations (0.1-1 Hz) in the central electrodes that exhibit phase tuning up to 4 seconds before the onset of a target cue, (iii) there are delta oscillations (1.5-3 Hz), which are entrained to predictive rhythmic warning cues, (iv) there is a selective modulation (increase or decrease) in the amplitude of occipital alpha band (8-12 Hz) based on the relevance of forthcoming visual cue, and (v) there exist a reduction in the beta band (14-30 Hz) amplitude in the sensory motor and association areas lasting up to 10 seconds. The phase tuning and entrainment resulted in a low variance of phase values at the arrival of the imperative stimulus, which may be required for its optimal processing. The amplitude modulation of alpha band activity is likely to be a resultant of sensory suppression and attention. The reduction of beta-band activity over long periods of time suggests holding of sensory-motor association areas until the execution of a planned action. We believe that these observations are the consequence of the endogenous drive on the ongoing oscillations to enhance the processing of the forthcoming stimuli and preparation for an intended action. In summary, the thesis provides methods for the recognition of anticipatory SCPs by exploiting spectral and spatio-temporal characteristics with high performances. By exploring various other oscillatory sub-bands, the spectral characteristics of different aspects of anticipatory behavior are also identified. Further, methods modeling these characteristics can bring forth more robust and faster techniques for BCI systems.

The purpose of this study was the development of a real-time filtering procedure of MRI artifacts in order to monitor the EEG activity during continuous EEG/fMRI acquisition. The development of a combined EEG and fMRI technique has increased in the past few years. Preliminary "spike-triggered" applications have been possible because in this method, EEG knowledge was only necessary to identify a trigger signal to start a delayed fMRI acquisition. In this way, the two methods were used together but in an interleaved manner. In real simultaneous applications, like event-related fMRI study, artifacts induced by MRI events on EEG traces represent a substantial obstacle for a right analysis. Up until now, the methods proposed to solve this problem are mainly based on procedures to remove post-processing artifacts without the possibility to control electrophysiological behavior of the patient during fMRI scan. Moreover, these methods are not characterized by a strong "prior knowledge" of the artifact, which is an imperative condition to avoid any loss of information on the physiological signals recovered after filtering. In this work, we present a new method to perform simultaneous EEG/fMRI study with real-time artifacts filtering characterized by a procedure based on a preliminary analytical study of EPI sequence parameters-related EEG-artifact shapes. Standard EEG equipment was modified in order to work properly during ultra-fast MRI acquisitions. Changes included: high-performance acquisition device; electrodes/cap/wires/cables materials and geometric design; shielding box for EEG signal receiver; optical fiber link; and software. The effects of the RF pulse and time-varying magnetic fields were minimized by using a correct head cap wires-locked environment montage and then removed during EEG/fMRI acquisition with a subtraction algorithm that takes in account the most significant EPI sequence parameters. The on-line method also allows a further post-processing utilization.