Non-invasive EEG Research Articles

Bio-Inspired Algorithms for Optimal Feature Selection in Motor Imagery-Based Brain-Computer Interface.

Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.

Frontal EEG Changes with the Recovery of Carotid Blood Flow in a Cardiac Arrest Swine Model.

Monitoring cerebral circulation during cardiopulmonary resuscitation (CPR) is essential to improve patients’ prognosis and quality of life. We assessed the feasibility of non-invasive electroencephalography (EEG) parameters as predictive factors of cerebral resuscitation in a ventricular fibrillation (VF) swine model. After 1 min untreated VF, four cycles of basic life support were performed and the first defibrillation was administered. Sustained return of spontaneous circulation (ROSC) was confirmed if a palpable pulse persisted for 20 min. Otherwise, one cycle of advanced cardiovascular life support (ACLS) and defibrillation were administered immediately. Successfully defibrillated animals were continuously monitored. If sustained ROSC was not achieved, another cycle of ACLS was administered. Non-ROSC was confirmed when sustained ROSC did not occur after 10 ACLS cycles. EEG and hemodynamic parameters were measured during experiments. Data measured for approximately 3 s right before the defibrillation attempts were analyzed to investigate the relationship between the recovery of carotid blood flow (CBF) and non-invasive EEG parameters, including time- and frequency-domain parameters and entropy indices. We found that time-domain magnitude and entropy measures of EEG correlated with the change of CBF. Further studies are warranted to evaluate these EEG parameters as potential markers of cerebral circulation during CPR.

Congruence and Discrepancy of Interictal and Ictal EEG With MRI Lesions in Pediatric Epilepsies.

Purpose. To evaluate the congruence or discrepancy of the localization of magnetic resonance imaging (MRI) lesions with interictal epileptiform discharges (IEDs) or epileptic seizure patterns (ESPs) in surface EEG in lesional pediatric epilepsy patients. Methods. We retrospectively analyzed presurgical MRI and video-EEG monitoring findings of patients up to age 18 years. Localization of MRI lesions were compared with ictal and interictal noninvasive EEG findings of patients with frontal, temporal, parietal, or occipital lesions. Results. A total of 71 patients were included. Localization of ESPs showed better congruence with MRI in patients with frontal lesions (n = 21, 77.5%) than in patients with temporal lesions (n = 24; 40.7%) (P = .009). No significant IED distribution differences between MRI localizations could be found. Conclusions. MRI lesions and EEG findings are rarely fully congruent. Congruence of MRI lesions and ESPs was highest in children with frontal lesions. This is in contrast to adults, in whom temporal lesions showed the highest congruency with the EEG localization of ESP. Lesional pediatric patients should be acknowledged as surgical candidates despite incongruent findings of interictal and ictal surface EEG.

Surgical disconnection of epilepsy network correlates with improved outcomes

PurposeA novel software algorithm combining non-invasive EEG and resting state functional MRI data to map networks of cortex correlated to epileptogenic tissue was used to map an epilepsy network non-invasively. The relationship between epilepsy network connectivity and outcomes after surgery was investigated using this non-invasive and non-concurrent modeling algorithm. MethodScalp EEG and resting state functional MRI were acquired for nineteen patients with temporal lobe epilepsy. The hypothetical irritative zone was mapped, and resting state functional MRI data was used to model regions functionally correlated with the irritative zone. Epilepsy network connectivity was measured in patient with temporal lobe epilepsy (n = 19) both pre- and post-operatively. Temporal networks were also mapped in healthy control participants (n = 6). ResultsThirteen of nineteen patients (68 %) were seizure free after 20.3 ± 4.8 months. Epilepsy network connectivity within the temporal lobe was significantly higher among patients with temporal lobe epilepsy compared to the healthy control patients (p < 0.05). Disconnection of the epilepsy network was significantly higher in patients who were seizure free. Using spearman rho analyses, neuropsychological function after surgery was found to be relatively better in patients with higher degree of epilepsy network disconnection. ConclusionsThe magnitude of network disconnection after surgery was strongly associated with increased rates of seizure freedom and relatively better neuropsychological measures of memory and naming function. It was shown that seizure-free outcomes and relatively improved neuropsychological function correlated with surgical disconnection of a highly synchronous epilepsy network.

Applicable features of electroencephalogram for ADHD diagnosis

Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental and psychiatric disorder, which affects 11% of children around the world. Several linear and nonlinear biomarkers from electroencephalogram (EEG) signals have been proposed for diagnosis of ADHD to date. However, the determination of which type of analysis gives us the best feature and biomarker to diagnose ADHD is still controversial. In this study, we aimed to evaluate and compare several categories of features, extracted from EEG signals, for diagnosis of ADHD. Thirty 7–12-year-old children fulfilling the DSM5 criteria for ADHD and thirty healthy children underwent a noninvasive EEG evaluation at resting-state. After preprocessing, five categories of features including morphological, time, frequency, time-frequency, and nonlinear features were extracted from EEGs. The efficacy of each feature category in ADHD diagnosis was determined using statistical analysis, receiver operating characteristic (ROC) curves, and evidential K-nearest neighbor (EKNN) classifier. Statistical analyses showed that 13.15, 13.68, 14.47, 14.03, and 34.73% of extracted features were significant (p < 0.05) in morphological, time, frequency, time-frequency, and nonlinear domains, respectively. The largest AUC values for the five morphological, time, frequency, time-frequency, and nonlinear feature categories, were 0.870, 0.796, 0.824, 0.806, and 0.899, respectively. We obtained the accuracies of 77.43% using morphological features, 74.09% using time features, 80.44% using frequency features, 78.50% using time-frequency features, and 86.40% using nonlinear features. Our results showed that EEG nonlinear analysis is a good quantitative tool to detect the abnormalities of the electrical activity of the brain in ADHD. This result was expected due to the complexity of the brain and the nonlinear nature of the EEG signal. Therefore, better outcomes may be expected in early diagnosis of diseases, especially psychiatric disorders, by increasing the use of nonlinear methods.

Computational analysis of EEG activity during stance and swing gait phases

Abstract Analysis of cortical electroencephalography (EEG) patterns may suggest novel neural dynamics related to healthy and diseased gait which could be critical for assessing neurological disorders. However, the dynamics of cortical involvement in walking is not clearly understood. In this study, using non-invasive EEG, we recorded and analyzed the stance and swing gait cycle phases in healthy volunteering subjects. Extracted spectral and temporal features of gait data for right toe-off and heel strike were ranked based, using machine learning algorithms to identify patterns related to swing and stance. Increased beta rhythms, positive and negative motor potentials for stance and swing could be targeted as biosignatures discriminating gait cycle phases. Identifying such biosignatures help classify stance and swing phases and may be pertinent to preclinical studies and in resource-limited environments where expensive equipment may not be accessible.

Exploring the Correlation Between Attention and Cognitive Load Through Association Rule Mining by Using a Brainwave Sensing Headband

In recent years, Internet of Things (IoT) technology has brought many applications and developments for wearable devices, and the use of non-invasive electroencephalography (EEG) instruments to measure attention has been a topic of discussion. However, the correlation between attention and cognitive load has rarely been analyzed by data mining. For this reason, this study used head-mounted non-invasive EEG instruments based on IoT technology to collect attention values related to two courses and extracurricular activities and used a cognitive load questionnaire to investigate the cognitive loads of subjects. Correlation analysis was carried out through data mining technology to find the correlation between attention and cognitive load. In addition, six short-term experiments and relaxation experiments were designed to measure the subjects’ maximum attention and minimum attention values, so as to propose a strategy for setting the attention baseline. According to the results of the various experiments, subjects suffering from overload showed a state of inattention during the whole activity while subjects suffering a high load showed low sustained attention; only subjects with a medium load showed high sustained attention. Subjects with a low load showed inattention for nearly the entire activity. In this study, a strategy for setting an attention baseline was proposed to normalize the attention values from different EEG instruments. The correlation between attention value and cognitive load is analyzed using association rule mining technology so that the change of cognitive load could be effectively estimated by measuring the attention value instead of using questionnaire in the future.

Possible Relationship between Long Term Use of Cell Phones and Tumors

Cell (or cellular or mobile) phones first became widely available in the United States in the 1990s, but their use has increased dramatically since then. Cell phone use is nowadays an unremarkable part of everyday activities. Cell phones use radio-waves to carry information between callers. The frequency of these waves ranges from about 850-1900 megahertz (MHz). The higher the frequencies, the greater the energy carried by the waves. Human brain cells communicate via electrical impulses, which can be detected by non-invasive EEG (electroencephalogram) measurements. Positron Emission Tomography (PET) imaging is another tool used to study the effect of cell phone radiation on human brain activity.

Possible Relationship between Long Term Use of Cell Phones and Tumors

Cell (or cellular or mobile) phones first became widely available in the United States in the 1990s, but their use has increased dramatically since then. Cell phone use is nowadays an unremarkable part of everyday activities. Cell phones use radio-waves to carry information between callers. The frequency of these waves ranges from about 850-1900 megahertz (MHz). The higher the frequencies, the greater the energy carried by the waves. Human brain cells communicate via electrical impulses, which can be detected by non-invasive EEG (electroencephalogram) measurements. Positron Emission Tomography (PET) imaging is another tool used to study the effect of cell phone radiation on human brain activity.

Recurrence plot structures reflect motor-related EEG pattern

Detection and classification of motor-related brain patterns from non-invasive electroencephalograms (EEGs) is challenging due to their non-stationarity and low signal-to-noise ratio and requires using advanced mathematical approaches. Traditionally applied methods such as time-frequency analysis and spatial filtering allow to quantify the main attribute of the motor-related brain activity – contralateral desynchronization of mu-band oscillations (8-13 Hz) in sensorimotor cortex – by measuring EEG signal’s amplitude, power spectral density, location etc. However, these features suffer from strong inter- and intra-subject variability. So, special attention is paid to the finding of stable features. In present paper, we investigate application of the recurrence plots – robust mathematical tool for nonstationary data analysis – to explore properties of motor-related EEG samples. Our goal is to show that recurrence plots are sensitive to the changes in brain activity accessed from noninvasive EEG recordings and may provide us a new context for interpretation of motor-related pattern in EEG.

Sleep-related epilepsy in daily practice at clinic of neurology-10 years experience of the non-invasive video EEG laboratory recording

Sleep-related epilepsy in daily practice at clinic of neurology-10 years experience of the non-invasive video EEG laboratory recording

Quantitative analysis of intracranial electrocorticography signals using the concept of statistical parametric mapping

Statistical parametric mapping (SPM) is a technique with which one can delineate brain activity statistically deviated from the normative mean, and has been commonly employed in noninvasive neuroimaging and EEG studies. Using the concept of SPM, we developed a novel technique for quantification of the statistical deviation of an intracranial electrocorticography (ECoG) measure from the nonepileptic mean. We validated this technique using data previously collected from 123 patients with drug-resistant epilepsy who underwent resective epilepsy surgery. We determined how the measurement of statistical deviation of modulation index (MI) from the non-epileptic mean (rated by z-score) improved the performance of seizure outcome classification model solely based on conventional clinical, seizure onset zone (SOZ), and neuroimaging variables. Here, MI is a summary measure quantifying the strength of in-situ coupling between high-frequency activity at >150 Hz and slow wave at 3–4 Hz. We initially generated a normative MI atlas showing the mean and standard deviation of slow-wave sleep MI of neighboring non-epileptic channels of 47 patients, whose ECoG sampling involved all four lobes. We then calculated ‘MI z-score’ at each electrode site. SOZ had a greater ‘MI z-score’ compared to non-SOZ in the remaining 76 patients. Subsequent multivariate logistic regression analysis and receiver operating characteristic analysis to the combined data of all patients revealed that the full regression model incorporating all predictor variables, including SOZ and ‘MI z-score’, best classified the seizure outcome with sensitivity/specificity of 0.86/0.76. The model excluding ‘MI z-score’ worsened its sensitivity/specificity to 0.86/0.48. Furthermore, the leave-one-out analysis successfully cross-validated the full regression model. Measurement of statistical deviation of MI from the non-epileptic mean on invasive recording is technically feasible. Our analytical technique can be used to evaluate the utility of ECoG biomarkers in epilepsy presurgical evaluation.

Source localization of high-frequency activity in tripolar electroencephalography of patients with epilepsy

ObjectiveThe objective of the study was to localize sources of interictal high-frequency activity (HFA), from tripolar electroencephalography (tEEG), in patient-specific, realistic head models. MethodsConcurrent electroencephalogram (EEG) and tEEG were recorded from nine patients undergoing video-EEG, of which eight had seizures during the recordings and the other had epileptic activity. Patient-specific, realistic boundary element head models were generated from the patient's magnetic resonance images (MRIs). Forward and inverse modeling was performed to localize the HFA to cortical surfaces. ResultsIn the present study, performed on nine patients with epilepsy, HFA observed in the tEEG was localized to the surface of subject-specific, realistic, cortical models, and found to occur almost exclusively in the seizure onset zone (SOZ)/irritative zone (IZ). SignificanceHigh-frequency oscillations (HFOs) have been studied as precise biomarkers of the SOZ in epilepsy and have resulted in good therapeutic effect in surgical candidates. Knowing where the sources of these highly focal events are located in the brain can help with diagnosis. High-frequency oscillations are not commonly observed in noninvasive EEG recordings, and invasive electrocorticography (ECoG) is usually required to detect them. However, tEEG, i.e., EEG recorded on the scalp with tripolar concentric ring electrodes (TCREs), has been found to detect narrowband HFA from high gamma (approximately 80 Hz) to almost 400 Hz that correlates with SOZ diagnosis. Thus, source localization of HFA in tEEG may help clinicians identify brain regions of the epileptic zone. At the least, the tEEG HFA localization may help determine where to perform intracranial recordings used for precise diagnosis.

EEG Markers of Cognitive Engagement

ObjectiveThe purpose of this study was to use portable electroencephalography and qualitative assessments to characterize cognitive changes associated with perceived increase in mental load and to identify markers of mental fatigue in these individuals.BackgroundThe ability to focus on cognitive tasks impacts everything from our social interactions to our success in the classroom or workplace. Concussion negatively impacts the ability to focus and causes patients to experience signs of mental fatigue more quickly than those without concussion. The mechanisms behind these changes are still not well understood.Design/MethodsFifteen concussion patients and fifteen age-matched controls were recruited to participate in this study. Participants performed two, thirty-minute testing sessions spaced 1 month apart. In each session, participants performed 8 cognitive tasks eliciting varying levels of cognitive activity. Cognitive activity was quantitatively assessed using a MUSE non-invasive EEG headset. These data were compared to a perceived level of cognitive activity determined by the individual using the Klepsh et al (2017) cognitive engagement scale and mental fatigue assessed by the Mental Fatigue Scale (Johansson2014).ResultsThe results demonstrate that frequency based EEG changes correlated with decreased ability to focus on the cognitive task and with perceived cognitive fatigue in both concussion patients and healthy controls.ConclusionsFuture studies should utilize the same methods to monitor cognitive activity differences during daily functional living.

Electroencephalography in epilepsy: look for what could be beyond the visual inspection.

Since its starting point in 1929, human scalp electroencephalography (EEG) has been routinely interpreted by visual inspection of waveforms using the assumption that the activity at a given electrode is a representation of the activity of the cerebral cortex under it, but such a method has some limitations. In this review, we will discuss three advanced methods to obtain valuable information from scalp EEG in epilepsy using innovative technologies. Authors who had previous publications in the field provided a narrative review. Spike voltage topography of interictal spikes is a potential way to improve non-invasive EEG localization in focal epilepsies. Electrical source imaging is also a complementary technique in localization of the epileptogenic zone in patients who are candidates for epilepsy surgery. Quantitative EEG simplifies the large amount of information in continuous EEG by providing a static graphical display. Scalp electroencephalography has the potential to offer more spatial and temporal information than the traditional way of visual inspection alone in patients with epilepsy. Fortunately, with the help of modern digital EEG equipment and computer-assisted analysis, this information is more accessible.

Histogram of Gradient Orientations of Signal Plots Applied to P300 Detection.

The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects.

The Advantage of Low-Delta Electroencephalogram Phase Feature for Reconstructing the Center-Out Reaching Hand Movements

It is an emerging frontier of research on the use of neural signals for prosthesis control, in order to restore lost function to amputees and patients after spinal cord injury. Compared to the invasive neural signal based brain-machine interface (BMI), a non-invasive alternative, i.e., the electroencephalogram (EEG)-based BMI would be more widely accepted by the patients above. Ideally, a real-time continuous neuroprosthestic control is required for practical applications. However, conventional EEG-based BMIs mainly deal with the discrete brain activity classification. Until recently, the literature has reported several attempts for achieving the real-time continuous control by reconstructing the continuous movement parameters (e.g., speed, position, etc.) from the EEG recordings, and the low-frequency band EEG is consistently reported to encode the continuous motor control information. Previous studies with executed movement tasks have extensively relied on the amplitude representation of such slow oscillations of EEG signals for building models to decode kinematic parameters. Inspired by the recent successes of instantaneous phase of low-frequency invasive brain signals in the motor control and sensory processing domains, this study examines the extension of such a slow-oscillation phase representation to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time. The data for analysis are collected on five healthy subjects performing 2D hand center-out reaching along four directions in two sessions. On representative channels over the cortices encoding the execution information of reaching movements, we show that the low-delta EEG phase representation is characterized by higher signal-to-noise ratio and stronger modulation by the movement tasks, compared to the low-delta EEG amplitude representation. Furthermore, we have tested the low-delta EEG phase representation with two commonly used linear decoding models. The results demonstrate that the low-delta EEG phase based decoders lead to superior performance for 2D executed movement reconstruction to its amplitude based counterparts, as well as the other-frequency band amplitude and power based features. Thus, our study contributes to improve the movement reconstruction from EEG by introducing a new feature set based on the low-delta EEG phase patterns, and demonstrates its potential for continuous fine motion control of neuroprostheses.

Low-frequency cortical responses to natural speech reflect probabilistic phonotactics

Humans comprehend speech despite the various challenges such as mispronunciation and noisy environments. Our auditory system is robust to these thanks to the integration of the sensory input with prior knowledge and expectations built on language-specific regularities. One such regularity regards the permissible phoneme sequences, which determine the likelihood that a word belongs to a given language (phonotactic probability; “blick” is more likely to be an English word than “bnick”). Previous research demonstrated that violations of these rules modulate brain-evoked responses. However, several fundamental questions remain unresolved, especially regarding the neural encoding and integration strategy of phonotactics in naturalistic conditions, when there are no (or few) violations. Here, we used linear modelling to assess the influence of phonotactic probabilities on the brain responses to narrative speech measured with non-invasive EEG. We found that the relationship between continuous speech and EEG responses is best described when the stimulus descriptor includes phonotactic probabilities. This indicates that low-frequency cortical signals (<9 Hz) reflect the integration of phonotactic information during natural speech perception, providing us with a measure of phonotactic processing at the individual subject-level. Furthermore, phonotactics-related signals showed the strongest speech-EEG interactions at latencies of 100–500 ms, supporting a pre-lexical role of phonotactic information.

Filtering techniques for channel selection in motor imagery EEG applications: a survey

Brain computer interface (BCI) systems are used in a wide range of applications such as communication, neuro-prosthetic and environmental control for disabled persons using robots and manipulators. A typical BCI system uses different types of inputs; however, Electroencephalography (EEG) signals are most widely used due to their non-invasive EEG electrodes, portability, and cost efficiency. The signals generated by the brain while performing or imagining a motor related task [motor imagery (MI)] signals are one of the important inputs for BCI applications. EEG data is usually recorded from more than 100 locations across the brain, so efficient channel selection algorithms are of great importance to identify optimal channels related to a particular application. The main purpose of applying channel selection is to reduce computational complexity while analysing EEG signals, improve classification accuracy by reducing over-fitting, and decrease setup time. Different channel selection evaluation algorithms such as filtering, wrapper, and hybrid methods have been used for extracting optimal channel subsets by using predefined criteria. After extensively reviewing the literature in the field of EEG channel selection, we can conclude that channel selection algorithms provide a possibility to work with fewer channels without affecting the classification accuracy. In some cases, channel selection increases the system performance by removing the noisy channels. The research in the literature shows that the same performance can be achieved using a smaller channel set, with 10–30 channels in most cases. In this paper, we present a survey of recent development in filtering channel selection techniques along with their feature extraction and classification methods for MI-based EEG applications.

Use of human invasive SEEG and non-invasive EEG recordingsin vivo towards tDCS dose individualization

Transcranial direct current stimulation (tDCS) is a type of non-invasive neuromodulatory rehabilitative therapy that has been the focus of many recent studies in a variety of disease conditions, e.g., stroke, spinal cord injury (SCI), depression, etc. However, one hurdle for success is the variability in behavioral outcomes across different subjects. One reason for such inter-individual variability is differences in tDCS-generated electric fields (EF) inside the brain of each subject and thus the resulting tDCS-modulated neural activity. We believe a solid understanding of in vivo tDCS-generated EF and neurophysiological reactivity to tDCS is crucial to eventually minimize the variability in tDCS-related behavioral (e.g., Fugl-Meyer Motor Scale) and physiological (e.g., transcranial magnetic stimulation or TMS-evoked motor potentials or MEP) responses. Importantly, it would help us formulate dose individualization based on personal characteristics for tDCS, as already occurred for other neuromodulatory techniques, e.g., repetitive TMS (rTMS), and likely improve outcomes by decreasing inter-individual variability in recovery. This abstract will present, for the first time, tDCS-generated EF and tDCS-modulated changes in brain activity at subcortical level using stereoelectroencephalography (SEEG) with simultaneous scalp electroencephalography (EEG). We will use our finding to explore the possibility of using non-invasive EEG to individualize tDCS dose and montage in clinical setting.