The advantages of digital over analog recording techniques

Radio, Podcasts, and Music Streaming-An Electroencephalography and Physiological Analysis of Listeners' Attitude, Attention, Memory, and Engagement

Whilst radio, podcasts, and music streaming are considered unique audio formats that offer brands different opportunities, limited research has explored this notion. This current study analyses how the brain responds to these formats and suggests that they offer different branding opportunities. Participants' engagement, attitude, attention, memory, and physiological arousal were measured while each audio format was consumed. The results revealed that music streaming elicited more positive attitudes, higher attention, greater levels of memory encoding, and increased physiological arousal compared to either radio or podcasts. This study emphasises the importance for brands of utilising diverse audio channels for unique branding and marketing opportunities.

Prevalence of epileptiform electroencephalographic abnormalities in people without a history of seizures: A systematic review and meta-analysis

Abnormal patterns identified on electroencephalogram (EEG) are one of the primary diagnostic tests for epilepsy. However, epidemiological studies have established that both benign and epileptiform abnormalities (EAs) occur on the EEG of nonepileptic, seizure-free people as well. The reported rates of EAs in nonepileptic, seizure-free populations vary, and the true prevalence is unknown. The primary objective of this systematic review and meta-analysis was to estimate the overall prevalence of EAs in the EEG of people without a history of seizures. Secondary aims were to characterize (1) the cortical localization of focal abnormalities, (2) the proportion of findings that occurred during standard EEG stimulation protocols, and (3) the persistence and implications of abnormalities at follow-up. A comprehensive electronic search of six bibliographic databases was completed: Embase, MEDLINE, PsycInfo, Cumulative Index of Nursing and Allied Health Literature, Cochrane Central Register for Controlled Trials, and Web of Science. No search date restrictions were applied. Overall effect size was calculated using a generalized linear mixed-effects model. Fifty-three studies, totaling 73 990 individuals, met our inclusion criteria. The overall point prevalence of EAs was 1.74% (95% confidence interval [CI] = 1.13-2.67). Due to the risk of bias in the literature, especially from participant selection, we believe this to be an overestimate of the true prevalence. Prevalence of EAs was greater in children (2.45%, 95% CI = 1.41-4.21) and the elderly (5.96%, 95% CI = 1.39-22.13) compared with adults (.93%, 95% CI = .48-1.80). Reports of developing epilepsy after an EA-positive EEG were rare. The likelihood of subsequent positive findings on follow-up EEG may be as high as 50%. Our study has limitations in that males were overrepresented in the study samples, there is substantial heterogeneity among studies, and many studies provided insufficient detail about their exclusion criteria. Nonetheless, our estimates provide benchmark data for future studies examining EAs in clinical populations, particularly behavioral and psychiatric populations.

Point of Care Testing (POCT) in Psychopathology Using Fractal Analysis and Hilbert Huang Transform of Electroencephalogram (EEG)

Research has shown that relying only on self-reports for diagnosing psychiatric disorders does not yield accurate results at all times. The advances of technology as well as artificial intelligence and other machine learning algorithms have allowed the introduction of point of care testing (POCT) including EEG characterization and correlations with possible psychopathology. Nonlinear methods of EEG analysis have significant advantages over linear methods. Empirical mode decomposition (EMD) is a reliable nonlinear method of EEG pre-processing. In this chapter, we compare two existing EEG complexity measures - Higuchi fractal dimension (HFD) and sample entropy (SE), with our newly proposed method using Higuchi fractal dimension from the Hilbert Huang transform (HFD-HHT). We present an example using the three complexity measures on a 2-minute EEG recorded from a healthy 20-year-old male after signal pre-processing. Furthermore, we showed the usefulness of these complexity measures in the classification of major depressive disorder (MDD) with healthy controls. Our study is in line with previous research and has shown an increase in HFD and SE values in the full, alpha and beta frequency bands suggestive of an increase in EEG irregularity. Moreover, the HFD-HHT values decreased in those three bands for majority of electrodes which is suggestive of a decrease in irregularity in the frequency-time domain. We conclude that all three complexity measures can be vital features useful for EEG analysis which could be incorporated in POCT systems.

Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue

Mental fatigue is a widely studied topic on account of its serious negative effects. But how the neural mechanism of task switching before and after mental fatigue remains a question. To this end, this study aims to use brain functional network features to explore the answer to this question. Specifically, task-state EEG signals were recorded from 20 participants. The tasks include a 400-s 2-back-task (2-BT), followed by a 6480-s of mental arithmetic task (MAT), and then a 400-s 2-BT. Network features and functional connections were extracted and analyzed based on the selected task switching states, referred to from Pre_2-BT to Pre_MAT before mental fatigue and from Post_MAT to Post_2-BT after mental fatigue. The results showed that mental fatigue has been successfully induced by long-term MAT based on the significant changes in network characteristics and the high classification accuracy of 98% obtained with Support Vector Machines (SVM) between Pre_2-BT and Post_2-BT. when the task switched from Pre_2-BT to Pre_MAT, delta and beta rhythms exhibited significant changes among all network features and the selected functional connections showed an enhanced trend. As for the task switched from Post_MAT to Post_2-BT, the network features and selected functional connectivity of beta rhythm were opposite to the trend of task switching before mental fatigue. Our findings provide new insights to understand the neural mechanism of the brain in the process of task switching and indicate that the network features and functional connections of beta rhythm can be used as neural markers for task switching before and after mental fatigue.

Neural decoding of semantic concepts: A systematic literature review

Objective Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world. Modern neuroscience research is increasingly exploring how individual semantic concepts are encoded within our brains and a number of studies are beginning to reveal key patterns of neural activity that underpin specific concepts. Building upon this basic understanding of the process of semantic neural encoding, neural engineers are beginning to explore tools and methods for semantic decoding: identifying which semantic concepts an individual is focused on at a given moment in time from recordings of their neural activity. In this paper we review the current literature on semantic neural decoding. Approach We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we assess the eligibility of published peer-reviewed reports via a search of PubMed and Google Scholar. We identify a total of 74 studies in which semantic neural decoding is used to attempt to identify individual semantic concepts from neural activity. Results Our review reveals how modern neuroscientific tools have been developed to allow decoding of individual concepts from a range of neuroimaging modalities. We discuss specific neuroimaging methods, experimental designs, and machine learning pipelines that are employed to aid the decoding of semantic concepts. We quantify the efficacy of semantic decoders by measuring information transfer rates. We also discuss current challenges presented by this research area and present some possible solutions. Finally, we discuss some possible emerging and speculative future directions for this research area. Significance Semantic decoding is a rapidly growing area of research. However, despite its increasingly widespread popularity and use in neuroscientific research this is the first literature review focusing on this topic across neuroimaging modalities and with a focus on quantifying the efficacy of semantic decoders.

A study on CNN image classification of EEG Signals represented in 2D and 3D

Objective The novelty of this study consists of the exploration of multiple new approaches of data pre-processing of brainwave signals, wherein statistical features are extracted and then formatted as visual images based on the order in which dimensionality reduction algorithms select them. This data is then treated as visual input for 2D and 3D CNNs which then further extract 'features of features'. Approach Statistical features derived from three electroencephalography datasets are presented in visual space and processed in 2D and 3D space as pixels and voxels respectively. Three datasets are benchmarked, mental attention states and emotional valences from the four TP9, AF7, AF8 and TP10 10-20 electrodes and an eye state data from 64 electrodes. 729 features are selected through three methods of selection in order to form 27x27 images and 9x9x9 cubes from the same datasets. CNNs engineered for the 2D and 3D preprocessing representations learn to convolve useful graphical features from the data. Main results: A 70/30 split method shows that the strongest methods for classification accuracy of feature selection are One Rule for attention state and Relative Entropy for emotional state both in 2D. In the eye state dataset 3D space is best, selected by Symmetrical Uncertainty. Finally, 10-fold cross validation is used to train best topologies. Final best 10-fold results are 97.03% for attention state (2D CNN), 98.4% for Emotional State (3D CNN), and 97.96% for Eye State (3D CNN). Significance: The findings of the framework presented by this work show that CNNs can successfully convolve useful features from a set of pre-computed statistical temporal features from raw EEG waves. The high performance of K-fold validated algorithms argue that the features learnt by the CNNs hold useful knowledge for classification in addition to the pre-computed features.

An Introductory Guide to Organizational Neuroscience

The time is ripe for a renewed and interdisciplinary approach to organizational research that incorporates neuroscientific techniques. Like all methods, they have methodological, analytical, and interpretational limitations; however, the potential gains from using these techniques are far more considerable. We have therefore assembled a succinct yet authoritative collection of articles on the topic of neuroscience in organizational research, to serve as a solid introduction to the methods of neuroscience and what they can accomplish. The special topic is organized into two parts. The first includes a set of accessible reviews of the palette of brain imaging, mapping, and stimulation techniques (fMRI, fNIRS, EEG, MEG, and NIBS) as well as examples of the application of neuroscience methods to various disciplines including economics, marketing, finance, organizational behavior, neuroethology, as well an integrative translational critique on a variety of applications. The second is a collection of articles resulting from a competitive call for submissions that cover various neuroscience topics but also address important methodological and philosophical issues. The articles lay out a roadmap for the effective integration of neuroscientific methods into organizational research.

Application of mobile phones in epilepsy care

Objectives To evaluate the applications of mobile phones in the day to day care of epileptic patients as a diagnostic, prognostic and therapeutic tool.

Methods Detailed search of various mobile applications in the field of epileptology was made in MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, CINAHL, LILACS and corresponding developer websites of mobile applications were also looked into regarding their technical specifications and user friendliness.

Results A plethora of apps are available across various mobile platforms especially Android, iOS and Windows. Careful selection and application of such apps by both the healthcare providers, the epileptic patients and their caregivers with proper understanding of their potential benefits as well as limitations will result in better diagnosis, prognosis and treatment of epilepsy.

Conclusion The field of medicine is rapidly inculcating advanced cutting edge technologies for better diagnosis of diseases and better targeted therapy to such diseases. Hi tech electronic gadgets, in particular, are now becoming part and parcel of patient care in many specialties. The advent of the modern portable computers has revolutionised almost every specialty. The field of mobile technology is advancing with a break neck pace, with increase in mobile subscribers, advanced handsets practically like digital personal assistants with advanced capabilities. The possibilities of using such rapidly evolving mobile technology in the field of medicine are endless. This article explores such possibilities in the field of epileptology after analysing the current and existing applications of mobile phones in care of the epileptic patients worldwide.

Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data.

Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations

In this paper we analyzed, by the F_DFA root mean square fluctuation (rms) function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG). We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA) method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F₃32, F₆37 (frontal region of the head) and P₃49, P₆54 (parietal region of the head). We verified that the amplitude of the F_DFA rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between F_DFA (in log scale) for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, α_DFA by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing.

Weak connections form an infinite number of patterns in the brain

Recently, much attention has been paid to interpreting the mechanisms for memory formation in terms of brain connectivity and dynamics. Within the plethora of collective states a complex network can exhibit, we show that the phenomenon of Collective Almost Synchronisation (CAS), which describes a state with an infinite number of patterns emerging in complex networks for weak coupling strengths, deserves special attention. We show that a simulated neuron network with neurons weakly connected does produce CAS patterns, and additionally produces an output that optimally model experimental electroencephalograph (EEG) signals. This work provides strong evidence that the brain operates locally in a CAS regime, allowing it to have an unlimited number of dynamical patterns, a state that could explain the enormous memory capacity of the brain, and that would give support to the idea that local clusters of neurons are sufficiently decorrelated to independently process information locally.

Voltage imaging to understand connections and functions of neuronal circuits

Understanding of the cellular mechanisms underlying brain functions such as cognition and emotions requires monitoring of membrane voltage at the cellular, circuit, and system levels. Seminal voltage-sensitive dye and calcium-sensitive dye imaging studies have demonstrated parallel detection of electrical activity across populations of interconnected neurons in a variety of preparations. A game-changing advance made in recent years has been the conceptualization and development of optogenetic tools, including genetically encoded indicators of voltage (GEVIs) or calcium (GECIs) and genetically encoded light-gated ion channels (actuators, e.g., channelrhodopsin2). Compared with low-molecular-weight calcium and voltage indicators (dyes), the optogenetic imaging approaches are 1) cell type specific, 2) less invasive, 3) able to relate activity and anatomy, and 4) facilitate long-term recordings of individual cells' activities over weeks, thereby allowing direct monitoring of the emergence of learned behaviors and underlying circuit mechanisms. We highlight the potential of novel approaches based on GEVIs and compare those to calcium imaging approaches. We also discuss how novel approaches based on GEVIs (and GECIs) coupled with genetically encoded actuators will promote progress in our knowledge of brain circuits and systems.

Prevalence of Epileptiform Discharges in Healthy Infants

Most studies on epileptiform discharges in young children were performed using analog electroencephalographic (EEG) recording systems and a limited numbers of electrodes that might have a lower detection rate for epileptiform discharges than modern digital recording systems. Knowing the prevalence of epileptiform discharges in healthy children is critical for a valid interpretation of findings in patients with a suspected epileptic disorder. We reviewed EEG recordings of 393 otherwise healthy children aged 12 to 60 months using digital EEG recording with respect to epileptiform discharges. We found epileptiform discharges in 3 children aged 12, 34 and 55 months resulting in a prevalence of epileptiform discharges in our cohort of 0.76% (95% confidence interval 0.0% to 1.62%). The prevalence of epileptiform discharges in children younger than 5 years is by far lower than in older children, and the digital findings are in accordance with previous data of conventional EEG.

A review of recent literature employing electroencephalographic techniques to study the pathophysiology, phenomenology, and treatment response of schizophrenia

Clinical experience and research findings suggest that schizophrenia is a disorder comprised of multiple genetic and neurophysiological subtypes with differential response to treatment. Electroencephalography (EEG) is a non-invasive, inexpensive and useful tool for investigating the neurobiology of schizophrenia and its subtypes. EEG studies elucidate the neurophysiological mechanisms potentially underlying clinical symptomatology. In this review article recent advances in applying EEG to study pathophysiology, phenomenology, and treatment response in schizophrenia are discussed. Investigative strategies employed include: analyzing quantitative EEG (QEEG) spectral power during the resting state and cognitive tasks; applying machine learning methods to identify QEEG indicators of diagnosis and treatment response; and using the event-related brain potential (ERP) technique to characterize the neurocognitive processes underlying clinical symptoms. Studies attempting to validate potential EEG biomarkers of schizophrenia and its symptoms, which could be useful in assessing familial risk and treatment response, are also reviewed.

Methods to eliminate stimulus transduction artifact from insert earphones during electroencephalography

OBJECTIVE

To reduce stimulus transduction artifacts in EEG while using insert earphones.

DESIGN

Reference Equivalent Threshold SPLs were assessed for Etymotic ER-4B earphones in 15 volunteers. Auditory brainstem responses (ABRs) and middle latency responses (MLRs)-as well as long-duration complex ABRs-to click and /dα/ speech stimuli were recorded in a single-case design.

RESULTS

Transduction artifacts occurred in raw EEG responses, but they were eliminated by shielding, counter-phasing (averaging across stimuli 180° out of phase), or rereferencing.

CONCLUSIONS

Clinical grade ABRs, MLRs, and cABRs can be recorded with a standard digital EEG system and high-fidelity insert earphones, provided one or more techniques are used to remove the stimulus transduction artifact.

Toward functioning and usable brain-computer interfaces (BCIs): a literature review

PURPOSE

The aim of this paper is to provide an exhaustive review of the literature about brain-computer interfaces (BCIs) that could be used with these paralysed patients. The electroencephalography (EEG) is the best candidate for the continuous use in the environment of patients' houses, due to its portability and ease of use. For this reason, the present paper will focus on this kind of BCI. Moreover, it is our aim to focus more on the patients, regarding their active role in the modulation of the brain activity. This leads to a differentiation between studies that use an active regulation and studies that use a non-active regulation.

METHOD

Relevant articles in the BCIs field were selected using MEDLINE and PsycINFO.

RESULTS

Research through data banks produced 980 results, which were reduced to 127 after exclusion criteria selection. These references were divided in four categories, based on the use of active or non-active regulation, and on the event related potential used.

CONCLUSIONS

In most of the examined works, the focus was on the development of systems and algorithms able to recognise and classify brain events. Although this kind of research is fundamental, a user-centred point of view was rarely adopted. [Box: see text].

Noninvasive assessment of physiologic state of living systems

OBJECTIVE

The aim of this research is to demonstrate the possibilities of a new device and propose the method of noninvasive evaluation of the integral physiologic state of biologic objects.

DESIGN

This study was a series of experiments with an elaborated device carried out on different living systems, including plants, animals, and people.

SETTING

Experiments were performed in the Laboratory of Integrative Biology at Orbeli Institute of Physiology of National Academy of Science of the Republic of Armenia.

METHODS

The working principle of the device is based on a change of the amplitude of light reflected from the sensor when a biologic object is placed some distance from the device. The obtained signals were monitored and analyzed by computer. Comparative studies were performed by standard electrophysiologic methods.

PARTICIPANTS

The experiments were performed mainly on laboratory rats, but some research was done with people.

RESULTS

Various biologic objects exert different influence on the device; also, signals from the photodetector are changed depending on the functional state of the studied object subjected to the physical or medical influence. The device does not respond to inanimate objects at room temperature.

CONCLUSIONS

The existence of a remote influence of biologic objects on surrounding objects is discovered by means of a novel elaborated device complex. The device can be used for objective distant evaluation of a functional state of biologic objects. Particularly, it can be employed for distant registration of signals from acupuncture points and zones.

Assessment of the QT interval in the electroencephalography (EEG) of children with syncope, epilepsy, and attention-deficit hyperactivity disorder (ADHD)

The interpretation of QT interval is often neglected during electroencephalography (EEG) reading. We compared the incidence of prolonged QT interval, as seen in the electrocardiography (ECG) recording lead of the EEG, in children presenting with seizure, syncope, or attention-deficit hyperactivity disorder (ADHD). Abnormal QT was defined as >460 ms. The incidence of prolonged QT in the seizure, syncope, and ADHD groups was 1/50 (2%), 7/50 (14%), and 2/50 (4%), respectively (P = .036, chi-square). The mean +/- SD of QT were 405 +/- 34, 424 +/- 39, and 414 +/- 36, respectively (P = .035, analysis of variance [ANOVA], syncope group, compared with seizure group). The incidence of prolonged QT as measured in the EEG was unexpectedly high in children presenting with seizure, syncope, or ADHD. These data support the concept that QT evaluation should be emphasized during routine EEG reading, as it may aid in identifying cases of undiagnosed cardiac conduction abnormalities. Prospective studies comparing EEG-ECG tracings with 12-lead ECG are warranted.

Prevalence of epileptiform discharges in healthy children--new data from a prospective study using digital EEG

PURPOSE

Data on epileptiform electroencephalography (EEG) discharges in healthy children are limited, with published studies dating back more than 20 years. Moreover, analyses have been performed exclusively using paper-recorded EEG, and reported prevalences differ significantly. With recent reports using these data as reference suggesting an increased prevalence of epileptiform EEG discharges in children with behavioral disturbances, acquisition of exact prevalence data has become even more critical. The aim of our study was to analyze the frequency of epileptiform EEG discharges in healthy children using digitally recorded EEG (DEEG) and to compare these data to those of previously published studies.

METHODS

Prospective analysis of DEEG was performed in 382 healthy children (226 male, 156 female) ages 6-13 years admitted to our hospital for minor head trauma. Recording was carried out for a minimum of 20 min including hyperventilation and photic stimulation. Analysis was carried out by two board-certified clinical neurophysiologists.

RESULTS

Epileptiform EEG discharges were detected in 25 of 382 children (11 of 226 male, 14 of 156 female) corresponding to an overall prevalence of 6.5%. Of these 25 children, 4 had either generalized or bifrontal spikes, 12 showed constant localized focal discharges, and 9 showed multifocal discharges. Compared to previous studies using non-DEEG recording, the prevalence of epileptiform EEG discharges in our population was significantly higher. No significant difference was found when comparing our data to prevalences recently reported in children with behavioral disturbances using DEEG.

CONCLUSIONS

Our study further highlights the urgent need to reevaluate the prevalence of epileptiform EEG discharges in healthy children using DEEG recordings in a large cohort.

[From the analogic to the digital era: technical EEG progress for presurgical investigations of refractory partial epilepsies]

Over the past two decades, exploration of epileptic patients has benefited from technological advances in electroencephalography. Computerized equipment can simultaneously retrieve cerebral electric activity and signs of the patient's ictal activity. The computer era has improved reliability, storage and analysis of the recorded data and provides easier communication tools for multidisciplinary staff and patient case studies. Therefore, an increasing number of patients could benefit from presurgical investigations and surgical treatment.

Clinical neurophysiology of epilepsy

Electroencephalography (EEG) continues to be the most important diagnostic tool in the management of patients with epilepsy. In particular, the high specificity of interictal epileptiform discharges makes scalp EEG a valuable tool in the evaluation of patients with a history of seizures or seizure-like episodes. Advances in technology, most notably the development of digital video-EEG, have significantly expanded the utility of EEG. In addition to the routine EEG, long-term monitoring studies including video-EEG, ambulatory EEG, and continuous EEG monitoring play important roles in various aspects of the diagnosis and treatment of epilepsy. Recent developments in computerized seizure detection and prediction algorithms, particularly those utilizing intracranial EEG electrodes, hold promise for future development of novel treatment strategies.

Quantified EEG analysis monitoring in a novel model of general anaesthesia in rats

The aim of this research was to evaluate the safety and reliability of an anaesthetic mixture (Equitensine: pentobarbital, chloral hydrate, dihydroxypropane, ethanol) which, unlike other 'classic' anaesthetics, such as ketamine [The Electroencephalogram in Anaesthesia, Springer, Berlin, 1984], has been demonstrated not to induce alterations in the extracellular concentrations of cerebral excitatory amino acids. Quantified EEG analysis monitoring and behavioural observation were used to quantify the degree and the time course of the changes in cerebral electrical activity, analgesia and sedation induced, in rats, by the compound under investigation. Equitensine (0.33 ml/100 g), administered intraperitoneally, induced analgesia (monitored by the tail flick method) for 60-70 min and a pattern of behavioural sedation (loss of the righting reflex) lasting, on average, 130-150 min. The EEG monitoring revealed a pattern typical of burst suppression which lasted 15-20 min, followed by another, lasting 270-300 min, characterized by slow waves of high amplitude. The quantified EEG analysis demonstrated that the changes in cerebral electrical activity lasted longer than behavioural observation suggested. The compound under examination was found to be safe, reliable and non-invasive to administer and sustain in all the animals, and quantified EEG analysis proved to be a very sensitive method for highlighting the functional changes in the central nervous system.

A general method for remontaging based on a singular value decomposition algorithm

The authors developed a general mathematic algorithm to convert any montage (referential, bipolar, or Laplacian) to any other by linear transformation. Input and output montages are described by matrices, and singular value decomposition is used to find the linear transformation. An error signal can be calculated from the input data to monitor remontaging validity. This algorithm also identifies output channels that cannot be obtained from the specified input. The authors tested this algorithm using an instrument that retrieves digitally encoded EEG data from videotape and produces signals in referential or bipolar form. They obtained good agreement when they compared referential and Laplacian data derived from bipolar output with the same montages calculated from referential output for the same EEG segment.

Role and limitations of routine and ambulatory scalp electroencephalography in diagnosing and managing seizures

The scalp electroencephalogram (EEG) is the cornerstone in the diagnosis and treatment of seizure disorders. The EEG, with its excellent temporal resolution, provides a direct measurement of cortical electrophysiology, revealing, for example, the presence of interictal epileptiform discharges that identify regions of an epileptogenic brain. We define the EEG characteristics of focal and generalized epileptiform discharges and provide evidence for their varying diagnostic importance in different patient populations. Identification of nonepileptiform EEG transients, such as wicket waves, small sharp spikes,rhythmic temporal theta activity, and 14- and 6-Hz positive bursts, that can be confused for epileptiform transients is emphasized. A final point is that the clinician must interpret EEG findings within the overall clinical context.

A simple algorithm for a digital three-pole Butterworth filter of arbitrary cut-off frequency: application to digital electroencephalography

Algorithms for low-pass and high-pass three-pole recursive Butterworth filters of a given cut-off frequency have been developed. A band-pass filter can be implemented by sequential application of algorithms for low- and high-pass filters. The algorithms correspond to infinite impulse-response filters that have been designed by applying the bilinear transformation to the transfer functions of the corresponding analog filters, resulting in a recursive digital filter with seven real coefficients. Expressions for filter coefficients as a function of the cut-off frequency and the sampling period are derived. Filter performance is evaluated and discussed. As in the case of their analog counterparts, their transfer function shows marked flattening over the pass band and gradually higher attenuation can be seen at frequencies above or below the cut-off frequency, with a slope of around 60 dB/decade. There is a 3 dB attenuation at the cut-off frequency and a gradual increase in phase shift over one decade above or below the cut-off frequency. Low-pass filters show a maximum overshoot of 8% and high-pass filters show a maximum downwards overshoot of approximately 35%. The filter is mildly under-damped, with a damping factor of 0.5. On an IBM 300GL personal computer at 600 MH with 128 MB RAM, filtering time with MATLAB 5.2 running under Windows 98 is of the order of 50 ms for 60000 samples. This will be adequate for on-line electroencephalography (EEG) applications. The simplicity of the algorithm to calculate filter coefficients for an arbitrary cut-off frequency can be useful to modern EEG laboratories and software designers for electrophysiological applications.

Computerized EEG monitoring

Monitoring of central nervous system function in the intensive care unit is becoming more widely accepted as an integral part of critical care. The history of developments in electroencephalogram (EEG) technology is reviewed to better appreciate the rate of technological developments and their application to clinical practice. Basic concepts of digital EEG are reviewed. Principals of intensive care unit monitoring as they apply to clinical neurophysiological techniques are examined to better understand the goals for an "ideal central nervous system monitor." Some current advances and directions for future development in computerized EEG monitoring are discussed.

A new way of building a database of EEG findings

Whereas computer-based electroencephalography (EEG) is widely applied, the EEG interpretations are usually not stored in a way that favours exploitation of modern computer technology. This paper reports an EEG description system facilitating categorization of EEG data in a computerized database. The system interactively communicates with the digital EEG system and also with the general patient administrative system. The main new quality of this system is the methods for data input and automatic data retrieval from several systems, rather than the establishment of a database of EEG data itself. The EEGs are visually analysed and categorized. Manually marked EEG events are automatically transferred to the database and such events as well as defined electrode positions within these epochs are directly linked to their corresponding descriptions. The database is updated without demand for filling in the events in the database in a second operation. Thereby, the EEG interpreter builds the database while analysing the EEG. This system provides an improved accessibility of EEG data for clinical, normative, educational and scientific use.

EEG monitoring in the intensive care unit: pitfalls and caveats

Electroencephalogram monitoring is a valuable means of monitoring thalamocortical function in the comatose, sedated, or paralyzed patient in the intensive care unit. The following problems arise especially with long-term recordings that are beyond those experienced in the standard EEG laboratory: 1) faulty electrodes, either single- or multiple-scalp electrodes or ground or reference electrodes; 2) connections of electronic equipment; 3) induced artifacts from electronic devices and nonelectronic equipment; 4) electrode placement issues; and 5) biologic, including movement-related, artifacts. Continuous quality improvement strategies should be implemented to minimize problems. Prompt troubleshooting and regular review sessions are two important components.

Dipole source localization of ictal epileptiform activity

Dipole source localization of ictal epileptiform activity recorded by scalp EEG was performed in patients prior to surgical treatment. The dipole tracing method combined with the scalp-skull-brain head model was used to locate epileptogenic foci. A digital EEG system was used for data collection. The accuracy of dipole source localization was evaluated by comparing the focus location with that obtained by chronic subdural electrocorticography. In a case of frontal lobe epilepsy with epileptogenic focus in the frontoparietal convexity, the results of dipole source localization agreed well with those obtained with chronic subdural electrocorticography. In a case of lateral temporal lobe epilepsy, the results of dipole source localization were consistent with those obtained with chronic subdural electrocorticography, but a small localization error was observed. The clinical usefulness of and suggestions for improving this method are discussed.

Computer-based electroencephalography: technical basics, basis for new applications, and potential pitfalls

EEG has been recorded on paper-based analog systems for over 50 years. In the past 5 years, computer-based digital systems have become more widely used. Digital systems eliminate some artifacts that plagued analog recordings but introduce subtle new problems including aliasing and dynamic range. Digital systems allow reformatting of the same EEG segment using different gain, filter and montage settings. The digital signal allows for measurement and computations on the EEG, leading to applications such as power spectrum, topographic mapping, and spike or seizure detection.

Common applications of electrophysiology (EEG) in the past and today: the technologist's view

The field of electroencephalography (EEG) has witnessed a dramatic development during the last decade. The electroencephalogram that had been principally used as a 'post-hoc' diagnostic procedure is now fully used as an 'on-line' monitor of neural function with its excellent temporal resolution. Neurophysiological monitoring in the operating room, neurological intensive care unit (ICU) and during endovascular procedures allows early identification of impending neurological deficits before irreversible neurological impairment. Long-term monitoring of scalp and synchronized video-EEG recordings and invasive (depth and subdural) electrode studies are well-established tools in the localization of the epileptogenic region for identification of potential candidates for surgery. The advent of digital EEG with digital storage and the ability to manipulate data with digital reformatting, filter and sensitivity changes has allowed us to maximize the information and reduce artifacts. These changes have revolutionized the way in which EEG is performed and interpreted.