EEG paroxysmal gamma waves during Bhramari Pranayama: a yoga breathing technique

Here we report that a specific form of yoga can generate controlled high-frequency gamma waves. For the first time, paroxysmal gamma waves (PGW) were observed in eight subjects practicing a yoga technique of breathing control called Bhramari Pranayama (BhPr). To obtain new insights into the nature of the EEG during BhPr, we analyzed EEG signals using time-frequency representations (TFR), independent component analysis (ICA), and EEG tomography (LORETA). We found that the PGW consists of high-frequency biphasic ripples. This unusual activity is discussed in relation to previous reports on yoga and meditation. It is concluded this EEG activity is most probably non-epileptic, and that applying the same methodology to other meditation recordings might yield an improved understanding of the neurocorrelates of meditation.     

EEG波形伪迹去除方法

EEG波形记录与ERP分析技术是认知科学和脑科学研究的新兴手段 ,但在实际研究和临床应用中 ,伪迹一直是困扰研究效度的重要因素。本文主要介绍了近年来兴起的回归方法、伪迹减法、主成分分析 (PCA)、独立成分分析 (ICA)、JADE分析等脑电伪迹去除技术。相对于传统方法 ,这些技术存在精度高、速度快、实用性强的优点 ,但它们都各自针对不同问题情境 ,均建立在特定假设基础上 ,所以应根据具体的研究目的和实验条件进行合理选择。通用性、实时性和稳定性将是伪迹去除技术的发展方向     

Mining event-related brain dynamics

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.     

Wavelet analysis of neuroelectric waveforms: a conceptual tutorial

This paper presents a nontechnical, conceptually oriented introduction to wavelet analysis and its application to neuroelectric waveforms such as the EEG and event related potentials (ERP). Wavelet analysis refers to a growing class of signal processing techniques and transforms that use wavelets and wavelet packets to decompose and manipulate time-varying, nonstationary signals. Neuroelectric waveforms fall into this category of signals because they typically have frequency content that varies as a function of time and recording site. Wavelet techniques can optimize the analysis of such signals by providing excellent joint time-frequency resolution. The ability of wavelet analysis to accurately resolve neuroelectric waveforms into specific time and frequency components leads to several analysis applications. Some of these applications are time-varying filtering for denoising single trial ERPs, EEG spike and spindle detection, ERP component separation and measurement, hearing-threshold estimation via auditory brainstem evoked response measurements, isolation of specific EEG and ERP rhythms, scale-specific topographic analysis, and dense-sensor array data compression. The present tutorial describes the basic concepts of wavelet analysis that underlie these and other applications. In addition, the application of a recently developed method of custom designing Meyer wavelets to match the waveshapes of particular neuroelectric waveforms is illustrated. Matched wavelets are physiologically sensible pattern analyzers for EEG and ERP waveforms and their superior performance is illustrated with real data examples.     

A Semantic Network Analysis of the International Communication Association

This article examines the structure of the International Communication Association (ICA) through semantic network analysis. Semantic network analysis examines the relationships among a system's components based on the shared meanings of symbols. Galileo analysis and Quadratic Analysis Procedure revealed that the semantic network for ICA based on paper titles presented to its divisions and interest groups at its 1991 conference had a high degree of correspondence with the affiliation structure reported by Barnett and Danowski. Both networks differentiated the humanistic divisions from the scientific, the mediated from the interpersonal, and the theoretical from the applied. The results are taken to be an indication of the validity of the procedures employed for determining semantic networks. Finally, results are interpreted in regard to Human Communication Research's relationship to its parent organization, ICA.     

Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.     

A new method for detecting causality in fMRI data of cognitive processing

One of the most important achievements in understanding the brain is that the emergence of complex behavior is guided by the activity of brain networks. To fully apply this theoretical approach fully, a method is needed to extract both the location and time course of the activities from the currently employed techniques. The spatial resolution of fMRI received great attention, and various non-conventional methods of analysis have previously been proposed for the above-named purpose. Here, we briefly outline a new approach to data analysis, in order to extract both spatial and temporal activities from fMRI recordings, as well as the pattern of causality between areas. This paper presents a completely data-driven analysis method that applies both independent components analysis (ICA) and the Granger causality test (GCT), performed in two separate steps. First, ICA is used to extract the independent functional activities. Subsequently the GCT is applied to the independent component (IC) most correlated with the stimuli, to indicate its causal relation with other ICs. We therefore propose this method as a promising data-driven tool for the detection of cognitive causal relationships in neuroimaging data.     

An independent component analysis of fMRI data of grapheme-colour synaesthesia

The independent component analysis (ICA) method was applied to fMRI data from two synaesthetes and their matched controls while they performed the coloured-word Stroop task and the single-letter (synaesthetic) Stroop task. ICA identified an 'attention' network, a 'perceptual' network as well as a 'conflict monitoring' network. Increased activity was observed in right V4 during the single-letter Stroop task for synaesthetes only. The finding confirms that the same neural substrate that is known to support the experience of physical colours also supports the experience of synaesthetic colours.     

On-line monitoring of eye-position signals in contingent and noncontingent paradigms

This paper presents a research methodology for the study of human attentional and perceptual processes by means of on-line monitoring of eye-position signals. The first part of the presentation considers techniques by which commercially available apparatus may be interfaced to a minicomputer for purposes of monitoring eye-position signals during psychological experiments. Hardware and software techniques related to automating the calibration, recording, and analysis of eye-position data are discussed. The second part of the paper discusses a more advanced research methodology, one in which visual stimuli are contingent upon momentary eye-position signals. The methodology is appropriate to a variety of studies in which eye movements are considered to be part of an attentional control system. Several experimental applications are described. The implementation of eye-position-stimulus contingencies on a small computer poses additional technical problems, several of which are discussed.     

The contribution of the inferior parietal cortex to spoken language production

This functional MRI study investigated the involvement of the left inferior parietal cortex (IPC) in spoken language production (Speech). Its role has been apparent in some studies but not others, and is not convincingly supported by clinical studies as they rarely include cases with lesions confined to the parietal lobe. We compared Speech with non-communicative repetitive tongue movements (Tongue). The data were analyzed with both univariate contrasts between conditions and probabilistic independent component analysis (ICA). The former indicated decreased activity of left IPC during Speech relative to Tongue. However, the ICA revealed a Speech component in which there was correlated activity between left IPC, frontal and temporal cortices known to be involved in language. Therefore, although net synaptic activity throughout the left IPC may not increase above baseline conditions during Speech, one or more local systems within this region are involved, evidenced by the correlated activity with other language regions.     

THE ICA COMMUNICATION AUDIT AND PERCEIVED COMMUNICATION EFFECTIVENESS CHANGES IN 16 AUDITED ORGANIZATIONS

This study brings to light evidence on the benefits of a methodology for evaluation of organizational communication processes and outcomes—the ICA Communication Audit. The audit procedure was developed and tested by ICA (International Communication Association) Division IV, between 1974–1974. Sixteen field tests of the audit have been conducted from 1974–1977. This study undertook an “after” survey of the 16 audited organizations to determine the degree to which the audit was perceived to improve organizational communication effectiveness as well as overall organizational effectiveness. The findings confirmed the ICA Communication Audit as a valid diagnostic methodology and organization development intervention technique which improved both communication awareness and processes in a majority of audited organizations.     

Can computer mice be used as low-cost devices for the acquisition of planar human movement velocity signals?

The main goal of this work is to determine whether a computer mouse can be used as a low-cost device for the acquisition of two-dimensional human movement velocity signals in the context of psychophysical studies and biomedical applications. A comprehensive overview of the related literature is presented, and the problem of characterizing mouse movement acquisition is analyzed and discussed. Then, the quality of velocity signals acquired with this kind of device is measured on horizontal oscillatory movements by comparing the mouse data to the signals acquired simultaneously by a video motion tracking system and a digitizing tablet. A synthesis of the information gathered in this work indicates that the computer mouse can be used for the reliable acquisition of biosignals in the context of human movement studies, particularly for many applications dealing with the velocity of the end effector of the upper limb. This paper concludes by discussing the possibilities and limitations of such use.     

Measuring concept semantic relatedness through common spatial pattern feature extraction on EEG signals

We study the semantic relationship between pairs of nouns of concrete objects such as “HORSE - SHEEP” and “SWING - MELON” and how this relationship activity is reflected in EEG signals. We collected 18 sets of EEG records; each set containing 150 events of stimulation. In this work we focus on feature extraction algorithms. Particularly, we highlight Common Spatial Pattern (CSP) as a method of feature extraction. Based on these latter, different classifiers were trained in order to associate a set of signals to a previously learned human answer, pertaining to two classes: semantically related, or not semantically related. The results of classification accuracy were evaluated comparing with other four methods of feature extraction, and using classification algorithms from five different families. In all cases, classification accuracy was benefited from using CSP instead of FDTW, LPC, PCA or ICA for feature extraction. Particularly with the combination CSP-Naïve Bayes we obtained the best average precision of 84.63%.     

Brainwave classification without the help of limb movement and any stimulus for character-writing application

Recently, Brain-Computer Interfaces (BCIs) have been extensively popular for employing Electroencephalography (EEG) signals to control devices with different applications. The use of BCIs currently involves for lots of applications to help the disabilities who cannot communicate with other people, as it is an alternative way for communication by passing the need of speech. Although the applications to spell the character with BCI systems (e.g., P300-speller, SSVEP-speller, Hex-O-spell) have been already developed, but these techniques are not flexible in the real scenarios because they require the stimulus all the time or stopping the activity to focus on the limb movement in order to provide the accuracy of brain responses. In this paper, the feasibility of brainwave classification for the applications of character-writing by considering only the EEG signals without the need of stimulus unlike the literature is newly introduced. This paper adopts a classification technique named Artificial Neural Network (ANN) and focuses on two different characters; straight line and circle. From the experimental results, the suitable position of electrodes are the pair of electrodes (F3 and F4) at the frontal lobe, which provide the best result as compared to other areas due to its important role in perception, maintenance and revival of the information. The experimental results indicate that the classification accuracy of the proposed technique is about 70%, which in turn leads to a significant achievement for the development of character-writing applications.     

At-Risk for Diabetes: Coping with the News

This study assessed 37 children's and 38 adults', as well as their family members' (39 mothers and 26 spouses), coping responses to the news that they (or a loved one) were islet-cell antibody positive (ICA+) and at risk for type 1 diabetes. The Ways of Coping Checklist (WCC) was administered 4 months after ICA+ notification and at follow-up 10 months later. Participants' state anxiety was measured a few days after ICA+ notification and again 4 months later, at the time of the initial WCC administration. Children's coping strategies differed from those of adults, and mothers' coping strategies differed from spouses'. Initial state anxiety in response to ICA+ notification was related to how participants subsequently coped with the news. Coping, in turn, was related to maintenance of state anxiety over time.     

Decomposing the brain: components and modes, networks and nodes

Smith and colleagues recently presented a temporal independent component analysis (tICA) decomposition of resting-state functional MRI data. Compared to the widely used spatial ICA (sICA), tICA better allows for a brain region to engage in multiple, independent interactions with other regions and will potentially offer new insights into brain function.     

Multiresolution Analysis of Event-Related Potentials by Wavelet Decomposition

Wavelet analysis is presented as a new tool for analyzing event-related potentials (ERPs). The wavelet transform expands ERPs into a time-scale representation, which allows the analyst to zoom in on the small scale, fine structure details of an ERP or zoom out to examine the large scale, global waveshape. The timescale representation is closely related to the more familiar time-frequency representation used in spectrograms of time-varying signals. However, time-scale representations have special properties that make them attractive for many ERP applications. In particular, time-scale representations permit theoretically unlimited time resolution for the detection of short-lived peaks and permit a flexible choice of wavelet basis functions for analyzing different types of ERPs. Generally, time-scale representations offer a formal basis for designing new, specialized filters for various ERP applications. Among recently explored applications of wavelet analysis to ERPs are (a) the precise identification of the time of occurrence of overlapping peaks in the auditory brainstem evoked response; (b) the extraction of single-trial ERPs from background EEG noise; (c) the decomposition of averaged ERP waveforms into orthogonal detail functions that isolate the waveforms experimental behavior in distinct, orthogonal frequency bands; and (d) the use of wavelet transform coefficients to concisely extract important information from ERPs that predicts human signal detection performance. In this tutorial we present an intuitive introduction to wavelets and the wavelet transform, concentrating on the multiresolution approach to wavelet analysis of ERP data. We then illustrate this approach with real data. Finally,we offer some speculations on future applications of wavelet analysis to ERP data.     

An analog optical link in surface mount technology (SMT) for multichannel biomedical data acquisition

A stand-alone, low-cost analog optical link for biomedical data is described. This compact device features low power consumption and low noise and serves as a safe link between biodata amplifiers and computerized recording equipment. A modular surface mount technology design assures the high reliability and high package density required in multichannel applications in EEG and event-related potential research. With only a few modifications, the data link can transmit dc signals with low drift, or interface fast signals such as brainstem evoked potentials. The maximum bandwidth ranges from dc to 5000 Hz.     

A proposed conceptual model of human behavior and its implications for design of warnings.

A model of human behavior is proposed that hierarchically describes levels of operator performance. Judgement-based performance occurs at the top level, and the lower levels correspond to knowledge-, rule-, and skill-based performance. Different forms of information denoted as values, symbols, signs, and signals are used at each of these levels of performance, allowing the effectiveness of different warning applications to be inferred. To be effective, warning information must be presented in the form appropriate for the operator's level of performance. Values therefore are appropriate when performance is at the judgement-based level. Explicit verbal information (symbols) is most likely to be effective when directed toward changing behavior from a knowledge- to a rule-based level, as when recommending actions in novel situations or to a judgement-based level, when goal priorities need to be changed. Signs are likely to be effective when performance is at a rule-based level, while signals are best for guiding needed transitions from a skill- to a rule-based level. Warning information should be carefully matched to the level of performance at which errors are taking place to be most effective and avoid information overload. To attain this goal, task analysis that focuses on cognitive activity is of essence. This includes measuring users' knowledge and documenting the flow of information during task performance.