Wavelet analysis in neurodynamics

Public Speaking Anxiety (PSA) is a common and impairing psychological condition, causing significant distress and interference in multiple domains of functioning. Acceptance and Commitment Therapy (ACT) has demonstrated efficacy treating several psychological disorders, and preliminary evidence supports its efficacy for PSA. However, there is no prior research examining the neurological changes associated with ACT for individuals with PSA. Electroencephalogram (EEG) is a useful technique for examining baseline resting state patterns of brain electrical activity in addition to changes that may occur following a course of treatment. In addition, frontal alpha asymmetry has been shown to be related to approach/avoidance behaviors that may be indicative of changes in PSA symptomatology. Analogue interventions can serve as "mini" doses of treatment, potentially allowing researchers to quickly determine the efficacy of a treatment for an individual prior to embarking on a lengthy course of therapy. The present study is the first to examine the role of frontal brain asymmetry among individuals with PSA receiving a brief, acceptance-based analogue intervention. Specifically, we sought to determine whether greater left frontal hemispheric activity at baseline predicted response to an ACT analogue treatment. We enrolled 33 participants with public speaking anxiety, collecting clinician-rated and self-rated baseline assessments of symptomatology. Baseline resting state frontal alpha asymmetry was recorded using EEG, followed by a second EEG recording in which participants were asked to anticipate giving a brief public speech immediately afterward. Following an initial public speaking task, participants were shown a 30minute, video-recorded, acceptance-based intervention introducing concepts derived from ACT. Following the intervention, a third EEG recording was made, in which participants were asked to anticipate giving a second public speech, utilizing the skills they learned during the intervention. Immediately following this recording period, participants delivered a second public speech to a small audience. Response to treatment was quantified using participants' self-reported ratings of anxiety and "openness to anxiety" recorded during the pre- and post-treatment public speaking tasks. Results indicate that individuals with greater baseline anxiety symptomatology demonstrated greater increases in relative right hemispheric activity in anticipation of the pre-treatment public speaking task. Overall, the sample demonstrated a trend toward greater relative left frontal hemispheric activity across the three EEG time points, although this change did not reach statistical significance. Although no measure of frontal asymmetry was able to predict response to treatment, participants reported statistically significant anxiety reduction and increases in openness to anxiety, providing support for the utility of analogue interventions. Implications of the findings are discussed, and recommendations for future research are provided.

The representation of spatiotemporal pattern of brain electrical activity is an important issue in the research of neural assemblies. This paper describes methods of parametric 3D head or brain modeling and its corresponding interpolation for functional imaging based on brain waves. The 3D interpolation method is an extension of cortical imaging technique. It can be used with features of either transformed domains or higher order statistics of brain waves on realistic head or brain models. The simulating result suggests that it is a better method in comparison with the global nearest neighbor technique. With those techniques, an example of brainwave imaging was given using partial coherence of event-related potentials estimated by multivariate autoregressive model.

Evidence from a variety of sensory modalities has suggested that the left hemisphere may be 'tuned' to process more rapidly changing stimuli than the right and some have suggested that this difference forms the foundation of the functional dichotomy often drawn between the two hemispheres. Odors may be thought to engage these same temporally dependent processes as portions of an odor mixture may come to be transduced into a phasic series of neural events. Using brain electrical activity, we show that the temporal sequence of the odor alters the pattern of brain electrical activity. Estimates of the source localization for this activity indicate that rapidly changing odors, like sounds, visual and tactile stimuli, show increased activity in the left hemisphere.

Several studies in the last 40 years have used electroencephalography (EEG) to recognize patterns of brain electrical activity correlated with emotions evoked by various stimuli. For example, the frontal alpha and theta asymmetry models to distinguish musical emotions and musical pleasure, respectively. Since these studies have used mainly tonal music, in this study, we decided to incorporate both tonal (n = 8) and atonal (n = 8) musical stimuli to observe the subjective and electrophysiological responses associated with valence, arousal, pleasure, and familiarity, from 25 nonmusician Mexican adults (10 females, 15 males; M = 37.8 years old, SD = 15.1). Our results showed that atonal music was perceived as less familiar and pleasurable than tonal music, according to the average subjective ratings. Interestingly, greater right hemispheric activity (alpha suppression) was associated with atonal music listening. Additionally, there was an increase of theta power at the right frontal cortex (F4) correlated with a decrease of pleasure ratings, in line with the frontal theta asymmetry (FTA) model. Finally, according to the model of frontal alpha asymmetry (FAA) to distinguish musical emotions, activation (alpha suppression) of the left frontal cortex (F3) was correlated with greater valence and arousal-that is, joyful music. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Abstract: Classification of human thought is an emerging research field that may allow us to understand human brainfunctions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introducea new approach to classify various mental states from noninvasive electrophysiological recordings of human brainactivity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signalsrecorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spa-tiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-relatedsynchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current(input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices.Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For theverification of our approach, eight participants took part in the present study; their EEG data were recorded whilethey performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequentlybetween trials in the same class than th ose in different classes, indicating that th ese spatiospectral pattern maps couldbe used to classify different mental states. The classification accuracy was evaluated for each participant from boththe proposed approach and a conventional mental state classification method based on the inter-hemispheric spectralpower asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (± 9.64%) wasattained for the proposed method; whereas an average accuracy of 57% (± 5.68%) was attained for the conventionalmethod (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple clas-sification approach might be one of the promising methods in discriminating various mental states.Key words:brain-computer interface (BCI), spatiospectral pattern, mental state classification, mind reading, elec-troencephalography (EEG)

Patterns of brain electrical activity during facial signs of emotion in 10-month-old infants.

Objective.Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain-computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance.Approach.We explicitly model the inter-channel relationships using the self-attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named spatial-temporal fusion network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships.Main results.The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU.Significance.The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments.

Review Article Tactile sensing for mechatronics—a state of the art survey

Epilepsy is a neurological condition characterised by abnormal behaviour and recurring seizures as a result of abnormal brain activity. Epilepsy diagnosis necessitates the examination of a huge amount of EEG data, which is both time-consuming and complex. Epilepsy is classified as unusual spikes in the EEG recordings. This research demonstrates utilizing statistical feature extraction followed by deep learning techniques LSTM and CNN to diagnose epilepsy from multi-channel EEG recordings. The method was created to address the shortcomings of previous Machine Learning methods that fail when dealing with massive and multi-channel datasets. Much importance has been placed in explaining the significance of the statistical features that are used as feature extraction. Furthermore, the proposed method has been validated on the clinical CHB-MIT dataset, providing validation accuracy of 94.5% and specificity of 96.3%.

ECT and multichannel EEG recording.

WAY-EEG-GAL is a dataset designed to allow critical tests of techniques to decode sensation, intention, and action from scalp EEG recordings in humans who perform a grasp-and-lift task. Twelve participants performed lifting series in which the object’s weight (165, 330, or 660 g), surface friction (sandpaper, suede, or silk surface), or both, were changed unpredictably between trials, thus enforcing changes in fingertip force coordination. In each of a total of 3,936 trials, the participant was cued to reach for the object, grasp it with the thumb and index finger, lift it and hold it for a couple of seconds, put it back on the support surface, release it, and, lastly, to return the hand to a designated rest position. We recorded EEG (32 channels), EMG (five arm and hand muscles), the 3D position of both the hand and object, and force/torque at both contact plates. For each trial we provide 16 event times (e.g., ‘object lift-off’) and 18 measures that characterize the behaviour (e.g., ‘peak grip force’).

Electroencephalographic (EEG) signals recorded from the scalp surface are generally highly correlated. Each channel is a linear mixture of concurrently active brain and non-brain electrical sources whose activities are volume conducted to the scalp electrodes with broadly overlapping patterns (Nunez et al., 1997). This property is particularly relevant to connectivity analyses, which seek to detect and characterize active interactions between brain regions. Therefore, meaningful connectivity patterns can be derived only from measures of cortical source activities and not directly from EEG channel activities (Michel and Murray, 2012). However, this poses a serious problem, since estimating the nature, number, brain (or non-brain) locations, and time courses of the active sources contributing to the scalp EEG is not straightforward (Baillet et al., 2001). Several methods have been proposed to estimate source activities from multi-channel EEG recordings, thereby removing the confounding effects of volume conduction. These methods can be grouped into three categories: (a) simple spatial filters that seek to reduce correlations between scalp channels based on idealized assumptions; (b) more complex spatial filters that seek to estimate net activities within ROIs based on detailed neurophysiological head models; and (c) blind spatial source separation methods that seek to separately identify source signals by exploiting source signal information differences. Whereas simple spatial filters such as bipolar derivations and Laplacian filters can reduce, to some extent, correlations among scalp-recorded channels (Fisch, 2012), more sophisticated spatial filtering methods use inverse imaging methods to estimate the time courses of cortical sources in given or estimated region of interest (ROI) (Baillet et al., 2001). Blind source separation techniques, in particular independent component analysis, by contrast, learn spatial filters from the EEG time courses that separate the data into constituent independent source activities. Their corresponding brain (or non-brain) locations can then be estimated using neurophysiological inverse imaging methods (Makeig et al., 1996; Jung et al., 2001; Delorme et al., 2012). Vector autoregressive (VAR) models are versatile tools for analyzing multivariate time series, including multi-channel EEG or multivariate source activities. VAR models predict current values of time series from their recent past (Lutkepohl, 2005). Importantly, they can be used to derive various electrophysiological connectivity measures (Schlogl and Supp, 2006). Volume conduction in biological tissue can be modeled as instantaneous propagation of activity from sources to recording channels. The resulting zero-phase connectivity may be treated as noise added to lagged connectivity patterns of interest. Although some measures, including the imaginary part of the coherency (Nolte et al., 2004), are insensitive to zero-phase connectivity, measures derived from VAR model coefficients do not include such zero-phase terms. Thus, volume conduction effects are not accounted for by the model and affect the correlation structure of the model residuals, which are normally assumed to be uncorrelated. Popular connectivity measures derived from VAR models include the Directed Transfer Function (DTF) (Kaminski and Blinowska, 1991) and the Partial Directed Coherence (PDC) (Baccala and Sameshima, 2001). Whereas the PDC is defined in terms of the system matrix (a frequency domain representation of the VAR model), the DTF is based on the inverse of the system matrix. Viewing lagged dependencies between source signals as information flow, the DTF may be said to be normalized by the inflow of information to some sink, while the PDC is normalized by the outflow of information from some source. As we will demonstrate below, both the DTF and the PDC are indeed adversely affected by volume conduction from multiple sources to the scalp electrodes, in contrast to the claim of Kaminski and Blinowska in their recent opinion article (Kaminski and Blinowska, 2014). Thus, in general direct application of connectivity measures to scalp EEG signals produces less than accurate results and also does not allow their clear interpretation in terms of underlying source dynamics.

EEG effects of zopiclone (7.5 mg), a cyclopyrrolone derivative with hypnotic action, were compared with effects of diazepam (10 mg). Multichannel EEG recordings, double-blind crossover trials with placebo, and oral single doses were used in healthy volunteers. Vigilance-controlled EEG before and after zopiclone (and placebo), and before and after diazepam (and placebo) were analyzed into FFT power spectra. Effects were assessed as placebo-referred pre-post-medication power differences in four frequency bands. Overall statistics showed significant (P < 0.007) global differences between medication effects in the delta frequency band (0.5-3.5 Hz). After zopiclone, fronto-central delta increased bilaterally, whereas after diazepam delta decreased over centro-parietal to right temporo-occipital regions. These spatially different brain electric effects show that different neuronal populations must have become active in response to zopiclone and diazepam.

We present a method for the analysis of electroencephalogram (EEG) signals which has the potential to distinguish between ictal and seizure-free intracranial EEG recordings. This is achieved by analyzing common frequency components in multichannel EEG recordings, using the multivariate empirical mode decomposition (MEMD) algorithm. The mean frequency of the signal is calculated by applying the Hilbert-Huang transform on the resulting intrinsic mode functions (IMFs). It has been shown that the mean frequency estimates for the ictal and seizure-free EEG recordings are statistically different, and hence, can serve as a test statistic to distinguish between the two classes of signals. Simulation results on real world EEG signals support the analysis and demonstrate the potential of the proposed scheme.

The temporal characteristics of seizures in neonatal hypoxic ischemic encephalopathy treated with hypothermia