Seizure-onset mapping based on time-variant multivariate functional connectivity analysis of high-dimensional intracranial EEG : a Kalman filter approach

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (< 60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach

Affective Brain-Computer Interfaces Neuroscientific Approaches to Affect Detection

The brain is involved in the registration, evaluation, and representation of emotional events, and in the subsequent planning and execution of adequate actions. Novel interface technologies – so-called affective brain-computer interfaces (aBCI) - can use this rich neural information, occurring in response to affective stimulation, for the detection of the affective state of the user. This chapter gives an overview of the promises and challenges that arise from the possibility of neurophysiology-based affect detection, with a special focus on electrophysiological signals. After outlining the potential of aBCI relative to other sensing modalities, the reader is introduced to the neurophysiological and neurotechnological background of this interface technology. Potential application scenarios are situated in a general framework of brain-computer interfaces. Finally, the main scientific and technological challenges that have to be solved on the way toward reliable affective brain-computer interfaces are discussed

The efficacy of transcranial current stimulation techniques to modulate resting-state EEG, to affect vigilance and to promote sleepiness

Transcranial Current Stimulations (tCSs) are non-invasive brain stimulation techniques which modulate cortical excitability and spontaneous brain activity by the application of weak electric currents through the scalp, in a safe, economic, and well-tolerated manner. The direction of the cortical effects mainly depend on the polarity and the waveform of the applied current. The aim of the present work is to provide a broad overview of recent studies in which tCS has been applied to modulate sleepiness, sleep, and vigilance, evaluating the efficacy of different stimulation techniques and protocols. In recent years, there has been renewed interest in these stimulations and their ability to affect arousal and sleep dynamics. Furthermore, we critically review works that, by means of stimulating sleep/vigilance patterns, in the sense of enhancing or disrupting them, intended to ameliorate several clinical conditions. The examined literature shows the efficacy of tCSs in modulating sleep and arousal pattern, likely acting on the top-down pathway of sleep regulation. Finally, we discuss the potential application in clinical settings of this neuromodulatory technique as a therapeutic tool for pathological conditions characterized by alterations in sleep and arousal domains and for sleep disorders per se

Monitoring the Depth of Anaesthesia

One of the current challenges in medicine is monitoring the patients’ depth of general anaesthesia (DGA). Accurate assessment of the depth of anaesthesia contributes to tailoring drug administration to the individual patient, thus preventing awareness or excessive anaesthetic depth and improving patients’ outcomes. In the past decade, there has been a significant increase in the number of studies on the development, comparison and validation of commercial devices that estimate the DGA by analyzing electrical activity of the brain (i.e., evoked potentials or brain waves). In this paper we review the most frequently used sensors and mathematical methods for monitoring the DGA, their validation in clinical practice and discuss the central question of whether these approaches can, compared to other conventional methods, reduce the risk of patient awareness during surgical procedures

Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

Comparison of electroencephalographic spectra from normal children to spectra from children with reading and general academic problems

INTRODUCTION: Reading problems affect a significant proportion of children. Causes range from attention deficit disorders to disruption of information processing. Attempts have been made to classify reading disorders into diagnostic categories such as dyslexia, but these classifications have been unsatisfactory for a number of reasons. In this project, an attempt was made to classify children with reading difficulties based upon their electroencephalographic (EEG) spectra. SUBJECTS: Sixty-four subjects participated in this project; all were students in grades 3 through 8 at local schools. On the basis of standardized academic test results, 33 subjects were classified as normal (no score below the 40th percentile), 14 were classified as having an isolated reading problem (a score below the 40th percentile on reading and/or language, and scores above the 40th percentile on math and/or general ability), and 17 were classified as having general problems (a score below the 40th percentile on reading and/or language, and a score below the 40th percentile on math and/or general ability). METHODS: EEG spectra were recorded from electrode sites OZ, PZ, and CZ during the fifth minute of five tasks : sitting quietly, listening to story, reading a story, performing mental arithmetic, and copy forms. Spectra were divided into theta, alpha, and beta bands. RESULTS: Amplitudes of the EEG bands were compared for the three subject groups. No statistically significant differences between the groups were found. Several individual subjects had spectra suggestive of attention deficit disorder. CONCLUSIONS: Analysis of EEG spectra did not provide a useful means for differentiating the three groups of subjects used in this study. It is possible that recording at different electrode sites or using different stimulus conditions might have allowed better differentiation of the subject groups

Comparison of electroencephalographic spectra from normal children to spectra from children with reading and general academic problems

Comparison of electroencephalographic spectra from normal children to spectra from children with reading and general academic problem

Clinical applications of EEG power spectra aperiodic component analysis: A mini-review

Objective: The present mini-review summarizes recent clinical findings related to the analysis of the aperiodic component of EEG (electroencephalographic) power spectra, making them quickly accessible to medical specialists and health researchers, with the aim of boosting related research. Methods: Based on our experience about clinicians’ literature-searching, we queried the PubMed database with terms related to EEG power spectra aperiodic component analysis and selected clinical studies that referenced such terms in the title/abstract, and were published in the last five years. Results: A total of 11 journal articles, dealing with 9 different neurologic and psychiatric conditions published between 1st January 2016 – April 1st 2021, were surveyed. Conclusions: All the reviewed studies focused on exploring the pathophysiological significance of the aperiodic component and its correlation with disease presence, stage, and severity. Despite the heterogeneity of pathologies, it was possible to cluster most of them according to the mechanism underlying slope alterations, namely hypo-/hyper-excitability. It was also possible to identify some counterintuitive findings, probably related to compensation mechanisms of disease-specific neurophysiological alterations. Significance: All the findings seem to support the role of the aperiodic activity as index of excitation/inhibition balance, with promising clinical applications that might challenge the traditional approach to pathologies diagnosis/treatment/follow-up

Neural Underpinnings of Walking Under Cognitive and Sensory Load: A Mobile Brain/Body Imaging Approach

Dual-task walking studies, in which individuals engage in an attentionally-demanding task while walking, have provided indirect evidence via behavioral and biomechanical measures, of the recruitment of higher-level cortical resources during gait. Additionally, recent EEG and imaging (PET, fNIRS) studies have revealed direct neurophysiological evidence of cortical contributions to steady-state walking. However, there remains a lack of knowledge regarding the underlying neural mechanisms involved in the allocation of cortical resources while walking under increased load. This dissertation presents three experiments designed to provide a greater understanding of the cortical dynamics implicated in processing load (top-down or bottom-up) during locomotion. Furthermore, we seek to investigate age-related differences in these neural pathways. These studies were conducted using an innovative EEG-based Mobile Brain/Body Imaging (MoBI) approach, combining high-density EEG, foot force sensors and 3D body motion capture as participants walked on a treadmill. The first study employed a Go/No-Go response inhibition task to evaluate the long-term test-retest reliability of two cognitively-evoked event-related potentials (ERPs), the earlier N2 and the later P3. Acceptable levels of reliability were found, according to the intraclass correlation coefficient (ICC), and these were similar across sitting and walking conditions. Results indicate that electrocortical signals obtained during walking are stable indices of neurophysiological function. The aim of the second study was to characterize age-related changes in gait and in the allocation of cognitive control under single vs. dual-task load. For young adults, we observed significant modulations as a result of increased task load for both gait (longer stride time) and for ERPs (decreased N2 amplitude and P3 latency). In contrast, older adults exhibited costs in the cognitive domain (reduced accuracy performance), engaged in a more stereotyped pattern of walking, and showed a general lack of ERP modulation while walking under increased load, all of which may indicate reduced flexibility in resource allocation across tasks. Finally, the third study assessed the effects of sensory (optic flow and visual perturbations) and cognitive load (Go/No-Go task) manipulations on gait and cortical neuro-oscillatory activity in young adults. While walking under increased load, participants adopted a more conservative pattern of gait by taking shorter and wider strides, with cognitive load in particular associated with reduced motor variability. Using an Independent Component Analysis (ICA) and dipole-fitting approach, neuro-oscillatory activity was then calculated from eight source-localized clusters of Independent Components (ICs). Significant modulations in average spectral power in the theta (3-7Hz), alpha (8-12Hz), beta (13-30Hz), and gamma (31-45Hz) frequency bands were observed over occipital, parietal and frontal clusters of ICs, as a function of optic flow and task load. Overall, our findings demonstrate the reliability and feasibility of the MoBI approach to assess electrocortical activity in dual-task walking situations, and may be especially relevant to older adults who are less able to flexibly adjust to ongoing cognitive and sensory demands while walking