Transcranial ultrasound stimulation selectively affects cortical neurovascular coupling across neuronal types and LFP frequency bands.

Previous studies have affirmed that transcranial ultrasound stimulation (TUS) can influence cortical neurovascular coupling across low-frequency (0-2 Hz)/high-frequency (160-200 Hz) neural oscillations and hemodynamics. Nevertheless, the selectivity of this coupling triggered by transcranial ultrasound stimulation for spike activity (> 300 Hz) and additional frequency bands (4-150 Hz) remains elusive. We applied transcranial ultrasound stimulation to mice visual cortex while simultaneously recording total hemoglobin concentration, spike activity, and local field potentials. Our findings include (1) a significant increase in coupling strength between spike firing rates of putative inhibitory neurons/putative excitatory neurons and total hemoglobin concentration post-transcranial ultrasound stimulation; (2) an ~ 2.1-fold higher Pearson correlation coefficient between putative inhibitory neurons and total hemoglobin concentration compared with putative excitatory neurons and total hemoglobin concentration (*P < 0.05); (3) a notably greater cross-correlation between putative inhibitory neurons and total hemoglobin concentration than that between putative excitatory neurons and total hemoglobin concentration (*P < 0.05); (4) an enhancement of Pearson correlation coefficient between the relative power of γ frequency band (30-80 Hz), hγ frequency band (80-150 Hz) and total hemoglobin concentration following transcranial ultrasound stimulation (*P < 0.05); and (5) strongest cross-correlation observed at negative delay for θ frequency band, and positive delay for α, ß, γ, hγ frequency bands. Collectively, these results demonstrate that cortical neurovascular coupling evoked by transcranial ultrasound stimulation exhibits selectivity concerning neuronal types and local field potential frequency bands.

Neurophysiological processes reflecting the effects of the immediate past during the dynamic management of actions.

In recent years, there has been many efforts to establish a comprehensive theoretical framework explaining the working mechanisms involved in perception-action integration. This framework stresses the importance of the immediate past on mechanisms supporting perception-action integration. The present study investigates the neurophysiological principles of dynamic perception-action bindings, particularly considering the influence of the immediate history on action control mechanisms. For this purpose, we conducted an established stimulus-response binding paradigm during EEG recording. The SR-task measures stimulus-response binding in terms of accuracy and reaction time differences depending on the degree of feature overlap between conditions. Alpha, beta and theta band activity in distinct time domains as well as associated brain regions were investigated applying time-frequency analyses, a beamforming approach as well as correlation analyses. We demonstrate, for the first time, interdependencies of neuronal processes relying on the immediate past. The reconfiguration of an action seems to overwrite immediately preceding processes. The analyses revealed modulations of theta (TBA), alpha (ABA) and beta band activity (BBA) in connection with fronto-temporal structures supporting the theoretical assumptions of the considered conceptual framework. The close interplay of attentional modulation by gating irrelevant information (ABA) and binding and retrieval processes (TBA) is reflected by the correlation of ABA in all pre-probe-intervals with post-probe TBA. Likewise, the role of BBA in maintaining the event file until retrieval is corroborated by BBA preceding the TBA-associated retrieval of perception-action codes. Following action execution, TBA shifted towards visual association cortices probably reflecting preparation for upcoming information, while ABA and BBA continue to reflect processes of attentional control and information selection for goal-directed behavior. The present work provides the first empirical support for concepts about the neurophysiological mechanisms of dynamic management of perception and action.

Fatigue in Multiple Sclerosis: A Resting-State EEG Microstate Study.

Fatigue affects approximately 80% of people with Multiple Sclerosis (PwMS) and can impact several domains of daily life. However, the neural underpinnings of fatigue in MS are still not completely clear. The aim of our study was to investigate the spontaneous large-scale networks functioning associated with fatigue in PwMS using the EEG microstate approach with a spectral decomposition. Forty-three relapsing-remitting MS patients and twenty-four healthy controls (HCs) were recruited. All participants underwent an administration of Modified Fatigue Impact scale (MFIS) and a 15-min resting-state high-density EEG recording. We compared the microstates of healthy subjects, fatigued (F-MS) and non-fatigued (nF-MS) patients with MS; correlations with clinical and behavioral fatigue scores were also analyzed. Microstates analysis showed six templates across groups and frequencies. We found that in the F-MS emerged a significant decrease of microstate F, associated to the salience network, in the broadband and in the beta band. Moreover, the microstate B, associated to the visual network, showed a significant increase in fatigued patients than healthy subjects in broadband and beta bands. The multiple linear regression showed that the high cognitive fatigue was predicted by both an increase and decrease, respectively, in delta band microstate B and beta band microstate F. On the other hand, higher physical fatigue was predicted with lower occurrence microstate F in beta band. The current findings suggest that in MS the higher level of fatigue might be related to a maladaptive functioning of the salience and visual network.

Low-frequency band noise generated by industrial recirculating aquaculture systems exhibits a greater impact on Micropterus salmoidess.

The effect of underwater noise environment generated by equipment in industrial recirculating aquaculture systems (RAS) on fish is evident. However, different equipment generate noise in various frequency ranges. Understanding the effects of different frequency ranges noise on cultured species is important for optimizing the underwater acoustic environment in RAS. Given this, the effects of underwater noise across various frequency bands in RAS on the growth, physiology, and collective behavior of juvenile largemouth bass (Micropterus salmoides) were comprehensively evaluated here. In this study, three control groups were established: low-frequency noise group (80-1000 Hz, 117 dB re 1µPa RMS), high-frequency noise group (1-19 kHz, 117 dB re 1µPa RMS), and ambient group. During a 30-day experiment, it was found that: 1) industrial RAS noise with different frequency bands all had a certain inhibitory effect on the growth of fish, which the weight gain rate and product of length and depth of caudal peduncle in the ambient group were significantly higher than those of the two noise groups, with the low-frequency noise group showing significantly lower values than the high-frequency noise group; 2) industrial RAS noise had a certain degree of adverse effect on the digestive ability of fish, with the low-frequency noise group being more affected; 3) industrial RAS noise affected the collective feeding behavior of fish, with the collective feeding signal propagation efficiency and feeding intensity of the noise groups being significantly lower than those of the ambient group, and the high-frequency noise group performing better than the low-frequency noise group as a whole therein. From the above, the underwater noise across different frequency bands generated by equipment operation in industrial RAS both had an impact on juvenile largemouth bass, with the low-frequency noise group being more severely affected.

Refinement of Different Frequency Bands of Geomagnetic Vertical Intensity Polarization Anomalies before M > 5.5 Earthquakes.

Geomagnetic vertical intensity polarization is a method with a clear mechanism, mature processing methods, and a strong ability to extract anomalous information in the quantitative analysis of seismogenic geomagnetic disturbances. The existing analyses of geomagnetic vertical intensity polarization are all based on the 5~100 s frequency band without refinement of the partitioning process. Although many successful results have been obtained, there are still two problems in the process of extracting anomalies: the geomagnetic anomalies that satisfy the determination criteria are still high in occurrence frequency; and the anomalies are distributed over too large an area in space, which leads to difficulties in determining the location of the epicenter. In this study, based on observations from western China, where fluxgate observation points are positioned in areas with frequent, densely distributed medium-strength earthquakes, we refined the frequency bands of geomagnetic vertical intensity polarization, recalculated the spatial and temporal evolution characteristics of geomagnetic disturbances before earthquakes, and improved the crossover frequency anomaly prediction index while promoting the application of the method in earthquake forecasting.

Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses.

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Effect of sugammadex on processed EEG parameters in patients undergoing robot-assisted radical prostatectomy.

BACKGROUND: Sugammadex has been associated with increases in the bispectral index (BIS). We evaluated the effects of sugammadex administration on quantitative electroencephalographic (EEG) and electromyographic (EMG) measures. METHODS: We performed a prospective observational study of adult male patients undergoing robot-assisted radical prostatectomy. All patients received a sevoflurane-based general anaesthetic and a continuous infusion of rocuronium, which was reversed with 2 mg kg-1 of sugammadex i.v. BIS, EEG, and EMG measures were captured with the BIS Vista™ monitor. RESULTS: Twenty-five patients were included in this study. Compared with baseline, BIS increased at 4-6 min (ß coefficient: 3.63; 95% confidence interval [CI]: 2.22-5.04; P<0.001), spectral edge frequency 95 (SEF95) increased at 2-4 min (ß coefficient: 0.29; 95% CI: 0.05-0.52; P=0.016) and 4-6 min (ß coefficient: 0.71; 95% CI: 0.47-0.94; P<0.001), and EMG increased at 4-6 min (ß coefficient: 1.91; 95% CI: 1.00-2.81; P<0.001) after sugammadex administration. Compared with baseline, increased beta power was observed at 2-4 min (ß coefficient: 93; 95% CI: 1-185; P=0.046) and 4-6 min (ß coefficient: 208; 95% CI: 116-300; P<0.001), and decreased delta power was observed at 4-6 min (ß coefficient: -526.72; 95% CI: -778 to -276; P<0.001) after sugammadex administration. Neither SEF95 nor frequency band data analysis adjusted for EMG showed substantial differences. None of the patients showed clinical signs of awakening. CONCLUSIONS: After neuromuscular block reversal with 2 mg kg-1 sugammadex, BIS, SEF95, EMG, and beta power showed small but statistically significant increases over time, while delta power decreased.

Neuronal Electrical Ongoing Activity as Cortical Areas Signature: An Insight from MNI Intracerebral Recording Atlas.

The time course of the neuronal activity in the brain network, the neurodynamics, reflects the structure and functionality of the generating neuronal pools. Here, using the intracranial stereo-electroencephalographic (sEEG) recordings of the public Montreal Neurological Institute (MNI) atlas, we investigated the neurodynamics of primary motor (M1), somatosensory (S1) and auditory (A1) cortices measuring power spectral densities (PSD) and Higuchi fractal dimension (HFD) in the same subject (M1 vs. S1 in 16 subjects, M1 vs. A1 in 9, S1 vs. A1 in 6). We observed specific spectral features in M1, which prevailed above beta band, S1 in the alpha band, and A1 in the delta band. M1 HFD was higher than S1, both higher than A1. A clear distinction of neurodynamics properties of specific primary cortices supports the efforts in cortical parceling based on this expression of the local cytoarchitecture and connectivity. In this perspective, we selected within the MNI intracortical database a first set of primary motor, somatosensory and auditory cortices' representatives to query in recognizing ongoing patterns of neuronal communication. Potential clinical impact stands primarily in exploiting such exchange patterns to enhance the efficacy of neuromodulation intervention to cure symptoms secondary to neuronal activity unbalances.

Frequency-dependent genetic modulation of neuronal oscillations: a combined transcriptome and resting-state functional MRI study.

Neuronal oscillations within certain frequency bands are assumed to associate with specific neural processes and cognitive functions. To examine this hypothesis, transcriptome-neuroimaging spatial correlation analysis was applied to resting-state functional magnetic resonance imaging data from 793 healthy individuals and gene expression data from the Allen Human Brain Atlas. We found that expression measures of 336 genes were correlated with fractional amplitude of low-frequency fluctuations (fALFF) in the slow-4 band (0.027-0.073 Hz), whereas there were no expression-fALFF correlations for the other frequency bands. Furthermore, functional enrichment analyses showed that these slow-4 fALFF-related genes were mainly enriched for ion channel, synaptic function, and neuronal system as well as many neuropsychiatric disorders. Specific expression analyses demonstrated that these genes were specifically expressed in brain tissue, in neurons, and during the late stage of cortical development. Concurrently, the fALFF-related genes were linked to multiple behavioral domains, including dementia, attention, and emotion. In addition, these genes could construct a protein-protein interaction network supported by 30 hub genes. Our findings of a frequency-dependent genetic modulation of spontaneous neuronal activity may support the concept that neuronal oscillations within different frequency bands capture distinct neurobiological processes from the perspective of underlying molecular mechanisms.

Pain chronification impacts whole-brain functional connectivity in women with hip osteoarthritis during pain stimulation.

OBJECTIVE: Previous neuroimaging studies have shown that patients with chronic pain display altered functional connectivity across distributed brain areas involved in the processing of nociceptive stimuli. The aim of the present study was to investigate how pain chronification modulates whole-brain functional connectivity during evoked clinical and tonic pain. METHODS: Patients with osteoarthritis of the hip (n = 87) were classified into 3 stages of pain chronification (Grades I-III, Mainz Pain Staging System). Electroencephalograms were recorded during 3 conditions: baseline, evoked clinical hip pain, and tonic cold pain (cold pressor test). The effects of both factors (recording condition and pain chronification stage) on the phase-lag index, as a measure of neuronal connectivity, were examined for different frequency bands. RESULTS: In women, we found increasing functional connectivity in the low-frequency range (delta, 0.5-4 Hz) across pain chronification stages during evoked clinical hip pain and tonic cold pain stimulation. In men, elevated functional connectivity in the delta frequency range was observed in only the tonic cold pain condition. CONCLUSIONS: Across pain chronification stages, we found that widespread cortical networks increase their synchronization of delta oscillations in response to clinical and experimental nociceptive stimuli. In view of previous studies relating delta oscillations to salience detection and other basic motivational processes, our results hint at these mechanisms playing an important role in pain chronification, mainly in women.

Neural signatures of heterogeneity in risk-taking and strategic consistency.

People display a high degree of heterogeneity in risk-taking behaviour, but this heterogeneity remains poorly understood. Here, we use a neural trait approach to examine if task-independent, brain-based differences can help uncover the sources of heterogeneity in risky decision-making. We extend prior research in two key ways. First, we disentangled risk-taking and strategic consistency using novel measures afforded by the Balloon Analogue Risk Task. Second, we applied a personality neuroscience framework to explore why personality traits are typically only weakly related to risk-taking behaviour. We regressed participants' (N = 104) source localized resting-state electroencephalographic activity on risk-taking and strategic consistency. Results revealed that higher levels of resting-state delta-band current density (reflecting reduced cortical activation) in the left dorsal anterior cingulate cortex and the left dorsolateral prefrontal cortex were associated with increased risk-taking and decreased strategic consistency, respectively. These results suggest that heterogeneity in risk-taking behaviour is associated with neural dispositions related to sensitivity to the risk of loss, whereas heterogeneity in strategic consistency is associated with neural dispositions related to strategic decision-making. Finally, extraversion, neuroticism, openness, and self-control were broadly associated with both of the identified neural traits, which in turn mediated indirect associations between personality traits and behavioural measures. These results provide an explanation for the weak direct relationships between personality traits and risk-taking behaviour, supporting a personality neuroscience framework of traits and decision-making.

Projections of IoT Applications in Colombia Using 5G Wireless Networks.

Wireless technologies are increasingly relevant in different activities and lines of the economy, as well as in the daily life of people and companies. The advent of fifth generation networks (5G) implies a promising synergy with the Internet of Things (IoT), allowing for more automations in production processes and an increase in the efficiency of information transmission, managing to improve the efficiency in decision-making through tools such as big data and artificial intelligence. This article presents a description of the 5G implementation process in Colombia, as well as a revision of opportunities when combining with IoT in featured sectors of the departmental development plans, such as agriculture, tourism, health, the environment, and industry. Results shows that the startup of 5G in Colombia has been a slow process, but there are comparisons with similar procedures in other developed countries. Additionally, we present examples of 5G and IoT applications which can be promoted in Colombia, aimed at improving the quality of life of their habitants and promoting economic development.

Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition.

Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the "SEED IV" data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the "DEAP" data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance.

Transcranial alternating current stimulation in the theta band but not in the delta band modulates the comprehension of naturalistic speech in noise.

Auditory cortical activity entrains to speech rhythms and has been proposed as a mechanism for online speech processing. In particular, neural activity in the theta frequency band (4-8 â€‹Hz) tracks the onset of syllables which may aid the parsing of a speech stream. Similarly, cortical activity in the delta band (1-4 â€‹Hz) entrains to the onset of words in natural speech and has been found to encode both syntactic as well as semantic information. Such neural entrainment to speech rhythms is not merely an epiphenomenon of other neural processes, but plays a functional role in speech processing: modulating the neural entrainment through transcranial alternating current stimulation influences the speech-related neural activity and modulates the comprehension of degraded speech. However, the distinct functional contributions of the delta- and of the theta-band entrainment to the modulation of speech comprehension have not yet been investigated. Here we use transcranial alternating current stimulation with waveforms derived from the speech envelope and filtered in the delta and theta frequency bands to alter cortical entrainment in both bands separately. We find that transcranial alternating current stimulation in the theta band but not in the delta band impacts speech comprehension. Moreover, we find that transcranial alternating current stimulation with the theta-band portion of the speech envelope can improve speech-in-noise comprehension beyond sham stimulation. Our results show a distinct contribution of the theta- but not of the delta-band stimulation to the modulation of speech comprehension. In addition, our findings open up a potential avenue of enhancing the comprehension of speech in noise.

Acoustic analysis and detection of pharyngeal fricative in cleft palate speech using correlation of signals in independent frequency bands and octave spectrum prominent peak.

BACKGROUND: Pharyngeal fricative is one typical compensatory articulation error of cleft palate speech. It passively influences daily communication for people who suffer from it. The automatic detection of pharyngeal fricatives in cleft palate speech can provide information for clinical doctors and speech-language pathologists to aid in diagnosis. RESULTS: This paper proposes two features (CSIFs: correlation of signals in independent frequency bands; OSPP: octave spectrum prominent peak) to detect pharyngeal fricative speech. CSIFs feature is proposed to detect the distribution characteristics of frequency components in pharyngeal fricative speech caused by the changed place of articulation and movement of articulators. While OSPP is presented to reflect the concentration degree of prominent peak which is closely related to the place of articulation in pharyngeal fricative, both features are investigated to relate to the altered production process of pharyngeal fricative. To evaluate the capability of these two features to detect pharyngeal fricative, we collected a speech database covering all the types of initial consonants in which pharyngeal fricatives occur. In this detection task, the classifier used to discriminate pharyngeal fricative speech and normal speech is based on ensemble learning. CONCLUSION: The detection accuracy obtained with CSIFs and OSPP features ranges from 83.5 to 84.5% and from 85 to 87%, respectively. When these two features are combined, the detection accuracy for pharyngeal fricative speech ranges from 88 to 89%, with an AUC (area under the receiver operating characteristic curve) value of 93%.

Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer's Disease: An Analysis Based on Frequency Bands.

We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer's Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and ß ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.

Dynamic changes in electroencephalogram spectral power with varying apnea duration in older adults.

Sleep apnea elicits brain and physiological changes and its duration varies across the night. This study investigates the changes in the relative powers in electroencephalogram (EEG) frequency bands before and at apnea termination and as a function of apnea duration. The analysis was performed on 30 sleep records (375 apnea events) of older adults diagnosed with sleep apnea. Power spectral analysis centered on two 10-s EEG epochs, before apnea termination (BAT) and after apnea termination (AAT), for each apnea event. The relative power changes in EEG frequency bands were compared with changes in apnea duration, defined as Short (between 10 and 20 s), Moderate (between 20 and 30 s) and Long (between 30 and 40 s). A significant reduction in EEG relative powers for lower frequency bands of alpha and sigma were observed for the Long compared to the Moderate and Short apnea duration groups at BAT, and reduction in relative theta, alpha and sigma powers for the Long compared to the Moderate and Short groups at AAT. The proportion of apnea events showed a significantly decreased trend with increased apnea duration for non-rapid eye movement sleep but not rapid eye movement sleep. The proportion of central apnea events decreased with increased apnea duration, but not obstructive episodes. The findings suggest EEG arousal occurred both before and at apnea termination and these transient arousals were associated with a reduction in relative EEG powers of the low-frequency bands: theta, alpha and sigma. The clinical implication is that these transient EEG arousals, without awakenings, are protective of sleep. Further studies with large datasets and different age groups are recommended.

EEG electrode selection for person identification thru a genetic-algorithm method.

New biometric identification techniques are continually being developed to meet various applications. Electroencephalography (EEG) signals may provide a reasonable option for this type of identification due its unique features that overcome the lacks of other common methods. Currently, however, the processing load for such signals requires considerable time and labor. New methods and algorithms have attempted to reduce EEG processing time, including a reduction of the number of electrodes and segmenting the EEG data into its typical frequency bands. This work complements other efforts by proposing a genetic algorithm to reduce the number of necessary electrodes for measurements by EEG devices. Using a public EEG dataset of 109 subjects who underwent relaxation with eye-open and eye-closed stimuli, we aimed to determine the minimum set of electrodes required for optimum identification accuracy in each EEG sub-band of both stimuli. The results were encouraging and it was possible to accurately identify a subject using about 10 out of 64 electrodes. Moreover, higher frequency bands required a fewer number of electrodes for identification compared with lower frequency bands.

Quantification and statistical analysis of the transient visual evoked potential to a contrast-reversing pattern: A frequency-domain approach.

Visual function is often assessed by recording transient visual evoked potentials to contrast reversal of spatial patterns (tVEP-CR). This technique relies on measurements of amplitudes and peak times of a few points in the time-domain waveform, which require subjective selection of appropriate time points in a possibly noisy waveform and ignores much of the informational content in the response. Here, we introduce a set of frequency-domain measures that capture the full content of the response. Magnitude-squared coherence is used to determine the significance and reliability of magnitude measures; estimates of time delay are based on frequency-domain phase measures. In Study 1, extensive testing of a small number of observers revealed response details, and in Study 2, testing of a larger sample verified the novel frequency-domain measures and demonstrated the validity of a short-duration technique to produce reliable tVEP-CRs. In addition, Study 2 revealed adaptation effects present under prolonged stimulation conditions. Principal component analyses provided evidence for six distinct frequency mechanisms, and comparisons with time-domain measures indicated that power in high-frequency bands may be used as objective measures of excitatory input to visual cortex. A middle-frequency band captures the major peaks in the tVEP-CR waveform, and its power is highly correlated with the standard peak-to-trough amplitude measure. These novel frequency-domain indices may serve as more precise and powerful tools to assess visual function in healthy and diseased states.