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Background: With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human–computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human–computer interactions. Methods: This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users' unconscious cognitive processing mechanisms during service failures. Results: The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. Conclusions: This study found that stance attribution explanations profoundly affect users' mental models of AI, which determine their responses to service failure. [ABSTRACT FROM AUTHOR]

Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.

Temporal dynamics of local cortical rhythms during acute pain remain largely unknown. The current study used a novel approach based on transcranial magnetic stimulation combined with electroencephalogram (TMS‐EEG) to investigate evoked‐oscillatory cortical activity during acute pain. Motor (M1) and dorsolateral prefrontal cortex (DLPFC) were probed by TMS, respectively, to record oscillatory power (event‐related spectral perturbation and relative spectral power) and phase synchronization (inter‐trial coherence) by 63 EEG channels during experimentally induced acute heat pain in 24 healthy participants. TMS‐EEG was recorded before, during, and after noxious heat (acute pain condition) and non‐noxious warm (Control condition), delivered in a randomized sequence. The main frequency bands (α, β1, and β2) of TMS‐evoked potentials after M1 and DLPFC stimulation were recorded close to the TMS coil and remotely. Cold and heat pain thresholds were measured before TMS‐EEG. Over M1, acute pain decreased α‐band oscillatory power locally and α‐band phase synchronization remotely in parietal–occipital clusters compared with non‐noxious warm (all p <.05). The remote (parietal–occipital) decrease in α‐band phase synchronization during acute pain correlated with the cold (p =.001) and heat pain thresholds (p =.023) and to local (M1) α‐band oscillatory power decrease (p =.024). Over DLPFC, acute pain only decreased β1‐band power locally compared with non‐noxious warm (p =.015). Thus, evoked‐oscillatory cortical activity to M1 stimulation is reduced by acute pain in central and parietal–occipital regions and correlated with pain sensitivity, in contrast to DLPFC, which had only local effects. This finding expands the significance of α and β band oscillations and may have relevance for pain therapies. [ABSTRACT FROM AUTHOR]

Background: With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human–computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human–computer interactions. Methods: This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users’ unconscious cognitive processing mechanisms during service failures. Results: The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. Conclusions: This study found that stance attribution explanations profoundly affect users’ mental models of AI, which determine their responses to service failure.

Background: Fluctuating neuronal network states influence brain responses to transcranial magnetic stimulation (TMS). Our previous studies revealed that transient spontaneous bihemispheric brain states in the EEG, driven by oscillatory power, information flow and regional domination, modify cortical EEG responses to TMS. However, the impact of ongoing fluctuations of large-scale brain network states on TMS-EEG responses has not been explored. Objectives: To determine the effects of large-scale brain network states on TMS-EEG responses. Methods: Resting-state EEG and structural MRI from 24 healthy subjects were recorded to infer large-scale brain states. TMS-EEG was acquired with TMS at state-related targets, identified by the spatial distribution of state activation power from resting-state EEG. TMS-induced oscillations were measured by event-related spectral perturbations (ERSPs), and classified with respect to the brain states preceding the TMS pulses. State-locked ERSPs with TMS at specific state-related targets and during state activation were compared with state-unlocked ERSPs. Results: Intra-individual comparison of ERSPs by threshold free cluster enhancement (TFCE) revealed that posterior and visual state-locked TMS, respectively, increased beta and alpha responses to TMS of parietal and occipital cortex compared to state-unlocked TMS. Also, the peak frequencies of ERSPs were increased with state-locked TMS. In addition, inter-individual correlation analyses revealed that posterior and visual state-locked TMS-induced oscillation power (ERSP clusters identified by TFCE) positively correlated with state-dependent oscillation power preceding TMS. Conclusions: Spontaneous transient large-scale brain network states modify TMS-induced natural oscillations in specific brain regions. This significantly extends our knowledge on the critical importance of instantaneous state on explaining the brain's varying responsiveness to external perturbation.

Fluctuating neuronal network states influence brain responses to transcranial magnetic stimulation (TMS). Our previous studies revealed that transient spontaneous bihemispheric brain states in the EEG, driven by oscillatory power, information flow and regional domination, modify cortical EEG responses to TMS. However, the impact of ongoing fluctuations of large-scale brain network states on TMS-EEG responses has not been explored. To determine the effects of large-scale brain network states on TMS-EEG responses. Resting-state EEG and structural MRI from 24 healthy subjects were recorded to infer large-scale brain states. TMS-EEG was acquired with TMS at state-related targets, identified by the spatial distribution of state activation power from resting-state EEG. TMS-induced oscillations were measured by event-related spectral perturbations (ERSPs), and classified with respect to the brain states preceding the TMS pulses. State-locked ERSPs with TMS at specific state-related targets and during state activation were compared with state-unlocked ERSPs. Intra-individual comparison of ERSPs by threshold free cluster enhancement (TFCE) revealed that posterior and visual state-locked TMS, respectively, increased beta and alpha responses to TMS of parietal and occipital cortex compared to state-unlocked TMS. Also, the peak frequencies of ERSPs were increased with state-locked TMS. In addition, inter-individual correlation analyses revealed that posterior and visual state-locked TMS-induced oscillation power (ERSP clusters identified by TFCE) positively correlated with state-dependent oscillation power preceding TMS. Spontaneous transient large-scale brain network states modify TMS-induced natural oscillations in specific brain regions. This significantly extends our knowledge on the critical importance of instantaneous state on explaining the brain's varying responsiveness to external perturbation. • Effects of large-scale brain network states on TMS-EEG responses were determined • Posterior state-locked TMS increased beta responses to TMS of parietal cortex • Visual state-locked TMS increased alpha responses to TMS of occipital cortex • State-locked TMS increased peak frequencies of event-related spectral perturbations • Large-scale brain network states modify TMS-induced natural oscillations [ABSTRACT FROM AUTHOR]

Introduction: The motor‐related bioelectric brain activity of healthy young and old subjects was studied to understand the effect of aging on motor execution. A visually cued finger tapping movement paradigm and high‐density EEG were used to examine the time and frequency characteristics. Methods: Twenty‐two young and 22 healthy elderly adults participated in the study. Repeated trials of left and right index finger movements were recorded with a 128‐channel EEG. Event‐Related Spectral Perturbation (ERSP), Inter Trial Coherence (ITC), and Functional Connectivity were computed and compared between the age groups. Results: An age‐dependent theta and alpha band ERSP decrease was observed over the frontal–midline area. Decrease of beta band ERSP was found over the ipsilateral central–parietal regions. Significant ITC differences were found in the delta and theta bands between old and young subjects over the contralateral parietal–occipital areas. The spatial extent of increased ITC values was larger in old subjects. The movement execution of older subjects showed higher global efficiency in the delta and theta bands, and higher local efficiency and node strengths in the delta, theta, alpha, and beta bands. Conclusion: As functional compensation of aging, elderly motor networks involve more nonmotor, parietal–occipital, and frontal areas, with higher global and local efficiency, node strength. ERSP and ITC changes seem to be sensitive and complementary biomarkers of age‐related motor execution. [ABSTRACT FROM AUTHOR]

Driver's cognitive workload has an important impact on driving safety. This paper carries out an on-road experiment to analyse the impact from three innovative aspects: significance analysis of electroencephalogram (EEG) under different cognitive workloads, distribution of EEG maps with different frequency signals and influence of different cognitive workloads on driving safety based on EEG. First, the EEG signals are processed and four frequencies of delta, theta, alpha and beta are obtained. Then, the time-frequency transform and power spectral density calculation are carried out by short-time Fourier to study the correlation of each frequency signal of different workload states, as well as the distribution pattern of the EEG topographic map. Finally, the time and space energy and phase changes in each cognitive task event are studied through event-related spectral perturbation and inter-trial coherence. Results show the difference between left and right brains, as well as the resource occupancy trends of the monitor, perception, visual and auditory channels in different driving conditions. Results also demonstrate that the increase in cognitive workloads will directly affect driving safety. Changes in cognitive workload have different effects on brain signals, and this paper can provide a theoretical basis for improving driving safety under different cognitive workloads. Mastering the EEG characteristics of signals can provide more targeted supervision and safety warnings for the driver. [ABSTRACT FROM AUTHOR]

Abstract Introduction The motor‐related bioelectric brain activity of healthy young and old subjects was studied to understand the effect of aging on motor execution. A visually cued finger tapping movement paradigm and high‐density EEG were used to examine the time and frequency characteristics. Methods Twenty‐two young and 22 healthy elderly adults participated in the study. Repeated trials of left and right index finger movements were recorded with a 128‐channel EEG. Event‐Related Spectral Perturbation (ERSP), Inter Trial Coherence (ITC), and Functional Connectivity were computed and compared between the age groups. Results An age‐dependent theta and alpha band ERSP decrease was observed over the frontal–midline area. Decrease of beta band ERSP was found over the ipsilateral central–parietal regions. Significant ITC differences were found in the delta and theta bands between old and young subjects over the contralateral parietal–occipital areas. The spatial extent of increased ITC values was larger in old subjects. The movement execution of older subjects showed higher global efficiency in the delta and theta bands, and higher local efficiency and node strengths in the delta, theta, alpha, and beta bands. Conclusion As functional compensation of aging, elderly motor networks involve more nonmotor, parietal–occipital, and frontal areas, with higher global and local efficiency, node strength. ERSP and ITC changes seem to be sensitive and complementary biomarkers of age‐related motor execution.

Increased exercise loads, as observed in elite athletes, seem to modulate the subjective pain perception in healthy subjects. The combination of electroencephalography (EEG) and standardized noxious stimulation can contribute to an objective assessment of the somatosensory stimulus processing. We assessed the subjective pain ratings and the electroencephalogram (EEG)-based response after standardized noxious mechanical and thermal stimuli as well as during conditioned pain modulation (CPM) in 26 elite endurance athletes and compared them to 26 recreationally active controls. Elite endurance athletes had consistently stronger somatosensory responses in the EEG to both mechanical and thermal noxious stimuli than the control group. We observed no significant group differences in the subjective pain ratings, which may have been influenced by our statistics and choice of stimuli. The CPM testing revealed that our conditioning stimulus modulated the subjective pain perception only in the control group, whereas the EEG indicated a modulatory effect of the conditioning stimulus on the spectral response only in the athletes group. We conclude that a higher activation in the cortical regions that process nociceptive information may either be an indicator for central sensitization or an altered stimulus salience in the elite endurance athletes' group. Our findings from our CPM testing were limited by our methodology. Further longitudinal studies are needed to examine if exercise-induced changes in the somatosensory system might have a critical impact on the long-term health of athletes. [ABSTRACT FROM AUTHOR]

Auditory steady-state responses (ASSRs) are recurrent neural activities entrained to regular cyclic auditory stimulation. ASSRs are altered in individuals with schizophrenia, and may be related to hypofunction of the N-methyl- D -aspartate (NMDA) glutamate receptor. Noncompetitive NMDA receptor antagonists, including ketamine, have been used in ASSR studies of rodent models of schizophrenia. Although animal studies using non-human primates are required to complement rodent studies, the effects of ketamine on ASSRs are unknown in intact awake non-human primates. In this study, after administration of vehicle or ketamine, click trains at 20–83.3 Hz were presented to elicit ASSRs during recording of electroencephalograms in intact, awake macaque monkeys. The results indicated that ASSRs quantified by event-related spectral perturbation and inter-trial coherence were maximal at 83.3 Hz after vehicle administration, and that ketamine reduced ASSRs at 58.8 and 83.3 Hz, but not at 20 and 40 Hz. The present results demonstrated a reduction of ASSRs by the NMDA receptor antagonist at optimal frequencies with maximal responses in intact, awake macaques, comparable to ASSR reduction in patients with schizophrenia. These findings suggest that ASSR can be used as a neurophysiological biomarker of the disturbance of gamma-oscillatory neural circuits in this ketamine model of schizophrenia using intact, awake macaques. Thus, this model with ASSRs would be useful in the investigation of human brain pathophysiology as well as in preclinical translational research. • ASSRs are altered in schizophrenia, possibly due to NMDA receptor hypofunction. • ASSRs in response to click trains at 20–83.3 Hz were recorded in awake monkeys. • Ketamine, NMDA receptor antagonist, reduced ASSRs at 58.8 and 83.3 Hz. • Ketamine model mimics gamma-oscillatory deficits of schizophrenia in monkeys. • This model may be useful in preclinical translational research of schizophrenia. [ABSTRACT FROM AUTHOR]

• Anticipated strong and weak distractors were presented during retention. • Distractor strength modulated frontal theta power and occipito-temporal alpha power. • Frontal and bottom-up theta connectivity increased with distractor strength. • Top-down alpha connectivity during retention increased with distractor strength. Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding. [ABSTRACT FROM AUTHOR]

Individuals at clinical high risk (CHR) for psychosis exhibit a compromised mismatch negativity (MMN) response, which indicates dysfunction of pre‐attentive deviance processing. Event‐related potential and time‐frequency (TF) information, in combination with clinical and cognitive profiles, may provide insight into the pathophysiology and psychopathology of the CHR stage and predict the prognosis of CHR individuals. A total of 92 individuals with CHR were recruited and followed up regularly for up to 3 years. Individuals with CHR were classified into three clinical subtypes demonstrated previously, specifically 28 from Cluster 1 (characterized by extensive negative symptoms and cognitive deficits), 31 from Cluster 2 (characterized by thought and behavioral disorganization, with moderate cognitive impairment), and 33 from Cluster 3 (characterized by the mildest symptoms and cognitive deficits). Auditory MMN to frequency and duration deviants was assessed. The event‐related spectral perturbation (ERSP) and inter‐trial coherence (ITC) were acquired using TF analysis. Predictive indices for remission were identified using logistic regression analyses. As expected, reduced frequency MMN (fMMN) and duration MMN (dMMN) responses were noted in Cluster 1 relative to the other two clusters. In the TF analysis, Cluster 1 showed decreased theta and alpha ITC in response to deviant stimuli. The regression analyses revealed that dMMN latency and alpha ERSP to duration deviants, theta ITC to frequency deviants and alpha ERSP to frequency deviants, and fMMN latency were significant MMN predictors of remission for the three clusters. MMN variables outperformed behavioral variables in predicting remission of Clusters 1 and 2. Our findings indicate relatively disrupted automatic auditory processing in a certain CHR subtype and a close affinity between these electrophysiological indexes and clinical profiles within different clusters. Furthermore, MMN indexes may serve as predictors of subsequent remission from the CHR state. These findings suggest that the auditory MMN response is a potential neurophysiological marker for distinct clinical subtypes of CHR. [ABSTRACT FROM AUTHOR]

This study aimed to elucidate the spatial and temporal patterns of brain neural activity that are associated with cold and hot sensations. Participants (n = 20) sat in a controlled room with their eyes closed and received local thermal stimuli to the right fingers using a Peltier apparatus. The thermal stimuli were repeated 40 times using a paired-thermal stimulus paradigm, comprising a 15 s-reference stimulus (32 °C), followed by 10 s-conditioned stimuli (24 °C and 40 °C, cold and hot conditions, respectively), for which 15-channel electroencephalography (EEG) signals were continuously monitored. To identify the patterns of brain neural activity, an independent component (IC) analysis was applied to the preprocessed EEG data. The equivalent current dipole locations were estimated, followed by clustering of the ICs with a dipole residual variance of <15 %. Subsequently, event-related spectral perturbations were analyzed in each identified cluster to calculate the power changes across specific frequency ranges. The right precentral gyrus, precuneus, medial frontal gyrus, middle frontal gyrus, superior frontal gyrus, cuneus, cingulate gyrus, left precentral gyrus, middle occipital gyrus, and cingulate gyrus were activated in both cold and hot conditions. In most activated regions, EEG power temporal changes were observed across the frequency ranges and were different between the two conditions. These results may suggest that cold and hot sensations are processed through different temporal brain neural activity patterns in overlapping brain regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

This study addressed the influences of acoustic context on evoked rhythmic activity in the human brain underlying discrimination of stationary and moving sound stimuli using the oddball paradigm. Changes in context were created by rearranging the roles of the standard and deviant stimuli in different series. Event-related spectral perturbations (ERSP) and intertrial phase coherence (ITC), calculated on the basis of frequency-time decomposition of EEG traces, were analyzed. The moving standard stimulus evoked greater coherence of α and θ oscillations than the stationary standard. Presentation of moving stimuli as deviants led to an additional increase in the coherence of the evoked oscillations. In the direct configuration, presentation of deviants produced synchronization of slow oscillations, while presentation in the reverse configuration produced desynchronization. The extent of phase coherence can presumptively be regarded as an objective indicator of the discrimination of moving and stationary sound signals. [ABSTRACT FROM AUTHOR]

Individuals at clinical high risk (CHR) for psychosis exhibit a reduced P300 oddball response, which indicates deficits in attention and working memory processes. Previous studies have mainly researched these responses in the temporal domain; hence, non-phase-locked or induced neural activities may have been ignored. Event-related potential (ERP) and time–frequency (TF) information, combined with clinical and cognitive profiles, may provide an insight into the pathophysiology and psychopathology of the CHR stage. The 104 CHR individuals who completed cognitive assessments and ERP tests were recruited and followed up between 2016 and 2018. Individuals with CHR were classified by three clinical subtypes demonstrated before, specifically 32 from Cluster-1 (characterized by extensive negative symptoms and cognitive deficits, at the highest risk for conversion to psychosis), 34 from Cluster-2 (characterized by thought and behavioral disorganization, with moderate cognitive impairment), and 38 from Cluster-3 (characterized by the mildest symptoms and cognitive deficits). Electroencephalograms were recorded during the auditory oddball paradigm. The P300 ERPs were analyzed in the temporal domain. The event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) were acquired by TF analysis. A reduced P300 response to target tones was noted in Cluster-1 relative to the other two clusters. Moreover, the P300 amplitude of Cluster-1 was associated with speed of processing (SoP) scores. Furthermore, the P300 amplitude of Cluster-3 was significantly correlated with verbal and visual learning scores. In the TF analysis, decreased delta ERSP and ITC were observed in Cluster-1; delta ITC was associated with SoP scores in Cluster-3. The results indicate relatively disrupted oddball responses in a certain CHR subtype and a close affinity between these electrophysiological indexes and attention, working memory, and declarative memory within different CHR clusters. These findings suggest that the auditory oddball response is a potential neurophysiological marker for distinct clinical subtypes of CHR. [ABSTRACT FROM AUTHOR]

Purpose: Perceptual decision-making is the act of choosing one option from a set of alternatives based on available sensory information. Regarding the serious role of this act in human personal and social lives, the neurophysiological analysis of the brain during this type of decision is of great interest. In this research, the underlying neural mechanism of these decisions is investigated using a perceptual decision-making Electroencephalogram (EEG) dataset with a perceptual discrimination task. Materials and Methods: An online available dataset containing the pre-processed EEG signals of 24 healthy participants during the perceptual decision-making task of Random Dot Kinematograms was used. After a secondary pre-processing stage, clean EEG signal was divided into 1.3-second segments and averaged for Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP) calculations. The task engagement index was also calculated and averaged among all participants. Results: According to the results, the amplitude of the N200 component in O1 and O2 channels was larger for correct choices than incorrect ones. Furthermore, in the O2 channel, it was observed that the average alpha power near 200 milli-seconds after stimulus onset was slightly higher in high and low confidence choices than medium confidence choices. The beta band power in the PO2 channel was also higher for correct choices rather than incorrect ones in this interval. Moreover, the results represented that the task engagement index was higher in medium confidence choices, especially in occipital and parieto-occipital channels. Conclusion: The larger N200 amplitude and the higher beta power for correct choices, and the lower alpha power for medium confidence choices may be due to more attention of the individuals to the stimuli. This phenomenon can be observed in the task engagement indices as well. This could be because the user expended more efforts in medium confidence to bring one of the choices to the decision threshold.

Objective . Time-frequency (T-F) analysis of electroencephalographic (EEG) is a common technique to characterise spectral changes in neural activity. This study explores the limitations of utilizing conventional spectral techniques in examining cyclic event-related cortical activities due to challenges, including high inter-trial variability. Approach . Introducing the cycle-frequency (C-F) analysis, we aim to enhance the evaluation of cycle-locked respiratory events. For synthetic EEG that mimicked cycle-locked pre-motor activity, C-F had more accurate frequency and time localization compared to conventional T-F analysis, even for a significantly reduced number of trials and a variability of breathing rhythm. Main results . Preliminary validations using real EEG data during both unloaded breathing and loaded breathing (that evokes pre-motor activity) suggest potential benefits of using the C-F method, particularly in normalizing time units to cyclic activity phases and refining baseline placement and duration. Significance . The proposed approach could provide new insights for the study of rhythmic neural activities, complementing T-F analysis., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

• An evaluation framework is proposed based on quantitative metrics to \ort the choice of the preferable EEG pre-processing strategy. • The effect of different pre-processing approaches is explored and quantified on an illustrative application. • The most critical steps result to be the segmentation of the data and the re-reference method, while the Independent Component Analysis algorithm has a small effect on the cleaning procedure. Electroencephalography (EEG) to study brain functions has become fundamental in many research settings across very different protocols. Indeed, a plethora of processing methods have been developed, for both data preparation (pre-processing) and analysis. While an effect of the pre-processing on the signal is admitted and accepted, there is an increasing effort to better understand to which extent such an influence may affect the analysis results, and which may be the best practices for the correct data pre-processing. Pre-processing procedures include different steps, and each of them may induce modifications affecting the study results. Thus, we analyze the effect of different methodologies at each step and propose quantitative parameters for the choice of the preferable strategy. We illustrate how method selection may affect the quality of EEG signal in an Action Observation and Motor Imagery protocol, using quantitative indices. We analyzed the effect of different strategies for early-stage data preparation; two independent component analysis (ICA) algorithms (SOBI and Extended Infomax) used for artifact removal; and four re-reference approaches (Common averaged reference-CAR, robust-CAR, reference electrode standardization technique – REST, and reference electrode standardization and interpolation technique – RESIT). The effects of the different pipelines were also evaluated through the computation of event-related spectral perturbation (ERSP) of the sensorimotor rhythm. Results showed that signal segmentation significantly affects the cleaning procedure, while comparable results are obtained across ICA approaches. Finally, similar topographical representations were obtained after the application of CAR, REST, and RESIT re-referencing approaches, where rCAR showed the most different ERSP topographical pattern. [ABSTRACT FROM AUTHOR]

Driver's cognitive workload has an important impact on driving safety. This paper carries out an on-road experiment to analyse the impact from three innovative aspects: significance analysis of electroencephalogram (EEG) under different cognitive workloads, distribution of EEG maps with different frequency signals and influence of different cognitive workloads on driving safety based on EEG. First, the EEG signals are processed and four frequencies of delta, theta, alpha and beta are obtained. Then, the time-frequency transform and power spectral density calculation are carried out by short-time Fourier to study the correlation of each frequency signal of different workload states, as well as the distribution pattern of the EEG topographic map. Finally, the time and space energy and phase changes in each cognitive task event are studied through event-related spectral perturbation and inter-trial coherence. Results show the difference between left and right brains, as well as the resource occupancy trends of the monitor, perception, visual and auditory channels in different driving conditions. Results also demonstrate that the increase in cognitive workloads will directly affect driving safety. Changes in cognitive workload have different effects on brain signals, and this paper can provide a theoretical basis for improving driving safety under different cognitive workloads. Mastering the EEG characteristics of signals can provide more targeted supervision and safety warnings for the driver., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liu, Qi, Hao, Lian and Luo.)