Recorded Brain Activity Research Articles

Investigating the hemiretinal asymmetry in emotion processing as a function of spatial frequency.

The subcortical visual pathway to the amygdala has long been considered a rapid and crude stream for processing emotionally salient information that is reliant on low spatial frequency (LSF) information. Recently, research has called this LSF dependency into question. To resolve this debate, we take advantage of an anatomical hemiretinal asymmetry, whereby the nasal hemiretina sends a higher proportion of information through the subcortical pathway than the temporal hemiretina. We recorded brain activity using electroencephalography (EEG) in human participants (N = 40) while they completed a monocular viewing paradigm. Pairs of faces (one fearful and one neutral, or both neutral) were projected simultaneously to the nasal and temporal hemiretina in three contrast-equated blocks; faces filtered to display only (i) LSF, (ii) high spatial frequency (HSF), or (iii) unfiltered information (broadband spatial frequency; BSF). BSF fearful faces were found to produce a greater naso-temporal asymmetry, with greater N170 amplitudes evoked by BSF faces in the nasal field, compared to HSF faces. Conversely, the naso-temporal asymmetry for LSF fearful faces did not differ between BSF and HSF. Collectively, these findings provide crucial evidence that the subcortical pathway carries combined spatial frequency visual signals, with a potential bias against HSF content.

Intravascular delivery of an ultraflexible neural electrode array for recordings of cortical spiking activity

Although intracranial neural electrodes have significantly contributed to both fundamental research and clinical treatment of neurological diseases, their implantation requires invasive surgery to open craniotomies, which can introduce brain damage and disrupt normal brain functions. Recent emergence of endovascular neural devices offers minimally invasive approaches for neural recording and stimulation. However, existing endovascular neural devices are unable to resolve single-unit activity in large animal models or human patients, impeding a broader application as neural interfaces in clinical practice. Here, we present the ultraflexible implantable neural electrode as an intravascular device (uFINE-I) for recording brain activity at single-unit resolution. We successfully implanted uFINE-Is into the sheep occipital lobe by penetrating through the confluence of sinuses and recorded both local field potentials (LFPs) and multi-channel single-unit spiking activity under spontaneous and visually evoked conditions. Imaging and histological analysis revealed minimal tissue damage and immune response. The uFINE-I provides a practical solution for achieving high-resolution neural recording with minimal invasiveness and can be readily transferred to clinical settings for future neural interface applications such as brain-machine interfaces (BMIs) and the treatment of neurological diseases.

IDENTIFICATION OF SCHIZOPHRENIA USING ALPHA POWER AND THETA PEAK FREQUENCY DURING COGNITIVE ACTIVITY

Schizophrenia (SCH) is a psychiatric disorder that causes mental illness and is very difficult to discriminate from the normal subject. As the current diagnostic procedure DSM-V is qualitative, accurate clinical detection is essential. Cognitive dysfunction is widespread in [Formula: see text]CH and it relates to brain activity. The aim of this research is to analyze EEG as a screening tool to identify SCH as it records brain activity. To stimulate cognitive activity, two different types of visual oddball paradigms are designed and are named P1 and P2. Although many researchers have analyzed the EEG of SCH, it remains unclear which recording condition is suitable. Therefore, EEG is recorded from 52 SCH subjects and 29 normal subjects during conventional recording conditions, namely rest, HV, PHV, Photic and cognitive activities (P1 and P2). The Welch power spectrum method is used to find the absolute power of the four EEG bands called [Formula: see text][Formula: see text], [Formula: see text][Formula: see text], [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text]. In addition to these, each band’s peak power [Formula: see text][Formula: see text], [Formula: see text][Formula: see text], [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text] and peak frequencies [Formula: see text][Formula: see text], [Formula: see text][Formula: see text], [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text] are also calculated from the power spectrum of the EEG signal. Out of these 12 features, [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text] are considered as the most significant features, because they are significant with [Formula: see text] < 0.001 in all recording conditions. The SVM classifier successfully predicts SCH with a maximum sensitivity of 98% and accuracy of 97% using [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text]. Therefore, it can be concluded that [Formula: see text][Formula: see text] and [Formula: see text][Formula: see text] can be used as features to use EEG as a screening tool for the identification of schizophrenia.

Huperzine A suppresses absence seizures in the genetic absence epilepsy rat from Strasbourg (GAERS) model of genetic generalized epilepsy with absence seizures.

We evaluated huperzine A treatment in the Genetic Absence Epilepsy Rat from Strasbourg (GAERS) model of genetic generalized epilepsy (GGE) with absence seizures. Adult male GAERS (N = 15) were implanted with EEG recording electrodes 10 days before receiving study drug. Each animal received the following six treatments as a single, intraperitoneal dose, 7 days apart (in random order): huperzine A (0.3, 1.0, or 3.0 mg/kg), two periods of vehicle (0.9% NaCl), or ethosuximide (100 mg/kg) as a positive control. Electroencephalograms (EEGs) were acquired for 24 h before and after each treatment and analyzed for seizure activity during the 90-min period immediately post-treatment, including 30-min intervals at 30, 60, and 90 min. Additional analyses evaluated seizure activity over the 24-h post-treatment period using 60-min intervals at 6, 12, and 24 h. The cumulative 24-h periods before and after each administered treatment were also compared. Two-way ANOVA showed a treatment difference [F(91,182) = 3.592, p < 0.0001] on the number of seizures over the first 90-min post-treatment (primary outcome); Tukey's post hoc analyses showed that, compared to vehicle, huperzine A (3.0 mg/kg) significantly reduced seizures in the 30-min (p = 0.02) and 60-min (p = 0.001) intervals, and ethosuximide significantly reduced seizures at all measured time intervals except the 1-h blocks at 12 and 24 h. Huperzine A 3.0 mg/kg and ethosuximide significantly reduced seizures during the cumulative 24-h post-treatment period relative to pretreatment baseline. While huperzine A 3.0 mg/kg did not differ significantly from ethosuximide at any time point, the study was not designed to evaluate non-inferiority. The only adverse event after huperzine A or ethosuximide was mild, dose-dependent sedation. Huperzine A potently suppressed absence-like seizures in GAERS, albeit with a shorter duration of action relative to ethosuximide, showing promise for clinical efficacy in GGE. This study looked at how huperzine A affects seizures in rats with similar abnormal brain activity as seen in humans with absence epilepsy. Rats received different treatments, placebo (i.e., saline solution), huperzine A, and ethosuximide. Ethosuximide is considered a gold standard treatment for absence epilepsy. We recorded brain activity to measure seizures before and after each treatment. We found that huperzine A (3.0 mg/kg) reduced seizures soon after treatment, like ethosuximide. Both treatments appeared safe, causing only mild sleepiness. The study shows that huperzine A could be a good new treatment for a type of absence epilepsy.

Neuro-cognitive effects of degraded visibility on illusory body ownership

Based on visuo-tactile stimulation, the rubber hand illusion induces a sense of ownership for a dummy hand. Manipulating the visibility of the dummy hand during the stimulation influences cognitive aspects of the illusion, suggesting that the related brain activity may be influenced too. To test this, we analyzed brain activity (fMRI), subjective ratings, and skin conductance from 45 neurotypical participants undergoing a modified rubber hand illusion protocol where we manipulated the visibility (high, medium, and low) of a virtual hand, not the brush (virtual hand illusion; VHI). To further investigate the impact of visibility manipulations on VHI-related secondary effects (i.e. vicarious somatosensation), we recorded brain activity and skin conductance during a vicarious pain protocol (observation of painful stimulations of the virtual hand) that occurred after the VHI procedure. Results showed that, during both the VHI and vicarious pain periods, the activity of distinct visual, somatosensory, and motor brain regions was modulated by (i) visibility manipulations, (ii) coherence between visual and tactile stimulation, and (iii) time of visuo-tactile stimulation. Accordingly, embodiment-related subjective ratings of the perceived illusion were specifically influenced by visibility manipulations. These findings suggest that visibility modifications can impact the neural and cognitive effects of illusory body ownership, in that when visibility decreases the illusion is perceived as weaker and the brain activity in visual, motor, and somatosensory regions is overall lower. We interpret this evidence as a sign of the weight of vision on embodiment processes, in that the cortical and subjective aspects of illusory body ownership are weakened by a degradation of visual input during the induction of the illusion.

How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study

EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, using the phase lag index (PLI) and the phase locking value (PLV) methods, we investigate how the performance of a connectivity-based EEG biometric system varies with respect to different time windows (using epochs of different lengths ranging from 0.5 s to 12 s with a step of 0.5 s) to understand if it is possible to define the optimal duration of the EEG signal required to extract those distinctive features. All the analyses were performed on two freely available EEG datasets, including 109 and 23 subjects, respectively. Overall, as expected, the results have shown a pronounced effect of the time window length on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase in the biometric performance as the time window increases. Furthermore, our initial findings strongly suggest that enlarging the window size beyond a specific maximum threshold fails to enhance the performance of biometric systems. In conclusions, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as an EEG fingerprint and that, in this context, it is essential to establish an adequate time window capable of capturing subject-specific features. Furthermore, we speculate that the poor performance obtained with short time windows mainly depends on the difficulty of correctly estimating the connectivity metrics from very small EEG epochs (shorter than 8 s).

Detection of Sleeping Pattern Using EEG Signals with Deep Learning

Background: Recorded brain activity analysis is a major tool used in modern medicine and sleep-based research. However, the volume and complexity of the data make manual investigation difficult. Furthermore, high inter-subject variability presents a challenge to deep learning techniques, especially in the detection of sleep-related EEG patterns and artifacts. Methods: Sleep Pattern Identifier (SPI), a hybrid classifier, was used in this study to address the aforementioned issues. SPI tests the efficacy of artifact detection while examining strategies and tactics used in real-world sleep research. A focus was on inter-subject variability, especially in data gathered from participants suffering from sleep disorders. For comparison and visual inspection of the data, formal statistical measures like accuracy, model loss, precision, recall, and ROC were employed. Results: With an accuracy of 94.85%, the suggested model outperformed current techniques and demonstrated higher accuracy. Further, the Sleep-EDF dataset was used in this investigation. Conclusion: The conclusion highlights the effectiveness of the SPI hybrid classifier in identifying EEG patterns, especially in situations where sleep research is conducted in the real world. Research methodologies based on sleep have made significant progress in handling artifacts and adapting to inter-subject variability.

Effects of acute and chronic ketamine administration on spontaneous and evoked brain activity

Schizophrenia is believed to be, at least in part, a dysfunction of the glutamatergic system. In line with anatomical evidence, suppressing N-methyl-D-aspartate (NMDA) neurotransmission leads to symptoms that are characteristic of schizophrenia. Rodent models of schizophrenia often involve the acute application of NMDA antagonists, which produce both behavioural and brain activity changes that closely resemble symptoms observed in schizophrenia. It is, however, important to note that the full spectrum of schizophrenia symptoms may not be manifested following the acute suppression of NMDA receptors. This has led to the proposal of a chronic model where NMDA receptors are suppressed for prolonged periods. Although the chronic model has shown promising results from a behavioural perspective and alterations in metabolic processes in the brain, its impact on brain oscillations remains largely unknown. The aim of this study is to examine the impact of acute and chronic NMDA neurotransmission suppression on brains’ oscillatory activity. To achieve this, chronic brain activity recordings in mice of both sexes were used to assess both spontaneous and evoked brain oscillations. The study demonstrates that an acute suppression of NMDA receptors alters brain oscillations across a wide frequency spectrum and diminishes the oscillatory potency in evoked responses, paralleling changes observed in schizophrenia. However, the chronic suppression of NMDA receptors did not have the expected cumulative effect on brain activity. This research highlights the robust yet similar impacts of acute and chronic NMDA receptor suppression on brain activity, contributing to the nuanced understanding of rodent models of schizophrenia.

Non-invasive electroencephalography in awake cats: Feasibility and application to sensory processing in chronic pain

BackgroundFeline osteoarthritis (OA) leads to chronic pain and somatosensory sensitisation. In humans, sensory exposure can modulate chronic pain. Recently, electroencephalography (EEG) revealed a specific brain signature to human OA. However, EEG pain characterisation or its modulation does not exist in OA cats, and all EEG were conducted in sedated cats, using intradermal electrodes, which could alter sensory (pain) perception. New methodCats (n=11) affected by OA were assessed using ten gold-plated surface electrodes. Sensory stimuli were presented in random orders: response to mechanical temporal summation, grapefruit scent and mono-chromatic wavelengths (500 nm-blue, 525 nm-green and 627 nm-red light). The recorded EEG was processed to identify event-related potentials (ERP) and to perform spectral analysis (z-score). ResultsThe procedure was well-tolerated. The ERPs were reported for both mechanical (F3, C3, Cz, P3, Pz) and olfactory stimuli (Cz, Pz). The main limitation was motion artifacts. Spectral analysis revealed a significant interaction between the power of EEG frequency bands and light wavelengths (p<0.001). All wavelengths considered, alpha band proportion was higher than that of delta and gamma bands (p<0.044), while the latter was lower than the beta band (p<0.016). Compared to green and red, exposure to blue light elicited distinct changes in EEG power over time (p<0.001). Comparison with existing methodThis is the first demonstration of EEG feasibility in conscious cats with surface electrodes recording brain activity while exposing them to sensory stimulations. ConclusionThe identification of ERPs and spectral patterns opens new avenues for investigating feline chronic pain and its potential modulation through sensory interventions.

Event-related potentials and behavioral correlates of emotional recognition memory in late pregnancy.

Research on cognitive and emotional functions during pregnancy challenges the prevalent perception of cognitive decline in pregnant women. This study investigates the behavioral and neural dynamics of cognitive-affective processing in third-trimester pregnant women, comparing them with non-pregnant controls. Using a 64-channel EEG-ERP system, we recorded brain activity as participants engaged in an emotional word recognition task. This task involved initially viewing a sequence of emotional and neutral words, followed by a recognition test where participants identified each word as 'new' or 'previously seen'. Contrary to widespread beliefs about diminished recognition ability during late pregnancy, our results revealed no significant differences in error rates between groups. However, pregnant participants demonstrated slower reaction times. In terms of neural responses, pregnant women exhibited increased amplitudes in the N1, P2, and N400 ERP components, suggesting that they may require additional brain resources compared with non-pregnant individuals to process perceptual information. A significant interaction was observed between pregnancy status and the emotional valence of stimuli. Pregnant women showed heightened N1 and N400 responses to negative words, indicating increased sensitivity to stimuli potentially representing threat. This enhanced response was not observed for positive or neutral words. Furthermore, there was an amplified N1 response to 'new' words, but not to 'old' words. These findings suggest that late pregnancy is characterized by heightened responsiveness to new and particularly negative stimuli, potentially leading to a more cautious behavioral approach. Heightened vigilance and sensitivity could offer evolutionary advantages, optimizing fetal development and enhancing maternal well-being.

Mouse Exploratory Behaviour in the Open Field with and without NAT-1 EEG Device: Effects of MK801 and Scopolamine.

One aspect of reproducibility in preclinical research that is frequently overlooked is the physical condition in which physiological, pharmacological, or behavioural recordings are conducted. In this study, the physical conditions of mice were altered through the attachments of wireless electrophysiological recording devices (Neural Activity Tracker-1, NAT-1). NAT-1 devices are miniaturised multichannel devices with onboard memory for direct high-resolution recording of brain activity for >48 h. Such devices may limit the mobility of animals and affect their behavioural performance due to the added weight (total weight of approximately 3.4 g). The mice were additionally treated with saline (control), N-methyl-D-aspartate (NMDA) receptor antagonist MK801 (0.85 mg/kg), or the muscarinic acetylcholine receptor blocker scopolamine (0.65 mg/kg) to allow exploration of the effect of NAT-1 attachments in pharmacologically treated mice. We found only minimal differences in behavioural outcomes with NAT-1 attachments in standard parameters of locomotor activity widely reported for the open field test between the drug treatments. Hypoactivity was globally observed as a consistent outcome in the MK801-treated mice and hyperactivity in scopolamine groups regardless of NAT-1 attachments. These data collectively confirm the reproducibility for combined behavioural, pharmacological, and physiological endpoints even in the presence of lightweight wireless data loggers. The NAT-1 therefore constitutes a pertinent tool for investigating brain activity in, e.g., drug discovery and models of neuropsychiatric and/or neurodegenerative diseases with minimal effects on pharmacological and behavioural outcomes.

A shared model-based linguistic space for transmitting our thoughts from brain to brain in natural conversations

Effective communication hinges on a mutual understanding of word meaning in different contexts. We recorded brain activity using electrocorticography during spontaneous, face-to-face conversations in five pairs of epilepsy patients. We developed a model-based coupling framework that aligns brain activity in both speaker and listener to a shared embedding space from a large language model (LLM). The context-sensitive LLM embeddings allow us to track the exchange of linguistic information, word by word, from one brain to another in natural conversations. Linguistic content emerges in the speaker's brain before word articulation and rapidly re-emerges in the listener's brain after word articulation. The contextual embeddings better capture word-by-word neural alignment between speaker and listener than syntactic and articulatory models. Our findings indicate that the contextual embeddings learned by LLMs can serve as an explicit numerical model of the shared, context-rich meaning space humans use to communicate their thoughts to one another.

SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants

Researchers investigating the neural mechanisms underlying speech perception often employ electroencephalography (EEG) to record brain activity while participants listen to spoken language. The high temporal resolution of EEG enables the study of neural responses to fast and dynamic speech signals. Previous studies have successfully extracted speech characteristics from EEG data and, conversely, predicted EEG activity from speech features. Machine learning techniques are generally employed to construct encoding and decoding models, which necessitate a substantial quantity of data. We present SparrKULee, a Speech-evoked Auditory Repository of EEG data, measured at KU Leuven, comprising 64-channel EEG recordings from 85 young individuals with normal hearing, each of whom listened to 90–150 min of natural speech. This dataset is more extensive than any currently available dataset in terms of both the number of participants and the quantity of data per participant. It is suitable for training larger machine learning models. We evaluate the dataset using linear and state-of-the-art non-linear models in a speech encoding/decoding and match/mismatch paradigm, providing benchmark scores for future research.

Dynamic network analysis of electrophysiological task data

Abstract An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data, this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. While this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM, and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.

Early parafoveal semantic integration in natural reading.

Humans can read and comprehend text rapidly, implying that readers might process multiple words per fixation. However, the extent to which parafoveal words are previewed and integrated into the evolving sentence context remains disputed. We investigated parafoveal processing during natural reading by recording brain activity and eye movements using MEG and an eye tracker while participants silently read one-line sentences. The sentences contained an unpredictable target word that was either congruent or incongruent with the sentence context. To measure parafoveal processing, we flickered the target words at 60 Hz and measured the resulting brain responses (i.e. Rapid Invisible Frequency Tagging, RIFT) during fixations on the pre-target words. Our results revealed a significantly weaker tagging response for target words that were incongruent with the previous context compared to congruent ones, even within 100ms of fixating the word immediately preceding the target. This reduction in the RIFT response was also found to be predictive of individual reading speed. We conclude that semantic information is not only extracted from the parafovea but can also be integrated with the previous context before the word is fixated. This early and extensive parafoveal processing supports the rapid word processing required for natural reading. Our study suggests that theoretical frameworks of natural reading should incorporate the concept of deep parafoveal processing.

Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA.

To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp. A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment. Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects. This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.

Deep learning based decoding of single local field potential events

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

On the speech envelope in the cortical tracking of speech

The synchronization between the speech envelope and neural activity in auditory regions, referred to as cortical tracking of speech (CTS), plays a key role in speech processing. The method selected for extracting the envelope is a crucial step in CTS measurement, and the absence of a consensus on best practices among the various methods can influence analysis outcomes and interpretation. Here, we systematically compare five standard envelope extraction methods the absolute value of Hilbert transform (absHilbert), gammatone filterbanks, heuristic approach, Bark scale, and vocalic energy), analyzing their impact on the CTS. We present performance metrics for each method based on the recording of brain activity from participants listening to speech in clear and noisy conditions, utilizing intracranial EEG, MEG and EEG data. As expected, we observed significant CTS in temporal brain regions below 10 Hz across all datasets, regardless of the extraction methods. In general, the gammatone filterbanks approach consistently demonstrated superior performance compared to other methods. Results from our study can guide scientists in the field to make informed decisions about the optimal analysis to extract the CTS, contributing to advancing the understanding of the neuronal mechanisms implicated in CTS.

Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface.

Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.

Maximizing the Effectiveness of Anti-Smoking Campaigns With Targeted Framing Strategies: Evidence From Behavioral and Neurological Studies

Background The global prevalence of smoking remains a significant public health concern, necessitating the development of effective anti-smoking campaigns to promote smoking cessation and discourage smoking initiation. Understanding the factors that influence individual responses to anti-smoking messages is critical for tailoring campaigns to different populations. Focus of the Article This study substantially exhibits the concepts of audience segmentation and targeting, informed by theory and formative research, and monitoring and evaluation research in the context of anti-smoking campaigns. Research Question The article aims to answer how smoking behavior and message framing influence individuals’ counterarguing responses to anti-smoking advertisements and the implications for ad effectiveness. Approach The research investigates the role of smoking behavior (smokers vs. non-smokers) and message framing (positive vs. negative) in shaping individuals’ counterarguing responses to anti-smoking advertisements, using two studies involving behavioral and neuroscientific methods. Methods Study 1 employed an online video task with 1920 participants, examining the effects of smoking behavior and message framing on ad effectiveness. Study 2 used electroencephalography (EEG) to record brain activity from 31 participants while they viewed anti-smoking video advertisements, focusing on the neural correlates of counterarguing and their effect on anti-smoking ad effectiveness. Results Study 1 found that smokers report lower ad effectiveness compared to non-smokers, and observed an interaction between smoking behavior and message framing. Study 2 found that smoking behavior significantly influenced brain activity in the dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (VLPFC), and anterior cingulate cortex (ACC) regions in response to anti-smoking ads. Additionally, a significant interaction was observed between smoking behavior and ad framing on brain activity in all three regions. Recommendations for Research or Practice The findings suggest that anti-smoking campaigns should be tailored to the specific needs and attitudes of smokers and non-smokers, with separate campaigns for each group. Future research could explore the role of other individual differences in shaping counterarguing responses, the long-term effects of exposure to different types of anti-smoking messages on smoking behavior, and using more advanced neuroimaging techniques to investigate the neural processes that underlie counterarguing responses to anti-smoking messages for better flow.